Chapter10: Unsupervised Learning

INDEX

0. Links & Overview

Notes PDF Download Link
Github Repo Link
Project 1: Kaggle
Project 2: Kaggle

1. Getting started with Machine Learning

What is Machine Learning?
How Machine Learning Works
Types of Machine Learning
Types of Data

2. Graphical & Analytical Representation of Data

Data Analysis & EDA
Graphical Representation of data
Limitation of traditional Data Analysis

3. Python as a Data-Analysis Tool

Why Python?
Jupyter Notebook
Data Types in Python
Basic Operations
Condition Statements & Loops
Functions in Python
Basic Libraries

4. Basic Data Exploration

Pandas Dataframe
Descriptive Statistics of Data
Numpy: A Statistics Module in Python
Matplotlib: Graph Plotting
Pandas: Some important Functions
Univariate Analysis
Treating Outliers
Correlation
ANOVA
Creating Training Datasets
FeatureScaling

5. Regression Modelling

Introduction
Model Evaluation Metrics
Linear Regressin
Cost Function Curve
Gradient Descent
Assumptions of Linear Regression
Steps of Linear Regression
Outcome of Linear Regression

6. Feature Engineering

Introduction
Transformation Techniques
Categorical Encoding
Feature Extraction
Dimensionality Reduction
Advanced Dimensionality Reduction
Forward Selection
Backward Selection

7. Logistic Regression

Introduction
Evaluation Metrics
Implementation

8. Decision Trees

Parametric v/s Non-Parametric Models
Working of Decision Trees
Types of Decision Trees
Splitting Criteria of Tree Nodes
Implementation

9. Ensemble Models

Ensemble Models
Bagging: Random Forest
Implementation
Hyper Parameter Tuning

10. Unsupervised Learning

Introduction
Clusters & it's properties
K-Means Clustering
Implementation

FEEDBACK FORM

CONNECT PORTFOLIO

Introduction

Clusters & it's properties

K-Means Clustering

Implementation

COMING SOON