AI & Machine Learning: From Basics to Advanced- Workshop
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- Module 1
-
Introduction to AI & ML
- Understanding AI & ML
- History and Evolution
- AI & ML Applications in Various Industries
- Future Trends in AI & ML
- Module 2
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Fundamentals of Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Key Concepts: Features, Labels, Training, Testing, and Validation
- Data Preprocessing Techniques
- Introduction to Python for AI/ML
- Module 3
-
Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Model Evaluation and Performance Metrics
- Module 4
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Unsupervised Learning
- Clustering Algorithms: K-Means, Hierarchical Clustering
- Dimensionality Reduction Techniques: PCA, t-SNE
- Anomaly Detection
- Module 5
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Neural Networks and Deep Learning
- Introduction to Neural Networks
- Deep Learning Basics
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Introduction to TensorFlow and Keras
- Module 6
-
Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Language Models
- NLP with NLTK and SpaCy
- Module 7
-
Reinforcement Learning
- Understanding Reinforcement Learning
- Markov Decision Processes
- Q-Learning
- Deep Q-Learning
- Module 8
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AI Ethics and Security
- Ethical Considerations in AI
- AI Security and Privacy
- Bias and Fairness in AI
- Module 9
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Capstone Project
- Real-World AI/ML Project
- End-to-End Implementation
- Presentation and Evaluation