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With Case Study This comprehensive course offers an in-depth journey into Machine Learning and Data Science
Updated on Sep, 2026
IT and Software , Other IT and Software, Python
Duration - 6.5 hours
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This all-encompassing course is going to take one on an intense journey through machine learning and Data Science, that keeps a focus both on the art of model building and evaluation, as well as data interpretation, solving actual problems with both supervised and unsupervised learning techniques. Most concepts are applied with both Python and R. Most important topics that are introduced include regression, classification, clustering, and dimensionality reduction. Major model evaluation tools covered in the course include the Bias-Variance Tradeoff, and cross-validation. Introductory chapters on powerful libraries like NumPy, Pandas, Scikit-learn, and t-SNE are also part of the course, as well as statistical modeling in R. Whether you're a beginner or aspire to know more in depth in Machine Learning, this course grooms you with the foundation and deeper insights for dominating the data science tools and methods. It is, therefore, apt for aspiring data scientists, analysts, or those enthusiastic about AI. . . Courtesy, Dr. FAK Noble Ai Researcher, Scientists, Product Developer, Innovator & Pure Consciousness Expert Founder of Noble Transformation Hub TM
What is the Introduction to Machine Learning?, basics and types of Machine Learning? ML Unsupervised Learning: Learn concepts and techniques of Unsupervised Learning. Supervised Learning - Regression: Master regression models as they predict continuous outcomes. Evaluation Metrics for Regression Model:- Evaluate regression models using metrics like MSE, RMSE, and R-squared. Supervised Learning - Classification in Machine Learning:- Learn algorithms of classification used to do categorical prediction. Supervised Learning - Decision Trees: Understand how the concept of Decision Trees works for both classification and regression. Unsupervised Learning - Clustering: Understand how clustering occurs over data points with multiple techniques within clustering.
Basic Understanding of Mathematics Familiarity with linear algebra, probability, and statistics is helpful.
Basic Analytical and Problem-Solving Skills Ability to think critically and solve complex problems.
Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
Check out the detailed breakdown of what’s inside the course
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