• Identify difference between supervised (classification) and unsupervised (clustering) technique
• Identify which technique they need to apply for a particular dataset and need
• Engineer features to meet the machine learning needs
• Write python code to carry out an analysis

Module 1: Introduction

• Introduction to Machine Learning
• Introduction to Scikit Learn Package
• Regression vs Classification

Module 2: Supervised Learning

• K Nearest Neighbour (kNN)
• Naïve Bayes
• Logistic Regression
• Support Vector Machine (SVM)
• Decision Tree & Random Forest
• Hyperparameter Model Tuning,
  Regularization Ridge and Lasso

Module 3: Unsupervised Learning

• Clustering

Module 4: Advanced Analytics

• Cross Validation
• Model Evaluation and Selection
• Select, Manipulate and Analyze Data
• Introduction to Ensemble Models