What You Will Learn

This course provides a structured approach to learning AI with Python. You will start from the basics and progress to more advanced topics.
Here are the core areas covered:

Module 1: Introduction to AI & Environment Setup

• Understand the basics of AI, Machine Learning, and Deep Learning.

• Set up your workspace by installing Python and Anaconda, and learn to use Jupyter Notebooks or Google Colab.

• Get familiar with essential Python libraries for AI, such as NumPy and Pandas.

Module 2: The Core of AI — Working with Data

• Learn data preprocessing techniques, including handling missing values and data cleaning.

• Practice feature engineering to convert raw data into a usable format for AI models.

• Use data visualization tools like Matplotlib and Seaborn to identify patterns in data.

Module 3: Introduction to Machine Learning (Scikit-Learn)

• Explore supervised learning methods, including regression and classification.

• Understand unsupervised learning through clustering techniques.

• Learn how to evaluate models using metrics such as accuracy and mean squared error.

Module 4: Introduction to Deep Learning & Neural Networks

• Gain insights into how neural networks operate, including key concepts such as neurons, weights, biases, and activation functions.

• Build your first perceptron, a basic neural network layer, using Python.

• Get a brief overview of modern deep learning frameworks like TensorFlow/Keras and PyTorch.

Module 5: Practical AI & Next Steps

• Learn about computer vision and natural language processing, focusing on how AI interprets images and text.

• Explore the AI landscape, moving from classic machine learning to modern concepts like computer vision pipelines.

• Complete a capstone project where you will build an end-to-end predictive model from a raw dataset.o work with AI using Python.