Python For Ai And Machine Learning: From Beginner To Pro

Master Python for Artificial Intelligence and Machine Learning with TensorFlow, PyTorch, and Scikit-Learn.

What you'll learn

Master Python programming for AI and ML applications.

Build machine learning models with Scikit-Learn (e.g., Random Forest).

Develop deep learning models using TensorFlow and PyTorch.

Process and visualize data with Pandas, NumPy, and Matplotlib for AI/ML tasks.

Requirements

No programmingA computer with internet access (Windows, macOS, or Linux).

No prior programming or AI/ML experience required—everything is taught from scratch.

A Google account for Google Colab (free) and optional GPU access.

Enthusiasm to learn and build exciting AI/ML projects. experience needed.

Description

Welcome to Python for AI and Machine Learning: From Beginner to Pro, the ultimate course to master Python for building cutting-edge artificial intelligence (AI) and machine learning (ML) models! This comprehensive 25+ hour course is crafted for complete beginners and aspiring professionals, requiring no prior coding experience. You’ll progress from Python fundamentals to advanced AI techniques using industry-standard tools like TensorFlow, PyTorch, and Scikit-Learn, guided step-by-step to ensure success.Through 4+ hands-on projects—including a crop health predictor, image classifier, air quality forecaster, and a custom ML application—you’ll gain practical skills to create a job-ready portfolio. Learn to process and visualize data with Pandas, NumPy, and Matplotlib, and train models in the cloud using Google Colab with GPU support. The course applies AI/ML to real-world challenges in industries like agriculture, healthcare, and environmental science, making it relevant for diverse learners.Taught by Dr. Azad Rasul, a geospatial data scientist and Assistant Professor with over 150,000 students mentored, this course offers clear explanations, practical projects, and career-focused guidance for high-demand data science and AI roles.Whether you’re aiming to land a data science job, enhance your current role, or explore AI innovations, this course equips you with the tools and knowledge to succeed. Join a global community of learners and start building impactful AI solutions today!

Overview

Section 1: Introduction

Lecture 1 Welcome and course overview

Lecture 2 What is Artificial Intelligence?

Lecture 3 What is Machine Learning?

Lecture 4 Why Python is the Top Choice for AI & Machine Learning?

Lecture 5 Setting up Python

Lecture 6 Setting up your Python environment for AI/ML

Lecture 7 Installing and Running Jupyter Notebook.

Section 2: Python Programming Fundamentals

Lecture 8 Python basics: Variables, data types, and operators

Lecture 9 Control flow: If statements, loops, and exceptions

Lecture 10 Functions in Python.

Lecture 11 Modules in Python.

Lecture 12 Working with lists, dictionaries, and sets for data processing.

Section 3: Data Handling with Pandas and NumPy

Lecture 13 Introduction to NumPy for numerical computing.

Lecture 14 File Handling in Python

Lecture 15 Managing Directories in Python

Lecture 16 Data manipulation with Pandas: DataFrames, filtering, and merging.

Section 4: Data Visualization with Matplotlib and Seaborn

Lecture 17 Creating plots with Matplotlib: Line, scatter, and bar charts.

Lecture 18 Advanced visualizations with Seaborn: Heatmaps, pair plots, and box plots.

Section 5: Introduction to Machine Learning with Scikit-Learn

Lecture 19 Advanced Machine Learning Techniques for Classifying Synthetic Data

Section 6: Deep Learning with TensorFlow and PyTorch

Lecture 20 Convolutional neural network (CNN) with PyTorch for image classification

Lecture 21 Using GPUs for faster training

Section 7: Cloud-Based AI with Google Colab

Lecture 22 Introduction to Goggle Colab

Lecture 23 Setting Up Google Colab for AI/ML Projects

Section 8: Capstone Project 1: Crop Health Prediction

Lecture 24 Building a Machine Learning Model for Crop Health Analysis

Section 9: Capstone Project 2: Air Quality Monitoring

Lecture 25 Air Quality Monitoring in India: A Python and ML Case Study Part 1

Lecture 26 Air Quality Monitoring in India: A Python and ML Case Study Part 2

Lecture 27 Air Quality Monitoring in India: A Python and ML Case Study Part 3

Lecture 28 Air Quality Monitoring in India: A Python and ML Case Study Part 4

Section 10: Capstone Project 3: Counting Plants Using Computer Vision Techniques

Lecture 29 Detecting and Counting Plants Using Computer Vision Techniques

Section 11: Capstoe Project 4: Comprehensive Guide to Machine Learning, and Data Processing.

Lecture 30 Comprehensive Guide to Machine Learning, and Data Processing, Part: 1

Lecture 31 Comprehensive Guide to Machine Learning, and Data Processing, Part: 2

Lecture 32 Comprehensive Guide to Machine Learning, and Data Processing, Part: 3

Lecture 33 Comprehensive Guide to Machine Learning, and Data Processing, Part: 4

Lecture 34 Comprehensive Guide to Machine Learning, and Data Processing, Part: 5

Section 12: Further Reading and Tutorials

Lecture 35 Further Reading and Tutorials

Complete Beginners,Aspiring Data Scientists and AI Engineers,Professionals in Related Fields,Students and Researchers,Developers Transitioning to AI/ML