Arani Consulting

Email:info@araniconsulting.com

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