The Supervised Machine Learning Bootcamp

Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs

Created by: 365 Careers
Creating opportunities for Data Science and Finance students

What Will I 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

Requirements

• The course is open to everyone who wants to learn data science.
• You’ll need to install Anaconda and Jupyter Notebook. We will show you how to do that step by step.

Target audience

• Aspiring data scientists and machine learning engineers
• Data Scientists and Data Analysts looking to up their skillset
• Anyone who wants to gain an understanding of the machine learning field and its vast opportunities