Data Science and Machine Learning Full course

About Course

This comprehensive course takes you step-by-step into the world of Data Science and Machine Learning using Python and Jupyter Notebook.
You will start from the basics of Python programming and progress through data analysis, visualization, statistics, machine learning algorithms, and real-world projects.
The course is designed with a hands-on approach — you’ll code along in Jupyter, work with real datasets, and build practical machine learning models.

By the end, you’ll have the skills to analyze data, build predictive models, and apply machine learning techniques confidently.

Understand the fundamentals of Python programming for data science.
Work efficiently with Jupyter Notebook for interactive coding and visualization.
Handle, clean, and preprocess datasets using NumPy & Pandas.
Create insightful data visualizations using Matplotlib & Seaborn.
Apply statistics and probability concepts in data science.
Explore machine learning algorithms (Regression, Classification, Clustering, etc.).
Build end-to-end machine learning projects with Scikit-Learn.
Understand model evaluation, tuning, and optimization.
Work on real-world datasets (finance, health, e-commerce, etc.).
Learn best practices for data science workflow and ML deployment basics.

Course Content

Introduction to Data Science

Lesson 1 Introduction to Data science

22:43
Lesson 2 Data Science Process & Workflow

06:38
Lesson 3 Difference Between Data Science & Related Fields

05:08
Lesson 4 Tools & Technologies in Data Science

01:44
Lesson 5 Setting up the environment install software

07:57

Basic Python Programming for Data Science

Lesson 1 Introduction to Python for Data Science

13:57
Lesson 2 Install Python

03:54
Lesson 3 Print Hello world Python

07:01
Lesson 4 Variable and Datatype

12:28
Lesson 5 Operators

13:45
Lesson 6 Input data from keyboard

04:35
Lesson 7 Decision control structure if, elif and else

10:50
Lesson 8 Loop (for ,while and nested loop)

11:07
Lesson 9 function

10:32
Lesson 10 List

12:38

Working with python libraries for Data Science

Data Cleaning and Visualization in Data Science

Global Population Growth Analysis Project

Introduction to Machine Learning

Somalia House Prices Prediction Project (ML + DL)

Lesson 0 Machine Learning Project Guideline

07:29
Lesson 1 Problem Definition

07:15
Lesson 2 Data Collecting

02:16
Lesson 3 Load and Inspection Dataset

11:41
Lesson 4 Data Cleaning and Preprocessing Outliers

08:49
Lesson 5 Data Cleaning and Preprocessing Removing Duplicate and Missing Values

22:58
Lesson 6 Convert Data Type

03:54
Lesson 7 Feature Engineering

17:16
Lesson 8 Data Visualization

25:54

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About Course

What Will You Learn?

Course Content

Introduction to Data Science

Lesson 1 Introduction to Data science

Lesson 2 Data Science Process & Workflow

Lesson 3 Difference Between Data Science & Related Fields

Lesson 4 Tools & Technologies in Data Science

Lesson 5 Setting up the environment install software

Basic Python Programming for Data Science

Lesson 1 Introduction to Python for Data Science

Lesson 2 Install Python

Lesson 3 Print Hello world Python

Lesson 4 Variable and Datatype

Lesson 5 Operators

Lesson 6 Input data from keyboard

Lesson 7 Decision control structure if, elif and else

Lesson 8 Loop (for ,while and nested loop)

Lesson 9 function

Lesson 10 List

Working with python libraries for Data Science

Lesson 1 NumPy Library for Descriptive Statistics

Lesson 2 Pandas Library

Lesson 3 Data Visualization

Lesson 4 Assignment

Data Cleaning and Visualization in Data Science

Lesson 1 Collect Dataset

Lesson 2 Preprocessing Data(Missing Value)

Lesson 3 Handling Outliers

Lesson 4 Duplicate Handling

Lesson 5 Upper and Lower case Handling

Lesson 6 Removing Punctuation Handling

Lesson 7 Data Visualization seaborn & matplotlib

Global Population Growth Analysis Project

Lesson 1 Dataset Preparing

Lesson 2 Load & Inspect Dataset

Lesson 3 Processing dataset

Lesson 4 Data Visualization

Introduction to Machine Learning

Introduction to Machine Learning

Somalia House Prices Prediction Project (ML + DL)

Lesson 0 Machine Learning Project Guideline

Lesson 1 Problem Definition

Lesson 2 Data Collecting

Lesson 3 Load and Inspection Dataset

Lesson 4 Data Cleaning and Preprocessing Outliers

Lesson 5 Data Cleaning and Preprocessing Removing Duplicate and Missing Values

Lesson 6 Convert Data Type

Lesson 7 Feature Engineering

Lesson 8 Data Visualization

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