Best Ai Course In Delhi NCR
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Introduction to Artificial Intelligence
Overview of AI and its History
Types of AI: Narrow AI vs. General AI vs. Superintelligence
AI in the Modern World and Industry Applications
Ethical Issues in AI and its Impact on Society
2. Fundamentals of Machine Learning
Introduction to Machine Learning (ML)
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Concepts: Training, Testing, Overfitting, Bias-Variance Tradeoff
Common Algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors (KNN), etc.
3. Data Preprocessing and Feature Engineering
Data Collection and Preparation
Data Cleaning: Handling Missing Data and Outliers
Feature Engineering and Selection
Scaling, Normalization, and Encoding Categorical Data
4. Supervised Learning
Overview of Supervised Learning
Regression Algorithms: Linear and Polynomial Regression
Classification Algorithms: Logistic Regression, Support Vector Machines (SVM), k-NN, Decision Trees, Random Forests Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC Curve
5. Unsupervised Learning
Overview of Unsupervised Learning
Clustering Algorithms: K-Means, DBSCAN, Hierarchical Clustering
Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE
Anomaly Detection and Applications6. Neural Networks and Deep Learning
Introduction to NLP and Text Preprocessing
Text Representation: Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
Sentiment Analysis and Text Classification
Named Entity Recognition (NER), Part-of-Speech Tagging
Advanced NLP Models: Transformer, BERT, GPT8. Reinforcement Learning
- Introduction to Reinforcement Learning (RL)
Key Concepts: Agent, Environment, Reward, Policy
Q-Learning and Deep Q Networks (DQN)
Markov Decision Processes (MDP)
Applications of RL (Robotics, Games, Autonomous Vehicles) - 9. Computer Vision
Overview of Computer Vision
Image Preprocessing and Augmentation
Object Detection and Recognition
Convolutional Neural Networks (CNNs) in Vision
Transfer Learning and Pre-trained Models (e.g., VGG, ResNet, YOLO)
10. AI Ethics and Safety
Ethical Considerations in AI Development
Bias and Fairness in AI Models
AI in Decision-Making and Accountability
AI Regulation and Privacy Concerns (GDPR, Data Privacy)
AI Safety and Control
11. AI Tools and Frameworks
Introduction to Popular AI Tools and Libraries
Machine Learning Libraries: Scikit-learn, XGBoost, LightGBM
Deep Learning Frameworks: TensorFlow, PyTorch, Keras
Cloud-Based AI Services: Google AI, Azure AI, AWS SageMaker
12. Model Deployment and Optimization
Introduction to Model Deployment
Deploying AI Models in Production
Model Optimization: Hyperparameter Tuning, Grid Search, Random Search
Monitoring and Maintaining AI Systems in Production
13. Advanced Topics in AI
Generative Adversarial Networks (GANs)
Transfer Learning and Few-Shot Learning
Meta-Learning and Self-Supervised Learning
Explainable AI (XAI)
14. AI in Industry Applications
AI in Healthcare: Diagnostics, Personalized Medicine
AI in Finance: Fraud Detection, Algorithmic Trading
AI in Autonomous Vehicles
AI in Robotics and Automation
AI for Predictive Analytics and Business Intelligence
15. Case Studies and Projects
Real-World AI Case Studies and Industry Applications
Hands-on AI Projects (Image Classification, Chatbots, Predictive Models)
End-to-End AI Project: Data Collection, Model Building, Evaluation, and Deployment