AI and Machine Learning 10th February – 31st of March 2025

Course Overview: 

This course provides an in-depth understanding of artificial intelligence and machine learning concepts, algorithms, and practical applications.

Course Objectives: 

By the end of this course, participants will be able to:

1. Understand AI and Machine Learning Fundamentals:

• Grasp the concepts and distinctions between AI, machine learning, and deep learning.

2. Explore Machine Learning Algorithms:

• Learn about supervised, unsupervised, and reinforcement learning.

3. Work with Data for Machine Learning:

• Prepare data for machine learning applications.

4. Build Machine Learning Models:

• Implement models using libraries such as Scikit-Learn, TensorFlow, and PyTorch.

5. Evaluate and Optimize Models:

• Assess model performance and apply optimization techniques.

6. Apply AI and ML to Real-world Problems:

• Solve practical business and industry challenges.

Course Contents:

Module 1: Introduction to AI and Machine Learning

• Key concepts and evolution

• Applications in various industries

Module 2: Data Preparation for Machine Learning

• Data cleaning and preprocessing

• Feature selection and engineering

Module 3: Supervised Learning Algorithms

• Linear and logistic regression

• Decision trees and support vector machines

Module 4: Unsupervised Learning Algorithms

• Clustering techniques

• Dimensionality reduction

Module 5: Neural Networks and Deep Learning

• Basics of neural networks

• Introduction to deep learning frameworks

Module 6: Model Evaluation and Optimization

• Performance metrics

• Hyperparameter tuning

Module 7: Real-world Applications and Case Studies

• AI-powered business solutions

• Hands-on projects

Target Audience:

• Data Scientists

• Software Developers

• Analysts

• Business Professionals

• Students