Machine Learning Online Course

Module 1: INTRODUCTION TO PYTHON

Introduction to Python

Brief Overview of Python

Applications of Python

Advantages of Python

Overview Machine Learning with Python

Introduction to the Basics of Python Programming

Operators

Loops: while & for

Conditionals: if-else

Functions: Defining Functions, Anonymous Functions

Python Data Structure

Numbers

String

List

Tuple

Dictionary

Module 2: INTRODUCTION TO NUMPY

Importance of Array

Creation of Array

Array operation

Linear Algebra

Array Methods

Module 3: INTRODUCTION TO PANDAS

Pandas Series

Pandas Dataframe

Working with Structured & Unstructured Data

Accessing Tabular Data

Panadas methods

Data analysis using Pandas

Accessing Row & Column data

Data Filtering & Cleaning

Accessing indexed data

Indexing/Reindexing

Statistical Interpretation

Data Wrangling

Data Analysis

Store Dataframe to file

Module 4: INTRODUCTION TO DATA VISUALIZATION

Introduction to Matplotlib

Visualization of Array Data

Visualization of Excel Data

Different types of plotting

Line Graphs

Bar Plots, Histograms, Box Plot, Stacked Plots, Scatter Plot, Pie Chart

Data Analysis & Data Visualization using Matplotlib

Linestyles and Color

Multiple Lines on Same Plot

Controlling Line Properties

Adding Labels, Gridlines, Annotations

X and Y Ticks and Rotations

Legends

Working with Multiple Figures and Axes

Share X and Y-Axis

Adding Subplots

Module 5: INTRODUCTION TO MACHINE LEARNING

Part A: Introduction to Machine Learning

Application fields of Machine learning

Advantages of Python in Machine Learning

Steps towards Machine Learning

Understanding of Algorithms (Supervised & Unsupervised)

Feature Selection

Hyperparameter Tuning

Application and Implementation of Scikit Learn

Part B: Data Processing & Machine learning: Supervised Learning

Supervised Learning Introduction

Supervised Learning Algorithms

Regression

Linear Regression

Classification:

Logistic Regression

K-Nearest Neighbor

Naïve Bayes

Decision Tree

Random Forest

Part C: Data Processing & Machine learning: Unsupervised Learning

Unsupervised Learning Introduction

Unsupervised Learning Algorithms

Clustering: K-Means Clustering

Dimension Reduction

Principle Component Analysis