AI and ML – 2 Year Diploma

0 student Language: English Level: All levels 0 lesson Duration: Lifetime access Location: Mandikatar, Miteripul

Description

This advanced 2-year diploma in AI and ML is designed for students who want to specialize in artificial intelligence and become industry-ready professionals. The program covers AI ethics, automation, big data analytics, and real-world AI applications across industries.

Course Content

2-Year AI and ML Diploma (100 Weeks)

Total Duration: 100 weeks (2 hours/day, 6 days a week)

Week 1–2: Introduction to Programming with Python (12 sessions)

Basics of programming: Variables, data types, and operators
Conditional statements and loops (if-else, for, while)
Functions: Definition, arguments, and return values
Introduction to Python IDEs and Jupyter Notebooks
Hands-on: Simple programs like calculators, and condition-based programs

Week 3: Python Data Structures (6 sessions)

Lists, tuples, and dictionaries: Creation, manipulation, and iteration
Strings: Common operations and slicing
Sets: Use cases and applications
Hands-on: Build small projects like contact books or inventory systems

Week 4: Python Libraries and File Handling (6 sessions)

Introduction to Python libraries (NumPy, Pandas, and Matplotlib)
File handling: Reading/writing text and CSV files
Hands-on: Process and visualize simple datasets

Weeks 5–6: Exploratory Data Analysis (12 sessions)

Pandas: Data manipulation, cleaning, and aggregation
Matplotlib & Seaborn: Data visualization techniques

Hands-On: Analyze datasets (e.g., sales or weather data)

Weeks 7–8: Mathematics for AI and ML (12 sessions)

Linear Algebra: Vectors, matrices, and transformations
Probability: Distributions, Bayes’ theorem, and expectations
Calculus: Derivatives and optimization basics
Hands-On: Solve mathematical problems using Python

Weeks 9–10: Introduction to Machine Learning (18 sessions)

ML Basics: Supervised vs. unsupervised learning
Regression: Linear regression (theory and implementation)
Classification: Logistic regression, decision trees, and random forests
Unsupervised: Clustering (K-Means), Dimensionality Reduction

Hands-On: Predict house prices, classify spam emails

Week 11: Feature Engineering and Preprocessing (6 sessions)

Handling missing data
Feature scaling and normalization
Encoding categorical variables
Hands-on: Prepare a dataset for ML models

Week 12: Basics of Reinforcement Learning and Deep Learning (6 sessions)

Reinforcement Learning Overview

Neural Networks: Basic structure and functioning

Introduction to TensorFlow/Keras

Weeks 13: Revision Exercises (6 sessions)

Data Structures
Statements and Loops
Functions
NumPy and Pandas
Supervised and Unsupervised ML

Weeks 13–14: Final Projects (6 sessions)

Project: Exploratory Data Analysis of any tabular data
Project: Develop an end-to-end ML solution (e.g., sales prediction)

Weeks 15–16: Advanced Supervised Learning (12 sessions)

Regularization: Ridge and Lasso regression
Advanced Classification: SVM, ensemble methods (Random Forest, Gradient Boosting), k-NN, Naïve Bayes, Gradient Boosting
Hands-On: Build robust supervised models

Weeks 17–18: Unsupervised Learning and Clustering (12 sessions)

Clustering: Centroid-based, Distribution-based, Density-based
Dimensionality Reduction: PCA, t-SNE, ICA

Hands-On: Customer segmentation, image compression

Weeks 19–20: Neural Networks and Optimization (12 sessions)

Architecture Basics: Layers, activation functions, loss functions
Optimization: Gradient descent, Adam optimizer
CNNs and RNNs

Hands-On: Train neural networks for tabular data classification

Weeks 21: Reinforcement Learning (6 sessions)

Reinforcement Learning Examples
Model-Based Methods vs Model-Free Methods

Weeks 22: SQL and Data Handling (6 sessions)

SQL Basics: Queries, joins, and integrating SQL with Python
Data Handling: Advanced cleaning, encoding, and scaling techniques

Hands-On: Use SQL for data analysis tasks

Weeks 23: Revision Exercises (6 sessions)

Basic Python Programming
NumPy and Pandas
Supervised and Unsupervised ML
SQL
Neural networks

Weeks 24–25: Capstone Project (12 sessions)

Real-World Problem: Collaborative projects integrating ML pipelines

Deliverables: Presentation, peer feedback

Weeks 26–28: R Programming and Hybrid Techniques (18 sessions)

Introduction to R: Data analysis and visualization basics
Combining Tools: Python and R in the same pipeline

Hands-On: Hybrid projects

Weeks 29–32: Advanced Machine Learning Techniques (24 sessions)

Ensemble Models: XGBoost, LightGBM
Time Series Analysis: ARIMA, LSTMs
AutoML Tools: H2O, Auto-sklearn
Hands-On: Build models for forecasting and anomaly detection

Weeks 33-34: Reinforcement Learning (12 sessions)

Model-Based Methods
Model-Free Methods (Value-Based, Policy-Based)

Weeks 35-36: Recommender Systems (12 sessions)

Techniques: Collaborative Filtering, Content-Based Filtering

Hands-On: Build a recommendation engine

Weeks 37–39: Computer Vision (18 sessions)

Image Processing: Preprocessing and augmentation
CNN Architectures: ResNet, EfficientNet
Applications: Object detection, segmentation
Hands-On: Train image classifiers

Weeks 40–42: Natural Language Processing (NLP) (18 sessions)

Text Processing: Tokenization, embeddings (Word2Vec, BERT)
Applications: Sentiment analysis, text summarization
Hands-On: Build chatbots and text classifiers

Weeks 43–44: Generative Models and Advanced AI Topics (12 sessions)

GANs: Generative Adversarial Networks
LLMs: Large Language Models, including fine-tuning

Hands-On: Build and experiment with GANs/LLMs

Weeks 45: AI Ethics (6 sessions)

Ethics: Bias, transparency, fairness

Weeks 46-47: Deployment (12 sessions)

Deployment: Flask/Django, cloud hosting
Hands-On: Deploy ML models on AWS/GCP

Weeks 48–50: Capstone Project and Presentation (18 sessions)

Comprehensive AI system development (healthcare, finance, or other domains)
Project milestones, reviews, and presentations

Bonus

Gen AI Foundations & Prompt Engineering Techniques
Creative Writing & Idea Generation using Gen AI
Visual Storytelling with Gen AI
Marketing Innovation with Gen AI
Dynamic Design & Prototyping with Gen AI
Video Content Creation with Gen AI
Audio Innovation with Gen AI
3D Model Creation
Responsible Usage & Ethical Consideration in AI
Project

Weeks 51–55: AI in Cybersecurity (30 sessions)

Cybersecurity Fundamentals for AI Practitioners:
- Introduction to cybersecurity threats: Phishing, malware, DDoS attacks
- Role of AI in modern threat detection and response systems
- Overview of cybersecurity tools and frameworks integrated with AI
AI-Driven Security Tools:
- Intrusion detection systems (IDS) and intrusion prevention systems (IPS) using ML
- Fraud detection systems for real-time financial and e-commerce applications
- Behavioral anomaly detection using clustering and unsupervised learning
Adversarial AI:
- Understanding adversarial attacks: Evasion, poisoning, model inversion
- Building resilient models: Adversarial training and robust optimization techniques
Hands-On:
- Develop an AI-based fraud detection system
- Simulate adversarial attacks and implement defense strategies

Weeks 56–60: Web Penetration Testing with AI (30 sessions)

Basics of Penetration Testing and Ethical Hacking:
- Overview of OWASP Top 10 vulnerabilities
- Tools and techniques: Burp Suite, Metasploit, Wireshark
AI in Web Penetration Testing:
- AI-powered vulnerability scanners: Automation of reconnaissance and testing
- Using AI for log analysis to detect anomalies and suspicious activities
- Identifying SQL injection, cross-site scripting (XSS), and authentication flaws
Threat Intelligence Using AI:
- Machine learning models for predicting potential vulnerabilities
- Building recommendation systems for secure coding practices
Hands-On:
- Create an AI-powered vulnerability scanner
- Automate security log analysis and vulnerability reporting

Weeks 61–65: Privacy and Ethics in AI (30 sessions)

Privacy-Preserving AI Techniques:
- Introduction to federated learning: Decentralized ML approaches for privacy
- Implementing differential privacy in ML workflows
Ethics and Bias in AI:
- Detecting and mitigating bias in AI models
- Case studies: Ethical lapses in AI and corrective strategies
Regulations and Standards:
- Understanding global AI regulatory frameworks (e.g., GDPR, CCPA)
- Building ethical and compliant AI systems
Hands-On:
- Implement a federated learning pipeline for sensitive data
- Build a bias detection and mitigation system for a sample dataset

Weeks 66–70: Advanced Generative AI (30 sessions)

Advanced Generative Architectures:
- StyleGAN, CycleGAN, and their applications in various industries
- Diffusion models for realistic image generation and restoration
Applications of Generative AI:
- AI in content creation: Generating music, videos, and artwork
- Text-to-image and image-to-text pipelines using AI
Hands-On:
- Create a generative art tool using StyleGAN
- Build a text-to-image pipeline with pre-trained models

Weeks 71–75: AI for Real-Time Applications (30 sessions)

AI for IoT and Edge Computing:
- Optimizing ML models for resource-constrained devices (e.g., Raspberry Pi)
- Predictive maintenance and real-time analytics in IoT systems
Streaming Data and Real-Time Processing:
- Using Kafka and Spark Streaming for real-time AI pipelines
- Implementing real-time fraud detection systems
Hands-On:
- Deploy lightweight AI models on IoT devices
- Build a real-time predictive analytics system

Weeks 81–85: AI for Specialized Domains (30 sessions)

AI in Healthcare:
- Disease prediction models and medical image analysis
- Personalized medicine: Recommender systems for treatment plans
AI in Finance:
- Risk assessment using ensemble models
- Fraud detection in real-time transactions
AI in Sustainability and Energy:
- Predictive analytics for energy optimization
- AI solutions for waste management and climate change applications
Hands-On:
- Develop a diagnostic tool for healthcare data
- Create a trading algorithm or sustainability monitoring system

Weeks 86–90: Advanced Deployment and MLOps (30 sessions)

Deployment Techniques:
- Advanced Docker and Kubernetes for deploying scalable AI systems
- Building CI/CD pipelines tailored for AI models
MLOps in Practice:
- Model versioning and monitoring for drift detection
- Real-world deployment using AWS SageMaker, Azure ML, and GCP tools
Hands-On:
- Create a secure, scalable MLOps pipeline for an AI project
- Deploy and monitor an AI model in a cloud environment

Weeks 91–95: Multi-Agent Systems and Collaborative AI (30 sessions)

Introduction to Multi-Agent Systems:
- Fundamentals of decentralized AI and agent-based modeling
- Applications: Swarm robotics, autonomous vehicles, and smart grids
Collaborative AI:
- Building AI systems for multi-user environments (e.g., virtual assistants)
- Reinforcement learning for multi-agent systems
Hands-On:
- Develop a multi-agent simulation for a collaborative task
- Implement reinforcement learning in a multi-agent setting

Weeks 96–100: Capstone Projects and Industry Collaboration (30 sessions)

Capstone Projects:
- Real-world projects in domains like cybersecurity, IoT, healthcare, or finance
- Focus on building fully deployable AI systems
Deliverables:
- AI system design and architecture
- Functional codebase with deployment-ready pipelines
- Project documentation and final presentation
Showcase:
- Present projects to a panel of industry experts for feedback and evaluation