Master Artificial Intelligence AI

What is AI?

AI is the ability of machines to perform tasks that require human intelligence, like learning, reasoning, and problem-solving.

How AI works

AI uses data, algorithms, and computing power to make predictions and automate tasks.

AI in everyday life

Virtual assistants (Open AI, Deepseek), recommendation systems (Netflix, YouTube), and chatbots.

Application Overview: Understanding the significance and types of applications.

Web Application Fundamentals: Key components and basic concepts of web applications.

Web Technologies: Essential technologies and frameworks used in web application development.

Software Development Life Cycle (SDLC): Phases and methodologies for effective software development.

Agile & Scrum: Principles, frameworks, and best practices for managing projects iteratively.

What is data? Information collected from different sources (text, images, videos, numbers).

Types of Data:

Structured Data: Organized in tables (databases, spreadsheets).

Unstructured Data: Freeform content (videos, images, text, emails).

Semi-structured Data: A mix of both (JSON, XML).

Data Storage: Databases (SQL, NoSQL), cloud storage (AWS, Google Drive).

Data Analysis : Finding useful information in data to help make better decisions.

Data Engineering: Building systems and processes to collect, store, and process large datasets using tools like Hadoop and Spark.

Processing Power for AI: AI needs strong computing resources to process large amounts of data.

Key Technologies:

CPU (Central Processing Unit): General-purpose processor, used for basic AI tasks.

GPU (Graphics Processing Unit): Specialized for AI training and deep learning.

TPU (Tensor Processing Unit): Optimized for AI workloads (used by Google).

Edge Computing: Running AI on devices instead of the cloud (e.g., AI in mobile phones).

Cloud AI Platforms: Google Cloud AI, AWS AI, Microsoft Azure AI.

AI aids code generation, bug fixing, and testing.

Example: GitHub Copilot.

AI in self-driving cars, robotics, and drones.

Example: Tesla autopilot.

AI diagnoses discovers drugs, and aids research.

Example: AI for MRI and CT scan analysis.

AI detects fraud, predicts stock trends, and automates trading.

Example: AI-powered credit scoring.

AI personalizes learning and automates grading.

Example: AI tutors like Duolingo.

AI recommends products and manages inventory, and pricing.

Example: Amazon’s recommendation system.

  Why Python? (Simplicity, Libraries, Community Support).

  Setting up Python (Anaconda, Jupyter Notebook, VS Code.

 Data Types (int, float, string, list, tuple, dict).

 Control Structures (if-else, loops).

 Functions & Modules.

 File Handling.

Classes & Objects

 Inheritance & Polymorphism.

 Encapsulation & Abstraction.

 How OOP is used in AI (e.g., Model Classes in TensorFlow/PyTorch).

NumPy – Arrays & Numerical Computation.

Pandas – Data Manipulation & Analysis.

Matplotlib & Seaborn – Data Visualization.

Reading/Writing CSV, Excel, JSON.

Handling Missing Data.

Data Cleaning & Transformation.

Why Statistics? (Data Analysis, Decision-Making, Model        Evaluation).  

Types: Descriptive vs. Inferential Statistics.

Measures of Central Tendency (Mean, Median, Mode).  

Measures of Dispersion (Variance, Standard Deviation, Range, IQR).  

Skewness & Kurtosis.

Concepts: Probability Rules, Conditional Probability.  

Probability Distributions:

Discrete (Binomial, Poisson).

Continuous (Normal, Exponential).

Hypothesis Testing (p-value, Confidence Intervals).

Common Tests: t-test, Chi-square test, ANOVA.

Data Visualization (Histograms, Box Plots, Scatterplots).  

Correlation & Covariance.

Outlier Detection.

Regression Basics (Linear & Logistic Regression).

Bias-Variance Tradeoff.

Overfitting vs. Underfitting.

What is ML? (Definition, Importance)

Types of ML:

Supervised Learning (Regression, Classification)

Unsupervised Learning (Clustering)

Reinforcement Learning (Agent-Environment Interaction)

Regression (Linear Regression, Multiple Regression)

Regularization (L1 - Lasso, L2 - Ridge)

Classification:

Logistic Regression

Decision Trees

Random Forest

K-Nearest Neighbors (KNN)

Naïve Bayes

Performance Metrics: Accuracy, Precision, Recall, Confusion Matrix

Clustering (K-Means, Hierarchical, DBSCAN)

Train-Test Split & Cross-Validation

Overfitting vs. Underfitting

Hyperparameter Tuning (Grid Search, Random Search)

Handling Missing Data

Feature Scaling (Normalization, Standardization)

Feature Selection & Extraction

What is Deep Learning?

Perceptron & Multi-Layer Perceptron (MLP)

Activation Functions (ReLU, Sigmoid, Tanh, Softmax)

Forward & Backpropagation

Architecture of ANNs

Training Process (Gradient Descent, Optimizers like Adam, SGD)

Loss Functions (MSE, Cross-Entropy)

CNN Architecture (Convolution, Pooling, Fully Connected Layers)

Padding, Strides, and Feature Maps

Transfer Learning & Pretrained Models (VGG, ResNet)

What is NLP?

NLP Pipeline (Tokenization, Lemmatization, Stopword Removal)

Bag of Words (BoW)

TF-IDF (Term Frequency-Inverse Document Frequency)

Word Embeddings (Word2Vec, GloVe)

Basics of RNNs & Their Limitations

Long Short-Term Memory (LSTM)

Gated Recurrent Units (GRU)

  What is Generative AI?

Key Applications

Text (Chatbots, Content Generation)

Image (DALL·E, MidJourney)

  Audio (Music Generation, Voice Synthesis)

Code (Cursor, Copilot)

Evolution of GenAI:

From Rule-Based Systems to Deep Learning

Comparison of Generative Models (GANs, VAEs, LLMs)

Challenges in GenAI (Bias, Hallucinations, Ethical Considerations)

What is Prompt Engineering?

Importance of Effective Prompt Design

Basic Prompting Techniques:

Instruction-Based Prompts

Few-Shot & Zero-Shot Learning

Advanced Prompt Engineering:

 Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Structured vs. Unstructured Prompts

Experimenting with LLMs (Using GPT-4, Claude, or LLaMA)

Transformers & LLMs   Why Transformers? (Limitations of RNNs & LSTMs)

⦿ Key Components:

Self-Attention Mechanism

Multi-Head Attention

Encoder-Decoder Architecture

◙ Evolution of Transformers:

From BERT to GPT, T5, and Beyond

  Large Language Models (LLMs) What are LLMs?

Pre Training vs. Fine-Tuning

Popular LLM Architectures:

GPT (OpenAI GPT-4,O3)

DeepSeek

BERT (Contextual Embeddings)

T5 (Text-to-Text Models)

◙ Challenges in LLMs:

Bias & Ethical Issues

Scalability & Cost

Model Hallucinations

GANs & VAEs (Other Generative Models)

Generative Adversarial Networks (GANs):

What are GANs?

How GANs Work : Generator & Discriminator

Applications of GANs (DeepFake, Image Generation,Super-Resolution)

◙ Autoencoders & Variational Autoencoders (VAEs):

What are Autoencoders?

Difference Between Autoencoders & VAEs

 Applications (Data Denoising, Anomaly Detection)

Lightweight Models (LIMs)

What are Lightweight AI Models?

Difference Between LIMs & LLMs

Use Cases of LIMs in Edge AI

LangChain   What is LangChain

  Building Modular LLM Workflows

Practical Applications

  Hugging Face   Overview of Hugging Face Transformers & Datasets

How to Fine-Tune & Deploy Models

Vector Databases & Retrieval-Augmented Generation (RAG)

Introduction to Vector Databases (Pinecone, Weaviate, FAISS)

Understanding RAG and Its Role in GenAI

What are AI Agents?

Difference Between AI Agents and Traditional AI Systems

Key Characteristics:

Autonomy

Goal-Oriented Behavior

Tool Usage & Execution

Real-World Applications of AI Agents

CrewAI (Multi-Agent Collaboration)

Overview: How CrewAI enables multi-agent workflows

Components: Roles, Tasks, Tools, Memory

Use Case: Automating research and content generation

N8N (Workflow Automation for AI Agents)

What is N8N?

Connecting AI Agents with APIs and automation

Use Case: AI-driven task execution with n8n

Langflow (Visual Agent Orchestration)

Introduction to Langflow

Building AI Agent workflows with a drag-and-drop interface

Use Case: Rapid prototyping and deployment of AI Agents

  Creating AI Agents using CrewAI + Lang Flow

  Automating tasks with CrewAI + N8N

  Multi-agent collaboration for business workflows

Definition: Physical AI, also known as Embodied AI, integrates AI with physical systems to enable machines to perceive, interpret, and act in real-world environments.

Core Components:

Sensors : Devices like LiDAR, cameras, and temperature sensors for environmental data collection.

Actuators : Robotic arms, motors, and other mechanisms to execute physical actions.

AI Algorithms : For real-time decision-making and pattern recognition.

Embedded Systems : Enabling low-latency processing and interaction.

Healthcare : Robotic surgery, patient monitoring, and rehabilitation.

Manufacturing : Automation, quality control, and predictive maintenance.

Transportation : Autonomous vehicles and drones.

Service Industry : Customer service robots and automated delivery systems.

NVIDIA Cosmos Platform

Overview: NVIDIA Cosmos is a platform designed to accelerate the development of physical AI systems such as autonomous vehicles and robots.

World Foundation Models (WFM): State-of-the-art models trained on millions of hours of driving and robotics video data, available under an open model license.

Benefits of Using NVIDIA Cosmos

Accessibility : Open and easy access to high-performance models and data pipelines.

Efficiency: Out-of-the-box optimizations minimize total cost of ownership and accelerate time-to-market.

Safety: Inbuilt guardrails to filter unsafe content and harmful prompts

Autonomous Vehicles: Enhanced perception and decision-making capabilities.

Robotics: Improved interaction with complex and unpredictable environments.

Augmented Reality: Optimized video sequences for AR applications