Month 1: Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
Week 1-2: Overview of AI and ML
Understanding the basics of artificial intelligence
Introduction to machine learning and its applications
Historical development and key milestones
Week 3-4: Types of Machine Learning
Supervised, unsupervised, and reinforcement learning
Understanding classification, regression, and clustering
Real-world examples and use cases
Week 5-6: Python Programming for AI/ML
Basics of Python programming language
Libraries for AI/ML (NumPy, Pandas)
Setting up the development environment
Month 2: Data Preprocessing and Exploratory Data Analysis (EDA)
Week 1-2: Data Collection and Cleaning
Gathering data for machine learning projects
Handling missing data and outliers
Data cleaning techniques
Week 3-4: Data Transformation and Feature Engineering
Scaling and normalizing data
Creating new features
Handling categorical data
Week 5-6: Exploratory Data Analysis (EDA)
Visualizing and understanding data distributions
Correlation analysis
Extracting insights from data
Month 3: Supervised Learning Algorithms
Week 1-2: Linear Regression
Basics of regression analysis
Implementing linear regression in Python
Evaluation metrics
Week 3-4: Classification Algorithms
Logistic Regression
Decision Trees and Random Forest
Support Vector Machines (SVM)
Week 5-6: Model Evaluation and Hyperparameter Tuning
Cross-validation and model performance
Hyperparameter tuning techniques
Best practices in model evaluation
Month 4: Unsupervised Learning Algorithms
Week 1-2: Clustering Algorithms
K-Means Clustering
Hierarchical Clustering
Evaluating clustering performance
Week 3-4: Dimensionality Reduction
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Reducing feature space for better modeling
Week 5-6: Case Studies and Practical Applications
Applying unsupervised learning to real-world scenarios
Discussion on successful use cases
Group projects and case study presentations
Month 5: Introduction to Neural Networks and Deep Learning
Week 1-2: Basics of Neural Networks
Understanding artificial neurons
Feedforward and backpropagation
Activation functions
Week 3-4: Introduction to Deep Learning
Building and training deep neural networks
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Week 5-6: Deep Learning Applications
Image and speech recognition
Natural Language Processing (NLP)
Practical implementation projects
Month 6: Final Projects and Certification
Week 1-2: Final Project Briefing
Students receive final project instructions
Choosing a real-world application of AI/ML
Week 3-4: Project Execution
Students work on their final projects
Instructor guidance and feedback
Week 5-6: Project Presentation and Certification
Students present their final projects
Certification awarded upon successful completion
Career guidance and next steps in AI and ML
Course Fees: PKR 5000 (Admission) + PKR 3000 per month