What you'll learn:
Knowledge about Data Science methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Requirements:
Basic knowledge of the Python programming language and preferably the Pandas library
The four ways of counting (+-*/)
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Description:
Welcome to the course Data Science Methods and Techniques for Data Analysis and Machine Learning!Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.This course will teach you a large selection of Data Science methods and techniques, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer.This is a three-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and Unsupervised Learning.You will learn to master Regression, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on Udemy, with hands-on, useful practical theory, and also automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.You will learn to master Classification and supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifiers Ensembles and Voting Classifier Ensembles.You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.You will learnKnowledge about Data Science methods, techniques, theory, best practices, and tasksDeep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidenceDetailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised LearningHands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python librariesAdvanced knowledge of A.I. prediction models and automatic model creationCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work lifeAnd much more.This course includesan easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this coursean easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding taskcontent that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineera large collection of unique content, and this course will teach you many new things that only can be learned from this course on Udemya course structure built on a proven and professional framework for learning.a compact course structure and no killing timeThis course is an excellent way to learn to master Regression, Prediction, Classification, and Cluster analysis!These are the most important and useful tools for modeling, AI, and forecasting.Is this course for you?This course is for you, regardless if you are a beginner or an experienced Data ScientistThis course is for you, regardless if you have a Ph.D. or no education or experience at allThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and unsupervised learning.Course requirementsBasic knowledge of the Python programming language and preferably the Pandas libraryThe four ways of counting (+-*/)Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 15+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Who this course is for:
This course is for you, regardless if you are a beginner or an experienced Data Scientist
This course is for you, regardless if you have a Ph.D. or no education or experience at all

https://rapidgator.net/file/b2e4d8804e3c6a5181253dc55dfe42ab/Data_Science_Methods_and_Techniques_2024.part1.rar.html
https://rapidgator.net/file/4086b0ce5df36b3ebe0993f95e04f61a/Data_Science_Methods_and_Techniques_2024.part2.rar.html
https://rapidgator.net/file/11d7a6af841a88c73b17f21c9cb37cf3/Data_Science_Methods_and_Techniques_2024.part3.rar.html
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https://rapidgator.net/file/1fb5b9fb6d9eee01393a313c088d8dbb/Data_Science_Methods_and_Techniques_2024.part5.rar.html
https://rapidgator.net/file/6c31f45c4dbc918ad333aa654c4bb494/Data_Science_Methods_and_Techniques_2024.part6.rar.html
https://rapidgator.net/file/af6b05150dceb95cc0bfbfda66bc97e0/Data_Science_Methods_and_Techniques_2024.part7.rar.html
https://rapidgator.net/file/6e50245f33136d4dc11ae18ba0228ed8/Data_Science_Methods_and_Techniques_2024.part8.rar.html