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Free Download [OFFER] Upskill Yourself By Learning Cad And Tinkercad For Students Last updated 1/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English (US) | Size: 2.81 GB | Duration: 3h 40m Learn tinkercad for student projects What you'll learn Introduction to CAD What is tinkercad How you can start using tinkercad Who can use tinkercad and its benefits Tinkercad for student projects Requirements Anyone who has basic computer knowledge Beginners who want to learn CAD / Computer aided design Anyone interested in 3D printing Best for students Description In this course you will learn basics of tinkercadHow to signup , login and start using tinkercad. We just need an email to signup and login. Then we are ready to go.Tinkercad is a free, web-based 3D design and modelling tool that allows users to create, modify, and share 3D models. It is aimed at beginners, hobbyists, and students, and offers an intuitive drag-and-drop interface and alarge library of pre-made shapes, making it easy to get started with 3D design. Tinkercad can be used for a variety of purposes, such as creating prototypes for engineering and product design, creating architectural models, and designing jewellery and other objects.Tinkercad is a great tool to learn CAD and especially used in schools colleges and for study purposed and also for 3d printingThis comes from Autodesk a company which produces professional CAD tools and is a market leader in this domainThe tool is very easy to learn and therefore i have made this tutorial for anyone who wants to get into CAD You will learn how to design basic shapes , how to use the different tool functionalities I will show how you can model different shapes like simple buildings , simple aeroplanes Will also show how to use the electronic circuits section in tinkercad where it is possible to design circuits and run simulations. No hardware is required but use can simply learn all circuit elements and run simulation. In this course i will provide you will many models which you can use for your college or student projects Who this course is for Anyone who has interest in models or CAD design,Students and professionals,Students seeking projects for learning CAD or school/college project Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Learning and Praticing OpenSCAD on 3D Modeling Published 4/2024 Created by Xiaoqi Zhao MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 6 Lectures ( 1h 34m ) | Size: 1.2 GB Learn to use OpenSCAD to create solid 3D CAD objects through hands-on tutorial What you'll learn: Get to know and familiar OpenSCAD software Understand and grasp the key programming approach for 3D modeling Learn by practicing of building 3D model via programming/coding Able to build variable 2D/3D shapes through following the hands-on demo Well prepared for further 3D modeling and 3D printing field Requirements: Basic programming knowledge, no specific language required Description: OpenSCAD is open source software for creating solid 2D/3D CAD objects., it's free and available for Linux/UNIX, MS Widnows and (Mac OSX).OpenSCAD is software for creating solid 3D CAD models. It is free software and available for Linux/UNIX, Windows and (Mac OSX). Unlike most free software for creating 3D models (such as Blender) it does not focus on the artistic aspects of 3D modelling but instead on the CAD aspects. Thus it might be the application you are looking for when you are planning to create 3D models of machine parts but pretty sure is not what you are looking for when you are more interested in creating computer-animated movies.OpenSCAD is not an interactive modeller. Instead it is something like a 3D-compiler that reads in a script file that describes the object and renders the 3D model from this script file. This gives you (the designer) full control over the modelling process and enables you to easily change any step in the modelling process or make designs that are defined by configurable parameters.OpenSCAD provides two main modelling techniques: First there is constructive solid geometry (aka CSG) and second there is extrusion of 2D outlines. Autocad DXF files can be used as the data exchange format for such 2D outlines. In addition to 2D paths for extrusion it is also possible to read design parameters from DXF files. Besides DXF files OpenSCAD can read and create 3D models in the STL and OFF file formats.This course aims to provide you detail guides and demos base on formal OpenSCAD tutorial, shows every single detail of coding with thorough explanation.After the course, you'll be able to use OpenSCAD to design and build creative 3D CAD objects, and be able to enroll my next level course -- "Mastering OpenSCAD in 10 Projects". Enjoy! Who this course is for: Anyone interests on 3D printing Anyone interests of building hands-on 2D / 3D shapes Anyone interests on learn new tool for building 3D CAD models via coding Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

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Free Download [OFFER] Mastering Machine Learning Algorithms using Python Published 4/2024 Created by Manas Dasgupta MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 108 Lectures ( 28h 34m ) | Size: 11.4 GB Build and optimize ML Models from a range of Supervised, Unsupervised, Regression and Classification Algorithms What you'll learn: Machine Learning Core Concepts in Detail Understand use-case scenarios for applying Machine Learning Detailed coverage of Python for Data Science and Machine Learning Regression Algorithm - Linear Regression Classification Problems and Classification Algorithms Unsupervised Learning using K-Means Clustering Exploratory Data Analysis Techniques Dimensionality Reduction Techniques (PCA) Feature Engineering Techniques Model Optimization using Hyperparameter Tuning Model Optimization using Grid-Search Cross Validation Introduction to Deep Neural Networks Requirements: Some exposure to Programming Languages will be useful Description: Are you aspiring to become a Machine Learning Engineer or Data Scientist? if yes, then this course is for you. In this course, you will learn about core concepts of Machine Learning, use cases, role of Data, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to build Classification Models using a range of Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python. Most of this course is hands-on, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques.This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations. There is also an introductory lesson included on Deep Neural Networks with a worked out example on Image Classification using TensorFlow and Keras. Course Sections:Introduction to Machine LearningTypes of Machine Learning AlgorithmsUse cases of Machine LearningRole of Data in Machine LearningUnderstanding the process of Training or LearningUnderstanding Validation and TestingIntroduction to PythonSetting up your ML Development EnvironmentPython internal Data StructuresPython Language ElementsPandas Data Structure - Series and DataFramesExploratory Data Analysis - EDALearning Linear Regression Model using the House Price Prediction case studyLearning Logistic Model using the Credit Card Fraud Detection case studyEvaluating your model performanceFine Tuning your modelHyperparameter TuningCross ValidationLearning SVM through an Image Classification projectUnderstanding Decision TreesUnderstanding Ensemble Techniques using Random ForestDimensionality Reduction using PCAK-Means Clustering with Customer Segmentation ProjectIntroduction to Deep Learning Who this course is for: Begginer to Advanced Machine Learning Engineers Begginer to Advanced Data Scientists Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Mastering Numpy For Machine Learning (2024) Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.63 GB | Duration: 2h 35m Efficient Data Manipulation and Array Operations for Seamless Machine Learning Implementation What you'll learn Introduction to NumPy: Covering basics, array creation, indexing, and slicing, providing a solid foundation for working with arrays efficiently. Array Operations: Exploring arithmetic operations, aggregation functions like mean, sum, max, and min, and understanding element-wise operations for comprehensi Array Manipulation: Delving into reshaping, stacking, splitting, and transposing arrays to understand and modify data structures effectively. Indexing and Slicing: Mastering advanced techniques such as boolean indexing, fancy indexing, and conditional selection to extract and manipulate data subsets a Random Number Generation: Exploring random sampling methods and probability distributions for simulations and statistical analysis tasks. Performance Optimization: Covering vectorization, broadcasting, and other optimization techniques to write efficient, high-performance code for numerical comput Requirements Basics of Python Programming and Mathematics Description Mastering NumPy for Machine Learning: Essential Tools and Techniques" offers a comprehensive exploration of NumPy, the fundamental library for numerical computing in Python, tailored specifically for machine learning practitioners. This course equips participants with the essential skills and techniques required to efficiently manipulate data and perform array operations crucial for seamless implementation of machine learning algorithms.Participants will delve into the core concepts of NumPy, including array creation, indexing, slicing, and manipulation, providing a solid foundation for handling large datasets effectively. Through hands-on exercises and real-world examples, students will learn to leverage NumPy's array operations for arithmetic computations, aggregation functions, and element-wise operations, facilitating data preprocessing and feature engineering tasks.Moreover, the course covers advanced topics such as universal functions (ufuncs), linear algebra operations, random number generation, and file input/output, empowering participants to tackle complex machine learning challenges with confidence. Through optimization techniques like vectorization and broadcasting, students will discover strategies to enhance computational efficiency and streamline their machine learning workflows.By the end of the course, participants will emerge equipped with the expertise to leverage NumPy effectively in their machine learning projects, enabling them to manipulate data efficiently, perform numerical computations with ease, and accelerate the development and deployment of machine learning models. Overview Section 1: Introduction Lecture 1 1. Numpy_Introduction Lecture 2 2. Numpy_Array_Creation Lecture 3 3. Numpy_Arange_Reshape Lecture 4 4. Numpy_Array_Conversion Lecture 5 5. Accessing Array Values Lecture 6 6. Numpy_Operations Lecture 7 7. Fancy Indexing and Sorting Arrays Lecture 8 8. Array Products and Concatenation Lecture 9 9. Broadcasting Data Scientists: Seeking proficiency in handling and analyzing large datasets efficiently for tasks like data cleaning, exploration, and modeling.,Machine Learning Engineers/Practitioners: Wanting to master NumPy for implementing machine learning algorithms, performing data preprocessing, and working with multidimensional arrays.,Researchers: In fields such as physics, biology, engineering, etc., needing to perform complex numerical computations, simulations, and data analysis.,Statisticians: Requiring tools for statistical analysis, hypothesis testing, and exploring datasets with extensive numerical functionalities.,Software Developers: Interested in incorporating numerical computing capabilities into their applications, particularly those involving scientific or data-driven functionalities.,Students: Pursuing studies in computer science, data science, engineering, or related fields, wanting to gain foundational skills in numerical computing with Python.,Professionals in Finance and Economics: Looking to leverage NumPy for financial modeling, risk analysis, and portfolio optimization tasks.,Anyone with an Interest in Numerical Computing: Regardless of professional background, who wants to enhance their skills in numerical computing and data manipulation using Python's NumPy library. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Organizational Learning and Development (2024) Released 4/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 5m | Size: 236 MB If you want to attract and retain top talent, you need to provide results-driven opportunities for your employees to learn and grow. Learning and development (L&D) programs have become a critical talent management tool for many organizations, helping leaders, managers, and recruiters build their talent pipelines by curating the conditions for learning success. In this course, industry leader and skills strategist Gina Jeneroux shows you how to take a more disciplined view of learning measurement at your organization to meet the learning needs of today's ever-changing workplace. Learn how to manage the business of learning by adopting the perspective of a strategic enabler, upskilling your employees for today and tomorrow, following best practices for effective learning, and developing your technology and data ecosystem more deliberately using the power of the SkillsEngine. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Machine Learning Project Guidelines Published 4/2024 Created by Balasubramanian Chandran MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 65 Lectures ( 13h 29m ) | Size: 6.66 GB A complete guide for developing ML projects with a well-defined methodology and best practices. What you'll learn: A deeper understanding of the 11 stages involved in developing and implementing ML projects Best practices to be followed while doing ML projects Building a template that you can use for your future ML projects Guidelines to Select Evaluation Metrics Guidelines to choose ML algorithms to solve specific problem(s) How you can visually compare the performances of ML models and select the best-performing model? What is data leakage and how to detect, prevent, and minimize it? Importance of converting business problems into analytical problems before building ML models How to understand datasets using Exploratory Data Analysis using various tools? Detailed approach to Data preprocessing How do various Regression and Classification algorithms (Linear, Non-linear, and Ensembles) and Clustering algorithms (K-Means and RFM Analysis) work? How to use various ML algorithms such as Linear Regression, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbors, and Support Vector Machines? How to use Decision Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting, K-Means? and so on How to apply ML algorithms in Python using Scikit-learn, XGBoost, and other ML libraries? How to perform Error Analysis and Troubleshoot Prediction Errors? How to tune Hyperparameters to improve Model Performances? How to build appealing visualization using Matplotlib, Seaborn, and Plotly? And, much more Requirements: Must have • Fundamentals of computer science and programming • High school-level basic mathematics Good to have • Basic Python programming • Basics of Linear Algebra • Basics of Statistics • Basics of Probability Theory • Basics of Object-Oriented Programming (OOPs) Description: This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to work with machine learning algorithms and models.This course is designed based on a whitepaper and the book "Machine Learning Project Guidelines" written by the author of this course.When building a high-performing ML model, it's not just about how many algorithms you know; instead, it's about how well you use what you already know.You will also learn that: There is NO single best algorithm that would work well for all predictive modeling problems And, the factors that determine which algorithm to choose for what type of problem(s) Even simple algorithms may outperform complex algorithms if you know how to handle model errors and refine the models through hyperparameter tuningThroughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 13 sections:IntroductionBusiness UnderstandingData UnderstandingResearchData PreprocessingModel DevelopmentModel TrainingModel RefinementModel EvaluationFinal Model SelectionModel Validation & Model DeploymentML Projects Hands-onML Project Template BuildingML Project 1 (Classification)ML Project 2 (Regression)ML Project 3 (Classification)ML Project 4 (Clustering - KMeans)ML Project 5 (Clustering - RFM Analysis)13. Congratulatory and Closing NoteThis course includes 48 lectures, 17 hands-on sessions, and 29 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure. Who this course is for: Beginners with little programming experience and basic mathematics Experienced programmers who want to pursue a career in ML/ Data Science/ AI People who have already taken other Machine Learning courses who want to strengthen their skills further and use a well-defined methodology in ML projects with best practices using a standardized project template Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] CBTNuggets - Introduction to Machine Learning Released 3/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 420 Lessons ( 48h ) | Size: 29 GB This entry-level training in machine learning and artificial intelligence prepares learners to convert vast datasets into not only meaningful information but also actionable insights, predictions, and forward-looking trends. The impact of machine learning on today's technological landscape is simply immeasurable. This course serves as an introduction to the groundbreaking power of machine learning, and aims to illuminate the exciting possibilities of solving real-world problems with machine learning. It's up to you to harness these insights and skills to solve specific problems in your organization or professional work. Fortunately, this course goes beyond the concepts of machine learning by offering hands-on opportunities to build models with scikit-learn, PyTorch, TensorFlow, and even a crash course in LLM development with OpenAI, LangChain, and HuggingFace. Once you complete this Introduction to Machine Learning training, you'll be adept at employing algorithms to uncover hidden insights, leverage statistical analysis, and generate data-driven predictive outcomes - all by using machine learning. For leaders of IT teams, this machine learning course offers an amazing transformative value: ideal for new junior data scientists transitioning into machine learning, integrating personalized training sessions, or simply a comprehensive reference for data science, machine learning, and artificial intelligence (AI) concepts and best practices. Introduction to Machine Learning: What You Need to Know This machine learning training features videos that cover essential data science, machine learning, and AI topics including Exploring machine learning fundamentals and the latest best practices Making sense of algorithms such as gradient descent and backpropagation Implementing classification and regression models to uncover patterns in data Diving into the perceptron and neural networks with powerful AI modeling concepts Hands-on introduction to PyTorch, and TensorFlow model building Distilling Large Language Models (LLMs) with ChatGPT, LangChain, and HuggingFace Who Should Take Introductory Machine Learning Training? The introduction to machine learning training is presented as associate-level data science training, which means it was designed for junior data scientists and aspiring machine learning engineers. This machine learning skills course offers significant value to both emerging IT professionals with at least a year of experience and seasoned data scientists looking to validate their data science skills in an ever advancing field. New or aspiring junior data scientists. If you're a brand new data scientist, you don't want to start your first job without a familiarity with machine learning. Whether you're looking for your first job or you're still a student, take this introduction to machine learning and bring all the capabilities and opportunities of machine learning with you to your first job from day one. Experienced junior data scientists. If you've navigated working as a data scientist for several years without delving into machine learning, congrats on your achievement! This introductory machine learning course will further broaden your wheelhouse of skills, empowering you to work with precision, efficiency, and alignment to the latest best practices and tools. Not to mention staying at the forefront of data science but also opening up profitable opportunities and advancement in your career. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Learning AutoCAD 2025 Released 3/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 2h 22m | Size: 334 MB In this course, Autodesk Certified Instructor Shaun Bryant shows you the basics of the AutoCAD user interface and leads you step-by-step through learning how to draw the kind of precise, measured 2D drawings that form the basis of design communication the world over. Explore the AutoCAD interface and learn how to draw simple geometry. Find out how to annotate simple designs and communicate your design intent. Plus, get practical advice on applying AutoCAD basics to your own drawings. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Godot Genesis - Learning Through 6 classic Game Genres Published 2/2024 Created by Omar Zaki MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 77 Lectures ( 18h 47m ) | Size: 7.81 GB Gain Hands on practice through 11 completely different Projects What you'll learn: Master Game Development: Learn core principles and processes with a 3-step approach. Skill in Multiple Genres: Gain expertise in 6 game genres through hands-on practice. Complete 2D and 3D Projects: Finish 11 projects for practical experience in game creation. Overcome Development Challenges: Learn solutions to common game design obstacles. Independently Create Games: Gain the skills to design any game in any genre with Godot 4. Proficiency in Godot 4: Master using Godot 4 for both 2D and 3D game development. Requirements: This course will start for complete Novice's Thus no programming experience is needed. You Will need a decent Laptop or PC to run Godot. You can find their requirements on their site Description: Learning to create your own games can be very difficult and daunting. Most Courses & Teachers will simply take you through the process of THEM creating THEIR own game. But as you might know and as I know. This doesn't help me much. So, I've developed a way for you to create your own game and implemented it into this course! The basic idea is that will we learn through a 3-step process, 1. Learning the Concept, 2. Examples, 3. Exercises.This process allows YOU as the learner to completely understand and digest what we're actually doing and apply it yourself at a later time. Thus, we will go through this 3-Step process throughout the entire course to learn in a more effective way. To add onto that, we will also be going through 6 DIFFERENT genres!1. Platformers2. RPG's3. Shooters4. Tower Defense Games5. Turn-Based Games6. RTS'sWe will be completing 11 different projects/assignments together! 2 assignments from each Section/Genre. One In 2D and one in 3DProviding you with possible solutions to each project!By the end of this long course, if followed properly, you have my guarantee that you will be able to create ANY course of your own in ANY genre within Godot 4! Who this course is for: Suitable for Beginner to Intermediate Aspiring Game Developers Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Unlocking Insights Machine Learning in Econometrics Published 3/2024 Created by Grant Gannaway MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 54 Lectures ( 3h 24m ) | Size: 906 MB This course provides a high-level overview of the most important concepts in machine learning and econometrics. What you'll learn: Understand Econometric Foundations: Grasp core concepts, models, and techniques in econometrics for data-driven economic analysis. Apply Statistical Methods: Apply regression analysis, time series modeling, and hypothesis testing to real-world economic datasets. Integrate Machine Learning: Explore the fusion of ML techniques with econometrics for enhanced predictive modeling and policy insights. Handle Economic Data: Learn data preprocessing, normalization, and handling outliers in economic datasets. Predict Economic Trends: Build predictive models to forecast economic trends, aiding informed decision-making. Ethical Data Usage: Understand ethical considerations and responsible use of data in economic analyses. Future Trends Awareness: Stay updated on emerging trends, like AI-driven economics, shaping the future of the field. Requirements: The only prerequisites are curiosity and enthusiasm: A genuine interest in exploring the intersection of machine learning and econometrics for advancing economic insights. If you are able to run simple commands in python, it will be useful to understand the code examples in the course. Description: One of the most valuable skills for the future will be unlocking insights from data. Often, practitioners are experts in machine learning or econometrics, but not both. However, having at least a basic understanding of the concepts in both econometrics and machine learning will allow practitioners to unlock data insights to the fullest extent. This course is designed to be a first step in bridging the gap between the two fields. Those fluent in machine learning will benefit from examples of econometric thinking, and econometricians will benefit from discussions of machine learning concepts. In this course, I will discuss the key concepts at the intersection of machine learning and econometrics. I will start by comparing and contrasting the two fields, then I will move into basic data handling skills, then I will discuss keys to exploratory data analysis, and end with a segment on using regression in a machine learning context to make economic predictions. I will give Python code examples for some concepts, and work through a basic case study predicting the economic growth of different countries around the world. This is an introductory course that provides overviews and summaries of the most important ideas, in future courses I will dig deeper into individual concepts - feel free to message me with suggestions! Who this course is for: Economics Students: Ideal for undergraduate and graduate economics students aiming to enhance analytical skills and apply machine learning in economic research. Data Analysts: Suited for data analysts seeking to specialize in economic analysis, combining econometrics and machine learning techniques. Economists: Valuable for practicing economists aiming to modernize their skills, leverage data-driven approaches, and make informed policy recommendations. Research Professionals: Beneficial for researchers and professionals in economics, finance, and related fields who want to integrate advanced data analysis techniques. Policy Makers: Useful for policymakers interested in data-driven insights to design effective economic policies and understand their potential impact. Academic Researchers: Appropriate for researchers and academics exploring interdisciplinary studies at the intersection of economics and machine learning. Data Science Enthusiasts: Suitable for individuals with a strong interest in data science and its applications in economic analysis. Prerequisite-Knowledge Seekers: Designed for learners looking to bridge their econometrics and machine learning expertise. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Learning Visual Studio Code (2024) Released 3/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 2h 10m | Size: 295 MB Visual Studio Code is a lightweight source code editor that boasts the advanced features of an IDE and runs on Windows, Linux, and macOS. It provides easy access to many extensions for additional features and support for languages like C#, C++, Python, Java, and much more. It also comes with support for embedded Git control, debugging, intelligent code completion, code refactoring, and more. In this course, learn the basics of Visual Studio Code with industry expert Reynald Adolphe. Reynald helps you get started with Visual Studio Code by showing you how to use the command line, install extensions, write and edit code, integrate with source control, and configure and use the terminal. By the end of this course, you'll also be equipped with the skills you need to know to automate everyday tasks and use the built-in Node.js debugger. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Coursera - IBM Machine Learning Professional Certificate Last updated 1/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 277 Lessons ( 31h 3m ) | Size: 3 GB Prepare for a career in machine learning. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months. What you'll learn Master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles Learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python Develop working knowledge of KNN, PCA, and non-negative matrix collaborative filtering Predict course ratings by training a neural network and constructing regression and classification models Skills you'll gain Ensemble Learning Linear Regression Machine Learning Feature Engineering Ridge Regression Statistical Hypothesis Testing Machine Learning (ML) Algorithms Supervised Learning Regression Analysis Exploratory Data Analysis Artificial Intelligence (AI) Decision Tree Prepare for a career in the field of machine learning. In this program, you'll learn in-demand skills like AI and Machine Learning to get job-ready in less than 3 months. Machine Learning is the use and development of computer systems that are able to learn and adapt by using algorithms and statistical models to analyze and draw inferences from patterns in data. Machine Learning is a branch of Artificial Intelligence (AI) where computers are taught to imitate human intelligence in that they solve complex tasks. Roles available to those proficient in Machine Learning include machine learning engineer, NLP scientist, and data engineer. This program consists of courses that provide you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. Topics covered include Supervised and Unsupervised learning, Regression, Classification, Clustering, Deep learning and Reinforcement learning. You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries, and you will apply what you have learned in various courses by completing a final capstone project. Upon completion, you'll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You'll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support. Applied Learning Project This Professional Certificate has a strong emphasis on developing the real-world skills that help you advance a career in Machine Learning and Deep Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments, and projects that will provide you with practical skills to use on Machine Learning jobs. These skills include Tools: Jupyter Notebooks and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow. Algorithms: Supervised and Unsupervised learning, Regression, Classification, Clustering, Linear Regression, Ridge Regression, Machine Learning (ML) Algorithms, Decision Tree, Ensemble Learning, Survival Analysis, K-means clustering, DBSCAN, Dimensionality Reduction Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Deep Learning From Scratch In Python Published 3/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.44 GB | Duration: 5h 16m Understand Convolutional Neural Networks and Implement your Object-Detection Framework From Scratch What you'll learn Understand how Deep Neural Networks work, practically and mathematically Understand Forward- and Backpropagation processes, mathematically and practically Design and implement a Deep Neural Network for multi-class classification Understand and implement the building blocks of Convolutional Neural Networks Understand and Implement cutting-edge Optimization, Regularization and Initialization techniques Train and validate a Convolutional Model on widely used datasets like MNIST and CIFAR-10 Understand and implement Transfer Learning Use a Convolutional Model to create a Real-Time, Multi-Object Detection System Requirements No prior knowledge is required Description This course is for anyone willing to really understand how Convolutional Neural Networks (CNNs) work. Every component of CNNs is first presented and explained mathematically, and the implemented in Python.Interactive programming exercises, executable within the course webpage, allow to gradually build a complete Object-Detection Framework based on an optimized Convolutional Neural Network model. No prior knowledge is required: the dedicated sections about Python Programming Basics and Calculus for Deep Learning provide the necessary knowledge to follow the course and implement Convolutional Neural Networks.In this course, students will be introduced to one of the latest and most successful algorithms for real-time multiple object detection. Throughout the course, they will gain a comprehensive understanding of the Backpropagation process, both from a mathematical and programming perspective, allowing them to build a strong foundation in this essential aspect of neural network training.By the course's conclusion, students will have hands-on experience implementing a sophisticated convolutional neural network framework. This framework will incorporate cutting-edge optimization and regularization techniques, enabling them to tackle complex real-world object detection tasks effectively and achieve impressive performance results. This practical knowledge will empower students to advance their capabilities in the exciting field of Computer Vision and Deep Learning. Overview Section 1: Neural Networks Basics Lecture 1 Introduction Lecture 2 Intuition about Fully-Connected Networks Lecture 3 Gradient Descent Algorithms Lecture 4 Training, Validation and Testing Section 2: Python Programming Basics Lecture 5 Python for CNNs Lecture 6 Working with Lists and Tuples Lecture 7 Working with NumPy Arrays Lecture 8 Object-Oriented Programming Section 3: Calculus for Deep Learning Lecture 9 The Derivative of a Function Lecture 10 The Product, Quotient and Power Rules Lecture 11 Derivatives by the Chain Rule Section 4: Cost Functions and Backpropagation Lecture 12 Backpropagation in Fully-Connected Networks Lecture 13 The Softmax Activation Function Lecture 14 The Cross-Entropy Cost Function Lecture 15 Backpropagation in the Output Layer Section 5: Building Blocks of Convolutional Neural Networks (CNNs) Lecture 16 Introduction to Convolutional Networks Lecture 17 Convolutions: Theory Lecture 18 Convolutions: Implementing an Edge Detector Lecture 19 Downsampling through Max Pooling Section 6: Backpropagation in Convolutional Neural Networks Lecture 20 Backpropagation in Convolutional Layers Lecture 21 Backpropagation in Pooling Layers Section 7: Integration of a Convolutional Model Lecture 22 Defining a Convolutional Model Lecture 23 The MNIST Dataset Lecture 24 Filter Visualization Section 8: Transfer Learning Lecture 25 What is Transfer Learning Section 9: Insights into Optimization and Regularization Lecture 26 Fully-Convolutional Implementation Lecture 27 The Vanishing Gradient and Dying ReLU Problems Lecture 28 Parameters Initialization Lecture 29 Learning Rate Decay Lecture 30 The Adam Optimizer Lecture 31 Testing the Optimized Model on MNIST Lecture 32 Testing the Optimized Model on CIFAR-10 Section 10: Multiple Object Detection in Real-Time Lecture 33 The YOLO Algorithm Lecture 34 Testing the YOLO Algorithm Everyone interested in really understanding Convolutional Neural Networks and willing to create their own Object Detection Framework in Python Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Deep Learning Zero To Hero - Hands-On With Python Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 6.05 GB | Duration: 10h 56m Learn Deep learning practically from scratch using Python What you'll learn How to build artificial neural networks Architectures of feedforward and convolutional networks The calculus and code of gradient descent Learn Python from scratch (no prior coding experience necessary) Requirements Basic Machine learning concepts and Python. Description Deep Learning is a new part of Machine Learning, which has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training we are going to learn and apply concepts of deep learning with live projects.The course includes the following;•Prediction in Structured/Tabular Data•Recommendation•Image Classification•Image Segmentation•Object Detection•Style Transfer•Super Resolution•Sentiment Analysis•Text Generation•Time Series (Sequence) Prediction•Machine Translation•Speech Recognition•Question Answering•Text Similarity•Image Captioning•Image Generation•Image to Image TranslationWe will be learning the followings:The theory and math underlying deep learningHow to build artificial neural networksArchitectures of feedforward and convolutional networksBuilding models in PyTorchThe calculus and code of gradient descentFine-tuning deep network modelsLearn Python from scratch (no prior coding experience necessary)How and why autoencoders workHow to use transfer learningImproving model performance using regularization Overview Section 1: Deep Learning ZERO To HERO - Hands-On With Python Lecture 1 Introduction to Hands on Deeplearning Lecture 2 What is Machine Learning Lecture 3 Popular ML Methods Lecture 4 What is Deep Learning Lecture 5 Applications of Deeplearning Lecture 6 Recommendations Lecture 7 Basic Concept of Deeplearning Lecture 8 Perception Lecture 9 Neural Network Lecture 10 Universal Approximations Theorem Lecture 11 Deep Neural Network Lecture 12 Deep Neural Network Continue Lecture 13 Getting Started Lecture 14 Where to write Code Lecture 15 Jupiter Notebook Lecture 16 Google Colab Lecture 17 Pytorch Lecture 18 Tensors Lecture 19 Tensors Continue Lecture 20 Gradients Lecture 21 MNIST Example Lecture 22 Check Sample Lecture 23 Hidden Layer Lecture 24 Interface on a Digit Lecture 25 Transfer-Learning-Overview Lecture 26 What is Transfer Learning Lecture 27 CS231n Convolutional Neural Networks Lecture 28 Download Dataset Lecture 29 Transform the Data Lecture 30 Visualize the Data Lecture 31 Define the Model Lecture 32 Add a Few Final Layers Lecture 33 Train the Model Lecture 34 Test the Model Lecture 35 What About CIFAR Lecture 36 Image Classifier on Cifar 10 Dataset Lecture 37 Download and Load Our Dataset Lecture 38 Train and Test Dataset Lecture 39 Define Our Neural Network Lecture 40 Working on Image Lecture 41 Input and Output Lecture 42 Define Our Loss Function Lecture 43 Train Data in Enumerate Lecture 44 Train Data in Enumerate Continue Lecture 45 Test the Neural Network on the Test Image Lecture 46 Intro to Text Classifier Lecture 47 Text Classification Using CNN Lecture 48 Prepare the Data Lecture 49 Build the Model Lecture 50 Build the Model Coninue Lecture 51 More on Build the Model Lecture 52 Define a Loss Function Lecture 53 Define a Loss Function Continue Lecture 54 More on Define a Loss Function Lecture 55 Evaluate or Test the Model Lecture 56 Intro to Text Generation Lecture 57 Text Generation-Transformers Lecture 58 Text Generation-Transformers Continue Lecture 59 Transformers-Architectures Lecture 60 Transformers-Architectures Cintinue Lecture 61 Word-Generation Lecture 62 Word-Generation Continue Lecture 63 Text-Generation Lecture 64 Intro to Text Translation Lecture 65 Loading-Data Lecture 66 Preparing-Data Lecture 67 Encoder-Attention Part 1 Lecture 68 Encoder-Attention Part 2 Lecture 69 Encoder-Attention Part 3 Lecture 70 Decoder Lecture 71 Train-Eval-Functions Lecture 72 Train-Eval-Functions Continue Lecture 73 Training-Fixes Lecture 74 Training-Evaluation Lecture 75 Prediction-Tabular-Data Part 1 Lecture 76 Prediction-Tabular-Data Part 2 Lecture 77 Prediction-Tabular-Data Part 3 Lecture 78 Prediction-Tabular-Data Part 4 Lecture 79 Collaborative Filtering Lecture 80 Collaborative Filtering Continue Lecture 81 Other Recommendation Approaches Aspiring Data Scientists and AI/Machine Learning/Deep Learning Engineers Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Django 5 Build a Complete Learning management System (LMS) Published 2/2024 Created by Hillary Ronoh MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 53 Lectures ( 5h 28m ) | Size: 2.71 GB Learn Django from scratch by Building a fully functional Learning management System with multiple Payment Gateways. What you'll learn: Learn how to use Python for web development with Django Learn how to use Python for web development with Django Learn How To Build Web Applications Using Django & Python Learn how to integrate payment gateways with Django Learn How To Customize Admin Panel In Django Learn How To Add Advanced Features Like Pagination & Search Requirements: Must be familiar with the basics of Python Description: Are you ready to take your Python Django web applications to the next level? In this course, you will learn how to build an advanced course or learning management system from scratch, so even if you are a beginner in Django, then do not worry, this course will take you step by step and make you a master working with Django and JavaScript, among many other tools we will use throughout the course.We're going to teach you the newest things for making awesome web apps with Python 3 and the latest Django version. But This course is like a toolkit that has everything you need. Whenever you're starting your adventure as a web developer, you can come back here for answers.Below are some features of the system we will be building in this courseThis system is a web based responsive application that includes an online learning management system.Fully responsive: High-quality responsive design makes the content accessible on different devices.YouTube Video Support can use video upload, (AWS S3, Vimeo coming soon) YouTube video links as course parts so they could be used as free and safe video storage.Staff & permissions: Create staff for different departments with specific access levels. Role Management system for various admin and staff in admin panel.Forget password: Forget password using Email Verification.Professional admin panel: Everything is under your control in the beautiful admin panel. There are many accessibilities, reports, and lists that are based on functionalities.Automatic logout after x ✅Duration Time inactive.Add Unlimited Level of category.Student dashboard course filter and search.User Management (Role based).PayPal payment gateway module compatible (Pay via Balance, Credit and Card Payment).Admin enroll a student directly to a course.Admin to create courses & assign it to instructors. Who this course is for: Anyone who want to learn Django web Framework Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Machine Learning With Python 2024 Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.71 GB | Duration: 8h 1m Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more! What you'll learn learn how to use data science and machine learning with Python. Understand Machine Learning from top to bottom. Learn NumPy for numerical processing with Python. Create supervised machine learning algorithms to predict classes. Requirements No prior knowledge of machine learning required. Basic knowledge of Python Description Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence. Overview Section 1: Machine Learning With Python 2023 Lecture 1 Introduction to Course Lecture 2 What is Machine Learning Lecture 3 Life Cycle Lecture 4 Introduction to Numpy Library Lecture 5 Creating Arrays from Scratch Lecture 6 Creating Arrays from Scratch Continued Lecture 7 Array Indexing and Slicing Lecture 8 Numpy Array Functions and Shape Modification Lecture 9 Mathematical Operations on Numpy Arrays Lecture 10 Introduction to Pandas Library Lecture 11 Working with Pandas DataFrames Lecture 12 Slicing and Indexing with Pandas Lecture 13 Create DataFrame and Explore Dataset Lecture 14 Data Analysis with Pandas DataFrame Lecture 15 Other Useful Methods in Pandas Library Lecture 16 Introduction to Matplotlib Lecture 17 Customizing Line Plots Lecture 18 Create Plot Using DataFrame Lecture 19 Standard Scaler to Scale the Data Lecture 20 Encoding Categorical Data Lecture 21 Sklearn Pipeline and Column Transformer Lecture 22 Evaluation Metrics in Sklearn Lecture 23 Linear Regression Lecture 24 Evaluation of Linear Regression Model Lecture 25 Polynomial Regression Lecture 26 Polynomial Regression Continued Lecture 27 Sklearn Pipeline Polynomial Regression Lecture 28 Decision Tree Classifier Lecture 29 Decision Tree Evaluation Lecture 30 Random Forest Lecture 31 Support Vector Machines Lecture 32 Kmeans Clustering Lecture 33 KMeans Clustering - Hands On Lecture 34 Data Loading and Analysis Lecture 35 Dimensionality Reduction with PCA Lecture 36 Hyper Parameter Tuning Lecture 37 Summary Section 2: Machine Learning with Python Case Study - Covid19 Mask Detector Lecture 38 Introduction to Course Lecture 39 Getting System Ready Lecture 40 Read and Write Images Lecture 41 Resize and Crop Lecture 42 Working with Shapes Lecture 43 Working with Text Lecture 44 Pre-Requisite for Face Detection Lecture 45 Detect the Face Lecture 46 Introduction to Deep Learning with Tensorflow Lecture 47 Model Building Lecture 48 Training the Mask Detector Lecture 49 Saving the Best Model Lecture 50 Basic Front End Design of App Lecture 51 File Upload Interface for App Lecture 52 App Prep Lecture 53 App Build and Testing Lecture 54 AWS Deployment Lecture 55 AWS Deployment Continued Section 3: Machine Learning Python Case Study - Diabetes Prediction Lecture 56 Introduction to Pima Indians Diabetes Using Machine Learning Lecture 57 Installation of Anaconda Lecture 58 Installation of Libraries Lecture 59 Steps in Machine Learning Lecture 60 Dataset and Logistic Regression Lecture 61 Pima Classification Lecture 62 Exclude the Header Lecture 63 Conversion of String into Number Lecture 64 Split the Dataset Lecture 65 Check the ROC Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Coursera - Deep Learning Specialization by DeepLearning.AI Last updated 1/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 373 Lessons ( 50h 51m ) | Size: 5.67 GB Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques! What you'll learn Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering Skills you'll gain Recurrent Neural Network Tensorflow Convolutional Neural Network Artificial Neural Network Transformers The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia. Applied Learning Project By the end you'll be able to • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.39 GB | Duration: 3h 13m Build Serverless Flight App with AWS CDK: Learn DynamoDB, Lambda, Cognito, Amplify, API Gateway, Event Bridge, SES. Free Download What you'll learn Build a robust Flight Seat Booking System from scratch. Harness the agility of serverless architecture with AWS CDK. Leverage AWS services for seamless integration and automation Learn To Test AWS services end to end Requirements Intermediate Programming experience will be needed, understanding of Object Oriented Programming is important Description Build a Real-World Application: Dive straight into the practical world of AWS CDK by constructing a Flight Seat Booking application from scratch.Hands-On Learning: Learn by doing with step-by-step guidance through the integration of AWS services such as DynamoDB, Lambda, Cognito, Amplify, API Gateway, Event Bridge, and SES.No-Nonsense Approach: Skip the fluff and get straight to the point. This course focuses on actionable insights and practical skills for immediate application.Learn AWS CDK: Gain proficiency in AWS Cloud Development Kit (CDK) as you build, deploy, and manage your application's infrastructure effortlessly.Event-Driven Architecture: Understand and implement event-driven architecture in the context of a real-world flight seat booking scenario.User Authentication with AWS Cognito: Ensure secure access to your application by implementing user authentication using AWS Cognito.API Development with AWS API Gateway: Create robust APIs to enhance the functionality and interaction of your application.Seamless Communication with SES: Implement communication features using Simple Email Service (SES) for a comprehensive user experience.End-to-End Testing with AWS: Learn to perform end-to-end testing on AWS for a reliable and scalable application.Ideal for Udemy Learners: Tailored for Udemy's interactive platform, this course is designed to accelerate your AWS CDK journey and advance your career in cloud application development. Enroll now to take your skills to new heights! Overview Section 1: Introduction Lecture 1 Course Resources Lecture 2 Creating AWS Account Lecture 3 Setting up AWS Credentials Lecture 4 What is AWS CDK Lecture 5 CDK Setup Lecture 6 CDK Walkthrough Section 2: Setting up Auth Flow Lecture 7 Section Overview Lecture 8 Understanding UserPool and UserPoolClient Lecture 9 Creating Database Stack Lecture 10 Creating Post Confirmation Lambda Function Lecture 11 Create Compute Stack Lecture 12 Deploying Auth Stack Lecture 13 FrontEnd WalkThrough Lecture 14 Set up for Testing Lecture 15 Writing our Sign Up Test Lecture 16 Running our Sign Up Test Section 3: Deploying Database Constucts Lecture 17 Deploying Flights and Seats Table Lecture 18 Showing Flight Data Lecture 19 Showing Seats Data Section 4: API and Event Bridge Stack Lecture 20 Deploy API Stack Lecture 21 Deploy Event Bridge Stack Lecture 22 Walk Through Lecture 23 Create Booking Function Lecture 24 Deploy Create Booking Function Lecture 25 Walk Through Create Booking Function Lecture 26 Register Booking Function Lecture 27 Walkthrough Register Booking Lecture 28 Deploy Sync Function Lecture 29 Walkthrough Sync Function Section 5: SES Stack Lecture 30 Housekeeping Lecture 31 Deploy SES Stack Lecture 32 Walkthrough Section 6: FrontEnd-Amplify Lecture 33 Setup Lecture 34 Walkthrough Software and cloud engineers who would like to learn Serverless technologies in a new way Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.39 GB | Duration: 3h 13m Build Serverless Flight App with AWS CDK: Learn DynamoDB, Lambda, Cognito, Amplify, API Gateway, Event Bridge, SES. Free Download What you'll learn Build a robust Flight Seat Booking System from scratch. Harness the agility of serverless architecture with AWS CDK. Leverage AWS services for seamless integration and automation Learn To Test AWS services end to end Requirements Intermediate Programming experience will be needed, understanding of Object Oriented Programming is important Description Build a Real-World Application: Dive straight into the practical world of AWS CDK by constructing a Flight Seat Booking application from scratch.Hands-On Learning: Learn by doing with step-by-step guidance through the integration of AWS services such as DynamoDB, Lambda, Cognito, Amplify, API Gateway, Event Bridge, and SES.No-Nonsense Approach: Skip the fluff and get straight to the point. This course focuses on actionable insights and practical skills for immediate application.Learn AWS CDK: Gain proficiency in AWS Cloud Development Kit (CDK) as you build, deploy, and manage your application's infrastructure effortlessly.Event-Driven Architecture: Understand and implement event-driven architecture in the context of a real-world flight seat booking scenario.User Authentication with AWS Cognito: Ensure secure access to your application by implementing user authentication using AWS Cognito.API Development with AWS API Gateway: Create robust APIs to enhance the functionality and interaction of your application.Seamless Communication with SES: Implement communication features using Simple Email Service (SES) for a comprehensive user experience.End-to-End Testing with AWS: Learn to perform end-to-end testing on AWS for a reliable and scalable application.Ideal for Udemy Learners: Tailored for Udemy's interactive platform, this course is designed to accelerate your AWS CDK journey and advance your career in cloud application development. Enroll now to take your skills to new heights! Overview Section 1: Introduction Lecture 1 Course Resources Lecture 2 Creating AWS Account Lecture 3 Setting up AWS Credentials Lecture 4 What is AWS CDK Lecture 5 CDK Setup Lecture 6 CDK Walkthrough Section 2: Setting up Auth Flow Lecture 7 Section Overview Lecture 8 Understanding UserPool and UserPoolClient Lecture 9 Creating Database Stack Lecture 10 Creating Post Confirmation Lambda Function Lecture 11 Create Compute Stack Lecture 12 Deploying Auth Stack Lecture 13 FrontEnd WalkThrough Lecture 14 Set up for Testing Lecture 15 Writing our Sign Up Test Lecture 16 Running our Sign Up Test Section 3: Deploying Database Constucts Lecture 17 Deploying Flights and Seats Table Lecture 18 Showing Flight Data Lecture 19 Showing Seats Data Section 4: API and Event Bridge Stack Lecture 20 Deploy API Stack Lecture 21 Deploy Event Bridge Stack Lecture 22 Walk Through Lecture 23 Create Booking Function Lecture 24 Deploy Create Booking Function Lecture 25 Walk Through Create Booking Function Lecture 26 Register Booking Function Lecture 27 Walkthrough Register Booking Lecture 28 Deploy Sync Function Lecture 29 Walkthrough Sync Function Section 5: SES Stack Lecture 30 Housekeeping Lecture 31 Deploy SES Stack Lecture 32 Walkthrough Section 6: FrontEnd-Amplify Lecture 33 Setup Lecture 34 Walkthrough Software and cloud engineers who would like to learn Serverless technologies in a new way Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Supervised Machine Learning In Python by EDUCBA Bridging the Gap Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.78 GB | Duration: 8h 22m A practical course about supervised machine learning using Python programming language What you'll learn Python Basics Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc.. Machine learning Concept and Different types of Machine Learning Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more Requirements Python porgramming language and Data pre-processing techniques Description In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language. Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.In the realm of cutting-edge technology, machine learning stands at the forefront, revolutionizing industries and transforming the way we interact with the world. From personalized recommendations to autonomous vehicles, machine learning empowers computers to learn from vast amounts of data and make intelligent decisions. If you've ever been captivated by the idea of building intelligent systems, understanding the prerequisites for machine learning is your essential first step.Embarking on a journey into machine learning requires a solid foundation in several key areas. As with any endeavor, building upon a sturdy groundwork paves the way for success. Let us unveil the prerequisites that will equip you with the skills to unravel the mysteries of machine learning and harness its potential to shape the future.Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many moreMachine learning Concept and Different types of Machine LearningMachine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..Feature engineeringPython Basics Overview Section 1: Supervised Machine Learning in Python Lecture 1 Introduction to Machine Learning Lecture 2 Advantages and Disadvantages of Machine Learning Lecture 3 NumPy Introduction Lecture 4 Features and Installation Lecture 5 NumPy Array Creation Lecture 6 NumPy Array Attributes Lecture 7 NumPy Array Operations Lecture 8 NumPy Array Operations Continue Lecture 9 NumPy Array Unary Operations Lecture 10 Numpy Array Splicing Lecture 11 NumPy Array Shpe Lecture 12 Stacking Together Different Arrays Lecture 13 Splitting one Array into Several Smaller ones Lecture 14 Copies and Views Lecture 15 NumPy Array Indexing Lecture 16 NumPy Array Indexing Continue Lecture 17 NumPy Array Boolean Lecture 18 Introduction to Matlplotlib Lecture 19 Understanding Various Functions of Pyplot Lecture 20 Multiple Figures and Subplots Lecture 21 Intro to Pandas Lecture 22 Intro to Pandas Continue Lecture 23 Data Structure in Pandas Lecture 24 Data Structure in Pandas Continue Lecture 25 Pandas Column Select Lecture 26 Remove Operations Lecture 27 Pandas Arithmetic Operations Lecture 28 Pandas Arithmetic Operations Continue Lecture 29 Introduction to Scikit Learn Lecture 30 Supervised Lecture 31 Unsupervised Learning Lecture 32 Load Data Set Lecture 33 Scikit Example Digits Lecture 34 Digits Dataset Using Matplotlib Lecture 35 Understading Metrics of Predicted Digits Dataset Lecture 36 Persisting Models Lecture 37 K-NN Algorithm with Example Lecture 38 Cross Validation Lecture 39 Cross Validation Techniques Lecture 40 K-Means Clustering Example Lecture 41 Agglomeration Lecture 42 PCA Pipeline Lecture 43 Face Recognition Lecture 44 Face Recognition Output Lecture 45 Right Estimator Lecture 46 Text Data Example Lecture 47 Extracting Features Lecture 48 Occurrences to Frequencies Lecture 49 Classifier Training Lecture 50 Performance Analysis on the Test Set Lecture 51 Parameter Tuning Lecture 52 Language Identifcation Lecture 53 Movie Review Screen Stream Lecture 54 Movie Review Screen Stream Continue Python developers, Data Scientists, Computer engineers, Researchers Students Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Deep Learning - Neural Networks In Python Using Case Studies Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.41 GB | Duration: 6h 18m Learn how a neural network is built from basic building blocks using Python What you'll learn Learn how a neural network is built from basic building blocks (the neuron) Learn how Deep Learning works Code a neural network from scratch in Python and numpy Describe different types of neural networks and the different types of problems they are used for Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data. Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.Learn how Deep Learning works (not just some diagrams and magical black box code)Learn how a neural network is built from basic building blocks (the neuron)Code a neural network from scratch in Python and numpyCode a neural network using Google's TensorFlowDescribe different types of neural networks and the different types of problems they are used forDerive the backpropagation rule from first principles Overview Section 1: Deep Learning: Convolutional Neural Network CNN using Python Lecture 1 Introduction of Project Lecture 2 Overview of CNN Lecture 3 Installations and Dataset Structure Lecture 4 Import libraries Lecture 5 CNN Model and Layers Coding Lecture 6 Data Preprocessing and Augmentation Lecture 7 Understanding Data generator Lecture 8 Prediction on Single Image Lecture 9 Understanding Different Models and Accuracy Section 2: Deep Learning: Artificial Neural Network ANN using Python Lecture 10 Introduction of Project Lecture 11 Setup Environment for ANN Lecture 12 ANN Installation Lecture 13 Import Libraries and Data Preprocessing Lecture 14 Data Preprocessing Lecture 15 Data Preprocessing Continue Lecture 16 Data Exploration Lecture 17 Encoding Lecture 18 Encoding Continue Lecture 19 Preparation of Dataset for Training Lecture 20 Steps to Build ANN Part 1 Lecture 21 Steps to Build ANN Part 2 Lecture 22 Steps to Build ANN Part 3 Lecture 23 Steps to Build ANN Part 4 Lecture 24 Predictions Lecture 25 Predictions Continue Lecture 26 Resampling Data with Imbalance-Learn Lecture 27 Resampling Data with Imbalance-Learn Continue Section 3: Deep Learning: RNN, LSTM, Stock Price Prognostics using Python Lecture 28 Introduction of Project Lecture 29 Installation Lecture 30 Libraries Lecture 31 Dataset Explore Lecture 32 Import Libraries Lecture 33 Data Preprocessing Lecture 34 Exploratory Data Analysis Lecture 35 Exploratory Data Analysis Continue Lecture 36 Feature Scaling Lecture 37 Feature Scaling Continue Lecture 38 More on Feature Scaling Lecture 39 Building RNN Lecture 40 Building RNN Continue Lecture 41 Training of Network Lecture 42 Prediction on Test Data Lecture 43 Prediction on Test Data Continue Lecture 44 Final Result Visualization Section 4: Deep Learning: Project using Convolutional Neural Network CNN in Python Lecture 45 Introduction to Project Lecture 46 Google Collab Lecture 47 Importing Packages and Data Lecture 48 Preprocessing and Model Creation Lecture 49 Training the Model and Prediction Lecture 50 Model Creation using CNN Lecture 51 CNN Model Prediction Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course,Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Experiment with Generative AI Image Learning Adobe Firefly Published 1/2024 Created by Uttam Kumar Roy MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 13 Lectures ( 1h 20m ) | Size: 1 GB Adobe Firefly For Beginners What you'll learn: Referencing Image Using Generative fill creatively Using Generative recolor All the features of Adobe Firefly Requirements: No experience needed. You just need to be able browse Adobe Firefly. You can use computer or smartphone to surf the site. Description: Today, there are many generative AI programs and most of them offer text to image.As Adobe has much experience in creating a lot of creative tools, their generative AI product Adobe Firefly offer many services.In this course, we will explore Adobe Firefly.You will learn:Text to Image and play with many of Adobe Firefly featuresGenerative Fill to add and remove objects easilyText EffectsGenerative Recolor for your artText to TemplateText to Vector GraphicsHow to use Adobe FireflyHow to start with promptsHow to use reference image as stylePhoto editingHow to use generative fill effectivelyHow use generative recolorWe will complete some projects together and you will get some tips to get more done from Adobe Firefly.FAQ:How To Complete The Project:Download the project filesYou can also go to Unsplash to download some files for the experimentGo to Adobe FireflyCan I follow the course using a smartphone?yes, you can go to Adobe Firefly but the user interface will be different than the computer.I am Uttam Kumar, I have a master's degree in e-commerce technology and I love teaching.I welcome you to this course. Who this course is for: Beginner who wants to explore Generative AI Image Photographer who wants sharpen their photo adding some extra details or removing objects easily Graphic designer who wants to create flyer, banner creatively by adding AI Image Homepage Say "Thank You" Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] The Beginners Guide To Learning HTML/HTML5 the right way Last updated 1/2023 Duration: 1h 6m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 413 MB Genre: eLearning | Language: English New to HTML? Don't Know Where To Start? Worry No more! I cover all the basics so you can stop thinking and start coding What you'll learn You'll be equipped with the right HTML knowledge and can start coding write away! Requirements No specific knowledge required for a prerequisite. Description HTML (Hyper text markup language) One of the oldest and one of the most revolutionary computer languages to have ever been founded since the dawn of time. Almost every website on earth has been coded with HTML. It is now time for you to also master the art of this language, so you can start creating responsive, beautiful websites for yourself or your clients and perhaps even make it a long term career to follow. In less than half an hour you will be set up with the basic knowledge of HTML that is essential to learn. We'll first familiarize ourselves with HTML and shortly afterwards we'll simply play around with the concepts and ideas we just learned and later I'll even guide you on what to learn after finishing this course! This course has lectures, walkthroughs, quizzes and tutorials on you can build your very first webpage and kickstart your journey in coding, and we development. I've used resources from various courses, to build a comprehensive yet concise course that gives you exactly what you need without all the extra bits. This means that all lectures are to the point and do not waste any of your time! Happy learning! Who this course is for Beginners in HMTL Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download Free Download Coursera - Learning Linux for LFCA Certification Specialization Released 12/2023 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 73 Lessons ( 4h 46m ) | Size: 616 MB Become proficient with Linux. Learn Linux for LFCA Certification Skills you'll gain applying permissions updating packages managing Linux File Systems creating links to files and directories filtering text files This specialization is intended for beginners to learn how to become proficient in Linux programming. It will prepare you for a role as an information technology professional by introducing you to the Linux operating system. You will explore creating security through backups and redundancy, securing the perimeter of your network and systems, and managing a system with the Linux OS installed. Applied Learning Project Each course within this specialization provides learners with the opportunity to test what they have learned throughout the courses with graded quizzes. Learners will also have the opportunity to practice writing code in a lab environment utilizing the provided labs. Homepage [Hidden Content]