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Module 1

Unit 1: Machine Learning Introduction

  1. Introduction to ML problems
  2. ML terminologies
  3. ML project workflow
  4. ML real life examples

Unit 2: Jupyter Notebook introduction

  1. Working with Jupyter notebooks
  2. Markdown and Code blocks
  3. Keyboard shortcuts

Unit 3: Python Basics

  1. Python syntax
  2. Basic data types
  3. Basic data structures

Unit 4: Python advanced

  1. Numpy Arrays
  2. Plotting using Matplotlib
  3. Pandas Dataframes
  4. Introduction to Scikit Learn package

Module 2

Unit 1: Regression Modeling

  1. Introduction
  2. Modeling concept
  3. Example problem – Housing price

Unit 2: Simple Linear Regression

  1. Error metric – SSE, MSE, R Squared
  2. Least Square algorithm
  3. Gradient Descent Algorithm
  4. Implementation using scikit-learn

Unit 3: Multiple Linear Regression

  1. Dummy variables
  2. Error metric – SSE, MSE, R Squared
  3. Gradient Descent Algorithm
  4. Feature Selection (Incremental)
  5. Implementation using scikit-learn

Unit 4: Polynomial Regression

  1. Non-linear relationship
  2. Higher order terms
  3. Feature selection
  4. Modeling concepts – Avoid overfitting
  5. Implementation using scikit-learn

Module 3

Unit 1: Classification Modeling

  1. Introduction to Classification Models
  2. Error Metrics : Accuracy Score
  3. Confusion Matrix
  4. Type1 and Type 2 errors
  5. Decision boundaries

Unit 2: Logistic Regression

  1. Discrete outcomes
  2. Logit function
  3. Probability scores
  4. Implementation using scikit-learn

Unit 3: Support Vector Machines

  1. Support Vectors
  2. Decision boundary
  3. Kernel trick
  4. Hyperparameters and Model tuning
  5. Implementation using scikit-learn

Unit 4: Decision Trees

  1. Entropy
  2. Using Entropy in classification
  3. Information Gain
  4. Tree pruning
  5. Implementation using scikit-learn

Unit 5: Random Forests

  1. Bias variance errors
  2. Ensembling
  3. Randomness in Random Forest
  4. Hyperparameters
  5. Implementation using scikit-learn

Module 4

Unit 1: Cluster Modeling

  1. Introduction to clustering
  2. Distance measures
  3. Error metrics
  4. Analysing cluster outputs

Unit 2: Hierarchical Clustering

  1. Agglomerative method
  2. Divisive method
  3. Understanding Dendrogram
  4. Cutting the dendrogram for obtaining clusters
  5. Implementation using scikit-learn

Unit 3: K-Means Clustering

  1. Distance measures
  2. Centroids and their importance
  3. Steps involved in K-Means
  4. Local optima problem
  5. Implementation using scikit-learn
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