Introduction to Machine Learning with Python

About Course

This course introduces you to the world of machine learning using Python. Designed with clarity and practicality in mind, you’ll explore the foundational concepts of AI, ML, and deep learning, learn how to code with Python, understand essential data preprocessing steps, and build machine learning models from scratch. You’ll wrap up the course with two real-world projects that showcase your new skills.

The differences between AI, Machine Learning, and Deep Learning
Supervised and Unsupervised learning algorithms
Python programming essentials for machine learning
Data preprocessing, visualization, and handling
How to build and evaluate ML models
Implementing ML models using libraries like Scikit-learn and Pandas
Building complete ML projects from scratch

Course Content

Module 1: Introduction to AI & Machine Learning

AI vs Machine Learning vs Deep Learning

05:34
Types of Machine Learning

07:03
Supervised Learning

06:12
Unsupervised Learning

08:02
Introduction to Deep Learning

08:39

Module 2: Python for Machine Learning

Module 3: Data Science Libraries in Python

Module 4: Data Preprocessing Essentials

Module 5: Model Building and Evaluation

Module 6: Key Algorithms

Module 7: Projects
To reinforce your learning, make sure to follow along with the project videos. Practicing as you watch will help you apply what you've learned and build real-world machine learning experience using Python.

Student Ratings & Reviews

No Review Yet

About Course

What Will You Learn?

Course Content

Module 1: Introduction to AI & Machine Learning

AI vs Machine Learning vs Deep Learning

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Introduction to Deep Learning

Module 2: Python for Machine Learning

Getting Started with Google Colab

Python Basics: Syntax

Python Basics: Data Types

Lists, Tuples, Sets, and Dictionaries

Python Operators and Logical Statements

Control Flow: if-else statement

Loops in Python | For Loop in Python and While Loop

Writing Functions in Python

Module 3: Data Science Libraries in Python

NumPy Basics for Numerical Computation

Pandas for Data Manipulation

Matplotlib for Data Visualization

Seaborn for Advanced Plotting

Module 4: Data Preprocessing Essentials

Data Collection

Importing from Kaggle

Handling Missing Values

Data Normalization and Standardization

Label Encoding and Categorical Data Handling

Train-Test Split and Data Balancing Techniques

Module 5: Model Building and Evaluation

Introduction to ML Models & Model Selection

Overfitting: Causes for Overfitting and its Prevention

Underfitting: Causes for Underfitting and its Prevention

Bias Variance Tradeoff

Model Evaluation I: Accuracy

Model Evaluation II: Precision, Recall, F1 Score

Introduction to Gradient Descent & Loss Functions

Module 6: Key Algorithms

Linear Regression Intuition & Implementation

Logistic Regression Intuition & Implementation

K-Nearest Neighbors (KNN) Basics

Decision Tree Classifier: Intuition and Math

Module 7: Projects To reinforce your learning, make sure to follow along with the project videos. Practicing as you watch will help you apply what you've learned and build real-world machine learning experience using Python.

Project 1: SONAR Rock vs Mine Prediction

Project 2: Diabetes Prediction Using ML

Student Ratings & Reviews

Module 7: Projects
To reinforce your learning, make sure to follow along with the project videos. Practicing as you watch will help you apply what you've learned and build real-world machine learning experience using Python.