Artificial Intelligence (AI) provides advanced technologies that enable machines to mimic human intelligence. AI encompasses machine learning, natural language processing, computer vision, and robotics. It automates tasks, enhances decision-making, and drives innovation across industries. AI systems are built on large datasets and algorithms, offering security and scalability for various applications globally.

• A fundamental understanding of algorithms, mathematics (especially linear algebra and statistics), and data structures is helpful. 
• Familiarity with basic computer operations and an interest in data analysis and problem-solving will enhance learning in Artificial Intelligence (AI).

Module 1: Introduction to Artificial Intelligence

• What is AI?
  • Definition and history of Artificial Intelligence.
  • AI vs. Machine Learning (ML) vs. Deep Learning (DL).
  • Applications of AI in various industries (Healthcare, Finance, Retail, etc.).
• Types of AI
  • Narrow AI, General AI, and Superintelligent AI.
  • Reactive Machines, Limited Memory, Theory of Mind, and Self-Aware AI.
• Key AI Terminologies
  • Agents, environments, rewards, actions, and policies.
  • Key concepts: Algorithms, neural networks, and heuristics.

Module 2: Mathematics for AI

• Linear Algebra
  • Vectors, matrices, and tensor operations.
  • Eigenvalues, eigenvectors, and matrix factorization.
• Probability and Statistics
  • Basics of probability (Bayes’ theorem, distributions).
  • Statistical measures (mean, variance, standard deviation).
  • Markov models and Hidden Markov Models.
• Calculus
  • Derivatives, partial derivatives, and gradients.
  • Optimization techniques (gradient descent and its variants).
• Discrete Mathematics
  • Graph theory and logic relevant to AI algorithms.

Module 3: Machine Learning Fundamentals

• Introduction to Machine Learning
  • Definition and types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
  • Key ML algorithms and when to use them.
• Supervised Learning
  • Linear regression, logistic regression.
  • Decision trees, random forests, and support vector machines (SVM).
• Unsupervised Learning
  • K-means clustering, hierarchical clustering.
  • Principal Component Analysis (PCA) and dimensionality reduction.
• Reinforcement Learning
  • Concepts of rewards and policies.
  • Q-learning and Deep Q Networks.

Module 4: Deep Learning

• Introduction to Neural Networks
  • Structure of an artificial neuron.
  • Activation functions and forward/backward propagation.
• Building Neural Networks
  • Types of neural networks: Feedforward, Recurrent (RNN), and Convolutional (CNN).
  • Frameworks: TensorFlow and PyTorch basics.
• Deep Learning Architectures
  • CNN for image processing.
  • RNN for sequence data and natural language processing (NLP).
  • Transformers and Attention Mechanisms.
• Advanced Topics
  • Generative Adversarial Networks (GANs).
  • Transfer learning and pre-trained models.

Module 5: Natural Language Processing (NLP)

• Basics of NLP
  • Tokenization, stemming, and lemmatization.
  • Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF).
• Advanced NLP Techniques
  • Word embeddings (Word2Vec, GloVe).
  • Sequence-to-sequence models.
  • Transformers and BERT (Bidirectional Encoder Representations from Transformers).
• Applications of NLP
  • Sentiment analysis, chatbots, and machine translation.
  • Speech recognition and text summarization.

Module 6: Computer Vision

• Basics of Computer Vision
  • Image processing techniques: Filtering, edge detection, and histograms.
  • Feature extraction: SIFT, SURF, and ORB.
• Deep Learning for Vision
  • Convolutional Neural Networks (CNN) architecture.
  • Object detection models: YOLO, SSD, and Faster R-CNN.
• Applications of Computer Vision
  • Image classification, object detection, and facial recognition.
  • Autonomous vehicles and medical imaging.

Module 7: AI Tools and Frameworks

• Programming Languages
  • Python for AI: Libraries and tools (NumPy, pandas, scikit-learn, etc.).
  • R for statistical analysis.
• AI Frameworks
  • TensorFlow, PyTorch, Keras.
  • OpenCV for computer vision tasks.
• Cloud AI Platforms
  • Google AI, AWS AI, and Microsoft Azure AI services.

Module 8: Ethical and Responsible AI

• AI Ethics
  • Bias in AI and its mitigation.
  • Fairness, accountability, and transparency in AI systems.
• Data Privacy
  • GDPR and other data privacy regulations.
  • Handling sensitive and personal data responsibly.
• AI in Society
  • The impact of AI on jobs and industries.
  • Ensuring inclusivity and addressing societal concerns.

Module 9: Real-World AI Applications

• AI in Healthcare
  • Diagnosis, drug discovery, and personalized medicine.
  • AI in medical imaging and robotics.
• AI in Finance
  • Fraud detection, algorithmic trading, and risk assessment.
• AI in Retail
  • Recommendation systems and inventory management.
  • Chatbots and customer service automation.
• AI in Autonomous Systems
  • Self-driving cars and drones.
  • AI in manufacturing and robotics.

Module 10: AI Project Development

• Problem Identification
  • Defining the problem statement.
  • Data collection and preprocessing.
• Model Development
  • Choosing the right algorithms and architectures.
  • Training and validating models.
• Deployment
  • Deploying AI models to production.
  • Monitoring and maintaining AI systems.
• Case Studies
  • Analyzing successful AI implementations.

Module 11: Capstone Project

• End-to-End AI Project
  • Identify a real-world problem and create an AI-based solution.
  • Include data collection, preprocessing, model building, and deployment.
  • Create a presentation showcasing the solution and results.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support