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AI & Machine Learning

AI & Machine Learning Fundamentals

Explore the core principles and practical applications of Artificial Intelligence and Machine Learning

Course Introduction

This course provides a comprehensive foundation in Artificial Intelligence and Machine Learning, covering both theoretical concepts and hands-on implementation using Python and popular libraries such as Scikit-learn, TensorFlow, and Keras.

Learning Outcomes

  • Understand key AI and ML concepts and algorithms
  • Preprocess and analyze datasets effectively
  • Build predictive models using supervised and unsupervised learning
  • Work with tools like Scikit-learn, TensorFlow, and Keras
  • Deploy trained models for real-world use cases
  • Explore neural networks, deep learning, and NLP fundamentals

Course Modules

Module 1: Introduction to AI & ML
  • What is AI? What is Machine Learning?
  • AI vs ML vs Deep Learning
  • Real-world applications
  • Setting up the Python environment
Module 2: Data Preparation and Exploration
  • Data types and data quality
  • Data cleaning and preprocessing
  • Exploratory data analysis (EDA)
  • Using pandas and matplotlib
Module 3: Supervised Learning
  • Linear and logistic regression
  • Decision trees and random forests
  • Model evaluation metrics
  • Overfitting and regularization
Module 4: Unsupervised Learning
  • K-means clustering
  • Hierarchical clustering
  • Dimensionality reduction (PCA)
  • Use cases in real data
Module 5: Neural Networks & Deep Learning
  • Understanding neural networks
  • Working with TensorFlow and Keras
  • Building and training deep learning models
  • Activation functions and optimizers
Module 6: Natural Language Processing
  • Text preprocessing techniques
  • Bag-of-Words and TF-IDF
  • Text classification models
  • Intro to Transformers and Hugging Face
Module 7: Model Deployment
  • Saving and loading models
  • Creating APIs with Flask
  • Deploying to platforms like Heroku or Render
  • Integrating ML models into web apps
Module 8: Capstone Project & Advanced Topics
  • Work on a real-world AI project
  • Experiment with time series or reinforcement learning
  • Ethics in AI and model fairness
  • Exploring AutoML and model tuning

Prerequisites

  • Basic knowledge of Python programming
  • Familiarity with mathematics (linear algebra, probability)
  • Understanding of fundamental programming concepts
  • Experience with Git and GitHub is helpful

Certification

Upon successful completion of this course, you will receive a certificate of completion, demonstrating your knowledge and practical experience in AI and Machine Learning.