Duration

End to End Machine Learning with Python

5 days

Overview

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This course is truly step-by-step. In every new lecture we build on what had already learned and move one extra step forward. End of the course we will have a capstone project, where we can truly experience how all learning on python we can use to solve real world problem.

Module 1: Introduction to the Course

Environment Set-Up
Python Environment Setup
Jupyter Overview
Jupyter Notebooks

Module 2: Python Crash Course

Introduction to Python Crash Course

(Includes variable, loop, conditional statement etc. from scratch)

Python Crash Course Exercises

Module 3: Python for Data Analysis - Pandas

Introduction to Pandas
Series
DataFrames
Missing Data
Groupby
Merging Joining and Concatenating
Operations
Data Input and Output
Pandas Exercises

Module 4: Python for Data Visualization - Seaborn

Introduction to Seaborn
Distribution Plots
Categorical Plots
Matrix Plots
Grids
Regression Plots
Style and Color
Seaborn Exercise

Capstone1: Project using Python Data Analysis Libraries

Module 5: Introduction to Machine Learning

Supervised Learning Overview
Evaluating Performance - Classification Error Metrics
Evaluating Performance - Regression Error Metrics
Machine Learning with Python

Capstone2: Project using Python Machine Learning Libraries

Module 6: Linear Regression

Linear Regression Theory
Linear Regression with Python
Linear Regression Project Overview

Module 7: Logistic Regression

Logistic Regression Theory
Logistic Regression with Python
Logistic Regression Project Overview

Module 8: K Means Clustering

K Means Algorithm Theory
K Means with Python
K Means Project Overview

Programme scope, delivery arrangements and suitability are confirmed during enquiry.