The Supervised Machine Learning Course
The Supervised Machine Learning Course
Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs
Why should you consider taking the Supervised Machine Learning course? The supervised machine learning algorithms you will learn here are some of the most powerful data science tools you need to solve regression and classification tasks. These are invaluable skills anyone who wants to work as a machine learning engineer and data scientist should have in their toolkit. Naïve Bayes, KNNs, Support Vector Machines, Decision Trees, Random Forests, Ridge and Lasso Regression. In this course, you will learn the theory behind all 6 algorithms, and then apply your skills to practical case studies tailored to each one of them, using Python’s sci-kit learn library.
What you’ll learn
- Regression and Classification Algorithms
- Using sk-learn and Python to implement supervised machine learning techniques
- K-nearest neighbors for both classification and regression
- Naïve Bayes
- Ridge and Lasso Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- Practical case studies for training, testing and evaluating and improving model performance
- Cross-validation for parameter optimization
- Learn to use metrics such as Precision, Recall, F1-score, as well as a confusion matrix to evaluate true model performance
- You will dive into the theoretical foundation behind each algorithm with the aid of intuitive explanation of formulas and mathematical notions
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