Lesson 1: Introduction to AI and ML
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What is Artificial Intelligence? - Definition, Key Goals, Real-world Applications
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What is Machine Learning? - Definition, Types of ML: Supervised, Unsupervised, Reinforcement, Importance in AI
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Differences Between AI, ML, and Deep Learning - Core Concepts, Use Cases for Each
Lesson 2: Mathematics for AI and ML
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Linear Algebra Basics - Vectors and Matrices, Matrix Multiplication, Eigenvalues and Eigenvectors
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Probability and Statistics - Bayes Theorem, Distributions (Normal, Poisson), Hypothesis Testing
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Calculus for ML - Gradients, Partial Derivatives
Lesson 3: ML Workflow
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Problem Definition - Understanding Business Goals, Framing as a Predictive Problem
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Data Collection and Preparation - Data Sources and APIs, Data Cleaning Techniques, Handling Missing Values
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Model Building and Deployment - Training and Testing, Hyperparameter Tuning, Deploying Models
Lesson 1: Supervised Learning
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Regression Techniques - Linear Regression, Polynomial Regression, Evaluation Metrics (RMSE, R²)
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Classification Techniques - Logistic Regression, Decision Trees, Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Lesson 2: Unsupervised Learning
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Clustering Techniques - K-Means, Hierarchical Clustering, Applications
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Dimensionality Reduction - Principal Component Analysis (PCA), t-SNE
Lesson 3: Ensemble Learning
- Bagging Methods - Random Forest, Bootstrapping
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Boosting Methods - Gradient Boosting, XGBoost, LightGBM
Lesson 1: Introduction to Deep Learning
- What is Deep Learning? - Neural Networks Overview, Key Applications
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Building Blocks of Neural Networks - Perceptrons, Activation Functions, Loss Functions
Lesson 2: Convolutional Neural Networks (CNNs)
- CNN Architecture - Convolution Layers, Pooling Layers, Fully Connected Layers
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Applications of CNNs - Image Classification, Object Detection
Lesson 3: Natural Language Processing (NLP)
- NLP Basics - Tokenization and Preprocessing, Word Embeddings (Word2Vec, GloVe)
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Advanced NLP Techniques - Transformers, BERT and GPT Models
Lesson 4: Reinforcement Learning (RL)
- RL Basics - Agents and Environments, Rewards and Policies
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Popular RL Algorithms - Q-Learning, Deep Q-Networks (DQN)
Lesson 1: Model Deployment
- Deployment Techniques - Flask/Django for Web Apps, Cloud Services (AWS, GCP, Azure)
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Monitoring and Scaling - Performance Metrics, Handling High Traffic
Lesson 2: Ethics and Bias in AI
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Ethical Considerations - Bias in AI, Transparency and Explainability
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Best Practices - Ensuring Fairness, Accountability
- CategoryData Science
- LevelExpert
- Duration3 Months
- Available SeatsUnlimited
Course Key Highlights
Real-Time Experts
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