Artificial Intelligence

  • English

Course Description

This course provides an in-depth introduction to Artificial Intelligence (AI), covering its core concepts, algorithms, and applications. It is designed for beginners and intermediate learners looking to understand how AI works and how it is transforming various industries. The course emphasizes practical AI techniques, including machine learning, neural networks, and natural language processing.

Module 1: Introduction to Artificial Intelligence

  1. What is Artificial Intelligence?
    1. Definition and scope
    2. History and evolution of AI
    3. Key milestones in AI development
  2. Types of AI
    1. Narrow AI vs. General AI vs. Superintelligent AI
    2. Reactive Machines, Limited Memory, Theory of Mind, Self-aware AI
  3. AI Applications
    1. Industry use cases (healthcare, finance, automotive, etc.)
    2. Everyday AI applications (virtual assistants, recommendation systems)
  4. AI vs. Machine Learning vs. Deep Learning
    1. Understanding the relationships and distinctions

 

Module 2: Foundations of Machine Learning

  1. Introduction to Machine Learning
    1. Definition and importance
    2. Types of machine learning: Supervised, Unsupervised, Reinforcement Learning
  2. Data Preprocessing
    1. Data collection and cleaning
    2. Feature selection and engineering
    3. Handling missing data and outliers
  3. Evaluation Metrics
    1. Accuracy, Precision, Recall, F1-Score
    2. Confusion Matrix
    3. ROC-AUC and other performance metrics
  4. Model Selection and Validation
    1. Cross-validation techniques
    2. Bias-Variance tradeoff
    3. Hyperparameter tuning

 

Module 3: Supervised Learning Algorithms

  1. Linear Models
    1. Linear Regression
    2. Logistic Regression
  2. Decision Trees and Ensemble Methods
    1. Decision Trees
    2. Random Forests
    3. Gradient Boosting Machines (GBM), XGBoost, LightGBM
  3. Support Vector Machines (SVM)
    1. Fundamentals of SVM
    2. Kernel tricks
  4. k-Nearest Neighbors (k-NN)
    1. Algorithm mechanics
    2. Choosing the right k

 

Module 4: Unsupervised Learning Algorithms

  1. Clustering Techniques
    1. K-Means Clustering
    2. Hierarchical Clustering
    3. DBSCAN
  2. Dimensionality Reduction
    1. Principal Component Analysis (PCA)
    2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
    3. Uniform Manifold Approximation and Projection (UMAP)
  3. Association Rule Learning
    1. Apriori Algorithm
    2. Eclat Algorithm

 

Module 5: Deep Learning Fundamentals

  1. Introduction to Neural Networks
    1. Perceptron and Multi-layer Perceptrons (MLP)
    2. Activation functions
  2. Training Neural Networks
    1. Backpropagation and gradient descent
    2. Optimization algorithms (SGD, Adam, RMSprop)
    3. Regularization techniques (Dropout, L2 regularization)
  3. Convolutional Neural Networks (CNNs)
    1. Architecture and components
    2. Applications in computer vision
  4. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
    1. Sequence modeling
    2. Applications in NLP and time-series data

 

Module 6: Natural Language Processing (NLP)

  1. Introduction to NLP
    1. Importance and applications
    2. Text preprocessing techniques
  2. Text Representation
    1. Bag of Words, TF-IDF
    2. Word Embeddings (Word2Vec, GloVe, FastText)
    3. Contextual Embeddings (BERT, GPT)
  3. NLP Tasks and Models
    1. Text classification, Sentiment Analysis
    2. Named Entity Recognition (NER)
    3. Machine Translation
  4. Advanced NLP Techniques
    1. Transformer architecture
    2. Attention mechanisms
    3. Generative models for text

 

Module 7: Computer Vision

  1. Introduction to Computer Vision
    1. Importance and applications
    2. Image processing basics
  2. Image Classification and Object Detection
    1. CNN architectures for classification
    2. YOLO, SSD, Faster R-CNN for object detection
  3. Image Segmentation
    1. Semantic vs. Instance segmentation
    2. U-Net, Mask R-CNN
  4. Advanced Topics in Computer Vision
    1. Generative Adversarial Networks (GANs)
    2. Image generation and style transfer

 

Module 8: Reinforcement Learning

  1. Fundamentals of Reinforcement Learning (RL)
    1. Agents, environments, rewards
    2. Exploration vs. Exploitation
  2. Key Concepts in RL
    1. Markov Decision Processes (MDP)
    2. Policy, Value function, Q-learning
  3. Advanced RL Algorithms
    1. Deep Q-Networks (DQN)
    2. Policy Gradient methods
    3. Actor-Critic models
  4. Applications of RL
    1. Game playing (e.g., AlphaGo)
    2. Robotics and autonomous systems

 

Module 9: AI Tools and Frameworks

  1. Programming Languages for AI
    1. Python essentials
    2. R for statistical analysis
  2. Machine Learning Libraries
    1. Scikit-learn
    2. TensorFlow and Keras
    3. PyTorch
  3. Data Handling and Visualization Tools
    1. Pandas, NumPy
    2. Matplotlib, Seaborn
  4. AI Development Platforms
    1. Jupyter Notebooks
    2. Google Colab
    3. Integrated Development Environments (IDEs)

 

Module 10: AI in Production

  1. Model Deployment
    1. Serving models with Flask, FastAPI
    2. Using cloud services (AWS SageMaker, Google AI Platform)
  2. Scaling AI Solutions
    1. Containerization with Docker
    2. Orchestration with Kubernetes
  3. Monitoring and Maintenance
    1. Model performance tracking
    2. Retraining strategies
  4. MLOps Practices
    1. CI/CD for machine learning
    2. Automation and reproducibility
    3.  

Module 11: Emerging Trends and Advanced Topics in AI

  1. Explainable AI (XAI)
    1. Importance of explainability
    2. Techniques for interpretable models
  2. Federated Learning
    1. Concepts and applications
    2. Privacy-preserving machine learning
  3. Quantum Machine Learning
    1. Introduction to quantum computing
    2. Potential of Quantum Algorithms in AI
  4. AI for Social Good
    1. Applications in healthcare, environment, education
    2. Case studies and projects

 

Er. Mayank Mishra
Er. Mayank Mishra
Full Stack Developer & Trainer

Course Includes:

  • Mode: Offline\Online
  • Language: English\Hindi
  • Certificate: Yes
  • Project Certificate: Yes

Get In Touch

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3 Months:

  • Price: Rs. 10000
  • Duration: 3 Months

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