Artificial Intelligences

Course Rate: INR 70000.00

Duration: 2 Months

About the Course:

Artificial Intelligence (AI)** covering beginner to advanced topics:

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## **1. Introduction to Artificial Intelligence**
- Definition of AI
- History and Evolution of AI
- Types of AI:
- Narrow AI vs. General AI vs. Super AI
- Reactive Machines, Limited Memory, Theory of Mind, and Self-Aware AI
- AI Applications in Different Industries
- Ethical Considerations in AI

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## **2. Mathematics for AI**
- Linear Algebra:
- Vectors, Matrices, and Tensors
- Eigenvalues and Eigenvectors
- Probability and Statistics:
- Probability Distributions
- Bayes’ Theorem
- Hypothesis Testing
- Calculus for AI:
- Differentiation and Integration
- Partial Derivatives and Gradient Descent
- Optimization Techniques:
- Convex Optimization
- Lagrange Multipliers

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## **3. Machine Learning Fundamentals**
- Introduction to Machine Learning (ML)
- Types of Machine Learning:
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Reinforcement Learning
- Evaluation Metrics:
- Accuracy, Precision, Recall, F1 Score
- Confusion Matrix
- Model Selection & Validation:
- Cross-Validation
- Bias-Variance Tradeoff
- Overfitting and Underfitting

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## **4. Supervised Learning Algorithms**
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Naïve Bayes Classifier
- K-Nearest Neighbors (KNN)
- Ensemble Methods (Bagging, Boosting, Stacking)

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## **5. Unsupervised Learning Algorithms**
- Clustering Techniques:
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimensionality Reduction:
- Principal Component Analysis (PCA)
- t-SNE
- Autoencoders
- Anomaly Detection Techniques

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## **6. Deep Learning and Neural Networks**
- Introduction to Neural Networks
- Perceptron and Multi-Layer Perceptron (MLP)
- Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
- Backpropagation and Gradient Descent
- Optimizers (SGD, Adam, RMSprop)
- Loss Functions (MSE, Cross-Entropy)

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## **7. Convolutional Neural Networks (CNNs)**
- Convolution and Pooling Operations
- Architectures of CNNs (LeNet, AlexNet, VGG, ResNet)
- Object Detection (YOLO, Faster R-CNN)
- Image Classification and Segmentation
- Transfer Learning with Pre-trained Models

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## **8. Recurrent Neural Networks (RNNs) and Sequence Models**
- Introduction to RNNs
- Vanishing and Exploding Gradient Problem
- Long Short-Term Memory (LSTM) Networks
- Gated Recurrent Units (GRUs)
- Sequence-to-Sequence Models
- Applications in Time Series and Natural Language Processing (NLP)

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## **9. Natural Language Processing (NLP)**
- Text Preprocessing (Tokenization, Stemming, Lemmatization)
- Bag of Words (BoW) and TF-IDF
- Word Embeddings (Word2Vec, GloVe, FastText)
- Transformers (BERT, GPT, T5)
- Named Entity Recognition (NER)
- Sentiment Analysis and Chatbots

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## **10. Reinforcement Learning**
- Introduction to Reinforcement Learning (RL)
- Markov Decision Processes (MDPs)
- Q-Learning and Deep Q-Networks (DQN)
- Policy Gradient Methods
- OpenAI Gym and Applications of RL

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## **11. AI in Computer Vision**
- Image Recognition and Classification
- Object Detection and Tracking
- Facial Recognition Systems
- Pose Estimation
- GANs (Generative Adversarial Networks)

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## **12. AI in Robotics and Autonomous Systems**
- Basics of Robotics
- Path Planning Algorithms
- Robot Perception and Control
- AI in Self-Driving Cars (Tesla, Waymo)
- AI in Drones and Industrial Automation

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## **13. AI in Business and Industry**
- AI for Healthcare (Disease Prediction, Medical Imaging)
- AI in Finance (Fraud Detection, Algorithmic Trading)
- AI in Marketing and Recommendation Systems (Netflix, Amazon)
- AI in Manufacturing and Supply Chain Optimization
- AI in Cybersecurity and Threat Detection

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## **14. AI Model Deployment and MLOps**
- Deploying AI Models using Flask and FastAPI
- Model Optimization and Quantization
- AI on Edge Devices (TensorFlow Lite, ONNX)
- MLOps:
- CI/CD for Machine Learning
- Model Monitoring and Retraining
- AutoML and Hyperparameter Tuning

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## **15. Ethics and Future of AI**
- AI Bias and Fairness
- Explainable AI (XAI)
- AI Regulations and Compliance (GDPR, CCPA)
- AI and Job Market Transformation
- Future Trends in AI (AGI, AI for Sustainability)

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## **16. Real-World AI Projects**
- AI-Powered Chatbot
- Image Recognition System
- Sentiment Analysis for Social Media
- Fraud Detection Model for Banking
- Self-Driving Car Simulation
- AI-based Virtual Assistant