1. Introduction

ML Fundamentals

ML Common Use Cases

Understanding Supervised and Unsupervised Learning Techniques

2. Clustering

Similarity Metrics

Distance Measure Types: Euclidean, Cosine Measures

Creating predictive models

Understanding K-Means Clustering

Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model

Case study

3. Implementing Association rule mining

What is Association Rules & its use cases?

What is Recommendation Engine & it’s working?

Recommendation Use-case

Case study

4. Decision Tree Classifier

How to build Decision trees

What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Decision Tree

Confusion Matrix

Case study

5. Random Forest Classifier

What is Random Forests

Features of Random Forest

Out of Box Error Estimate and Variable Importance

Case study

6. Naive Bayes Classifier.

Case study

7. Problem Statement and Analysis

Various approaches to solve a Data Science Problem

Pros and Cons of different approaches and algorithms.

8. Linear Regression

Case study

Introduction to Predictive Modeling

Linear Regression Overview

Simple Linear Regression

Multiple Linear Regression

9. Logistic Regression

Case study

Logistic Regression Overview

Data Partitioning

Univariate Analysis

Bivariate Analysis

Multicollinearity Analysis

Model Building

Model Validation

Model Performance Assessment AUC & ROC curves

Scorecard

10. Support Vector Machines

Case Study

Introduction to SVMs

SVM History

Vectors Overview

Decision Surfaces

Linear SVMs

The Kernel Trick

Non-Linear SVMs

The Kernel SVM

11. Time Series Analysis

Describe Time Series data

Format your Time Series data

List the different components of Time Series data

Discuss different kind of Time Series scenarios

Choose the model according to the Time series scenario

Implement the model for forecasting

Explain working and implementation of ARIMA model

Illustrate the working and implementation of different ETS models

Forecast the data using the respective model

What is Time Series data?

Time Series variables

Different components of Time Series data

Visualize the data to identify Time Series Components

Implement ARIMA model for forecasting

Exponential smoothing models

Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Implement respective model for forecasting

Visualizing and formatting Time Series data

Plotting decomposed Time Series data plot

Applying ARIMA and ETS model for Time Series forecasting

Forecasting for given Time period

Case Study

12. Machine learning algorithms Python

Various machine learning algorithms in Python

Apply machine learning algorithms in Python

13. Feature Selection and Pre-processing

How to select the right data

Which are the best features to use

Additional feature selection techniques

A feature selection case study

Preprocessing

Preprocessing Scaling Techniques

How to preprocess your data

How to scale your data

Feature Scaling Final Project

14. Which Algorithms perform best

Highly efficient machine learning algorithms

Bagging Decision Trees

The power of ensembles

Random Forest Ensemble technique

Boosting – Adaboost

Boosting ensemble stochastic gradient boosting

A final ensemble technique

15. Model selection cross validation score

Introduction Model Tuning

Parameter Tuning GridSearchCV

A second method to tune your algorithm

How to automate machine learning

Which ML algo should you choose

How to compare machine learning algorithms in practice

16. Text Mining& NLP

Sentimental Analysis

Case study