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Learn the key differences between Statistical ML vs Applied ML (Which one are you?)
Become familiar with SkLearn, Numpy and Pandas (Some of the most popular libraries in ML)
Quickstart guide and handy tips for any ML problem (Never be stuck again on any ML problem!)
Learn the following topics and in parallel, get your hands dirty with Python, the leading language of choice for Machine Learning! We'll be working on a dataset and then perform each of the below tasks on the data.
Data preprocessing, feature engineering and importance (How does data science plays a role in ML?)
Dimensionality Reduction techniques (How does Big data fit into the bigger picture?)
ML Pipeline (Understand the standard ML workflow)
Get to know when to use which algorithms with distinct examples(Use cases/Applications and concepts of ML algorithms)
What is Deep Learning? How is it a game-changer?
What is Active Learning/Reinforcement Learning?
110 Westwood Plaza, Los Angeles, CA 90095, United States
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