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Introduction to AI & ML
- What is AI?
- What is Machine Learning?
- History and Evolution of AI & ML
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Mathematics for AI & ML
- Linear Algebra
- Calculus
- Probability and Statistics
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Python for AI & ML
- Python Basics
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
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Data Preprocessing
- Data Cleaning
- Data Transformation
- Feature Engineering
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Supervised Learning
- Regression Algorithms
- Classification Algorithms
- Evaluation Metrics
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Unsupervised Learning
- Clustering Algorithms
- Dimensionality Reduction
- Anomaly Detection
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Neural Networks and Deep Learning
- Introduction to Neural Networks
- Deep Learning Frameworks: TensorFlow, Keras, PyTorch
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
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Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Language Models
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Reinforcement Learning
- Markov Decision Processes
- Q-Learning
- Deep Q-Networks
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Model Deployment and Production
- Model Serving
- APIs for ML Models
- Monitoring and Maintenance
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Ethics and Bias in AI
- Understanding Bias
- Ethical AI Practices
- Fairness in AI
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Capstone Projects
- Real-world Applications
- End-to-End ML Projects
- Research and Innovation
Happy Learning!