Size: 1004.51 MB
Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn
What you’ll learn
- Understand and implement K-Nearest Neighbors in Python
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Understand the limitations of KNN
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User KNN to solve several binary and multiclass classification problems
- Understand and implement Naive Bayes and General Bayes Classifiers in Python
- Understand the limitations of Bayes Classifiers
- Understand and implement a Decision Tree in Python
- Understand and implement the Perceptron in Python
- Understand the limitations of the Perceptron
- Understand hyperparameters and how to apply cross-validation
- Understand the concepts of feature extraction and feature selection
- Understand the pros and cons between classic machine learning methods and deep learning
- Use Sci-Kit Learn
- Implement a machine learning web service
Requirements
- Python, Numpy, and Pandas experience
- Probability and statistics (Gaussian distribution)
- Strong ability to write algorithms
Description
In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.- calculus
- probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy, Scipy, Matplotlib
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
- Students and professionals who want to apply machine learning techniques to their datasets
- Students and professionals who want to apply machine learning techniques to real world problems
- Anyone who wants to learn classic data science and machine learning algorithms
- Anyone looking for an introduction to artificial intelligence (AI)
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