Artificial Intelligence Course

AI & Machine Learning

Gain hands-on experience in Artificial Intelligence and Machine Learning by building real-world applications. This course covers core ML concepts, data processing, predictive modeling, and AI-driven problem solving. Learn to implement practical AI solutions that enhance decision-making, automation, and innovation across industries.

About Course

This course offers a comprehensive introduction to Artificial Intelligence (AI), covering fundamental concepts, techniques, and applications.

1. Understand Core AI & ML Concepts
2. Prepare and Analyze Data
3. Build and Train Machine Learning Models
4. Use AI Tools and Frameworks
5. Evaluate and Improve Models
6. Apply AI in Real-World Scenarios
7. Practice Responsible & Ethical AI
8. Complete a Full AI/ML Project

Course Curriculum

Module 1: Understanding AI

What is AI, Machine Learning, and Deep Learning
History and evolution of AI
Real-world AI applications

Module 2: AI vs Traditional Programming

Module 3: Fundamentals of Machine Learning

Module 4: Data Preparation

Module 5: Supervised Learning

Module 6: Unsupervised Learning

Module 7: Introduction to Neural Networks

Module 8: Introduction to Deep Learning Frameworks

Module 9: AI for Business & Industry

Module 10: AI Tools for Non-Coders

Module 11: Evaluating ML Models

Module 12: Model Optimization

Module 13 : AI Ethics & Responsible Use

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Level

Intermediate
Total Enrolled

3
Duration

60 hours 36 minutes
Last Updated

January 11, 2026
Certificate

Certificate of completion

PDF notes & lecture slides
Datasets for practice
Python notebooks / no-code AI templates
Video tutorials & step-by-step demos
Sample ML projects and example code
Quizzes, worksheets & evaluation tools
Final project checklist and presentation templates

Laptop or desktop with stable internet
Basic computer skills (typing, browsing, file management)
Python installed (or access to Google Colab for browser-based coding)
Free accounts: Google, Kaggle, GitHub (optional)
Calculator & notebook for practice
Optional: Smartphone for testing AI apps and tools

AI & Machine Learning

About Course

What I will learn?

Course Curriculum

Module 1: Understanding AI

What is AI, Machine Learning, and Deep Learning

History and evolution of AI

Real-world AI applications

Module 2: AI vs Traditional Programming

How AI differs from rule-based systems

AI problem-solving approaches

Module 3: Fundamentals of Machine Learning

Types of ML: Supervised, Unsupervised, Reinforcement Learning

Key concepts: Features, Labels, Training, Testing

Module 4: Data Preparation

Collecting and cleaning datasets

Data preprocessing and normalization

Module 5: Supervised Learning

Regression (Linear, Logistic)

Classification (Decision Trees, KNN, SVM)

Module 6: Unsupervised Learning

Clustering (K-Means, Hierarchical)

Dimensionality Reduction (PCA, t-SNE)

Module 7: Introduction to Neural Networks

Perceptrons, activation functions, forward & backward propagation

Simple neural network architecture

Module 8: Introduction to Deep Learning Frameworks

TensorFlow, Keras, PyTorch overview

Building a simple neural network

Module 9: AI for Business & Industry

AI in finance, healthcare, marketing, and IoT

Using ML for predictions, recommendations, and automation

Module 10: AI Tools for Non-Coders

No-code AI platforms (Runway ML, Lobe, Google Teachable Machine)

Building simple AI models without programming

Module 11: Evaluating ML Models

Accuracy, Precision, Recall, F1 Score

Confusion matrix and cross-validation

Module 12: Model Optimization

Hyperparameter tuning

Avoiding overfitting and underfitting

Module 13 : AI Ethics & Responsible Use

Bias and fairness in AI

Data privacy and security

Limitations of AI and responsible AI use

Student Ratings & Reviews

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