Master Artificial Intelligence AI

Fundamentals to IT & AI

Python for AI

Statistics for AI

Machine Learning

Deep Learning

Transformers & GEN AI

Ai Agents & Applications

Physical AI ( Robotics)

Realtime ClassRoom Training

Project and Task Based

6 to 8 Hrs Every Day

Interviews, Jobs and Placement Support

Communication Skills & Personality Development

Interview Preparations

50000 +

Students Enrolled

4.7

(500)

Ratings

6 months

Duration

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Demand

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Learn from Curated Curriculums developed by Industry Experts

Master Artificial Intelligence (AI) Course Curriculum

It stretches your mind, think better and create even better.

Fundamentals of AI

Module 1

Topics:

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.

Module 2

Topics:

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.

Module 3

Topics:

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.

Module 4

Topics:

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.

Module 5

Topics:

Software Development

AI aids code generation, bug fixing, and testing.

Example: GitHub Copilot.

Autonomous Systems

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

Example: Tesla autopilot.

Healthcare

AI diagnoses discovers drugs, and aids research.

Example: AI for MRI and CT scan analysis.

Finance

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

Example: AI-powered credit scoring.

Education

AI personalizes learning and automates grading.

Example: AI tutors like Duolingo.

Retail & E-Commerce

AI recommends products and manages inventory, and pricing.

Example: Amazon’s recommendation system.

Python for AI

Module 1

Topics:

Why Python?

Simplicity, Libraries, Community Support.

Setting up Python

Anaconda, Jupyter Notebook, VS Code.

Module 2

Topics:

Data Types

int, float, string, list, tuple, dict.

Control Structures

if-else, loops.

Functions & Modules

Functions and reusable code organization.

File Handling

Reading and writing files in Python.

Module 3

Topics:

Classes & Objects

Defining and using classes and objects.

Inheritance & Polymorphism

Reusing and extending class functionality.

Encapsulation & Abstraction

Data hiding and simplifying complex systems.

How OOP is used in AI

e.g., Model Classes in TensorFlow/PyTorch.

Module 4

Topics:

NumPy

Arrays & Numerical Computation.

Pandas

Data Manipulation & Analysis.

Matplotlib & Seaborn

Data Visualization.

Module 5

Topics:

Reading/Writing Data

CSV, Excel, JSON.

Handling Missing Data

Techniques to address incomplete datasets.

Data Cleaning & Transformation

Preparing data for analysis and modeling.

Statistics for AI

Module 1

Topics:

Why Statistics?

Data Analysis, Decision-Making, Model Evaluation.

Types

Descriptive vs. Inferential Statistics.

Module 2

Topics:

Measures of Central Tendency

Mean, Median, Mode.

Measures of Dispersion

Variance, Standard Deviation, Range, IQR.

Skewness & Kurtosis

Understanding data distribution shapes.

Module 3

Topics:

Concepts

Probability Rules, Conditional Probability.

Probability Distributions

Discrete (Binomial, Poisson).

Continuous (Normal, Exponential).

Module 4

Topics:

Hypothesis Testing

p-value, Confidence Intervals.

Common Tests

t-test, Chi-square test, ANOVA.

Module 5

Topics:

Data Visualization

Histograms, Box Plots, Scatterplots.

Correlation & Covariance

Measuring relationships between variables.

Outlier Detection

Identifying and handling anomalies in data.

Module 6

Topics:

Regression Basics

Linear & Logistic Regression.

Bias-Variance Tradeoff

Balancing model complexity and generalization.

Overfitting vs. Underfitting

Understanding model performance issues.

Machine Learning

Module 1

Topics:

What is ML?

Definition, Importance.

Types of ML

Supervised Learning (Regression, Classification).

Unsupervised Learning (Clustering).

Reinforcement Learning (Agent-Environment Interaction).

Module 2

Topics:

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.

Module 3

Topics:

Clustering

K-Means, Hierarchical, DBSCAN.

Module 4

Topics:

Train-Test Split & Cross-Validation

Splitting data and validating models.

Overfitting vs. Underfitting

Addressing model performance issues.

Hyperparameter Tuning

Grid Search, Random Search.

Module 5

Topics:

Handling Missing Data

Techniques for incomplete datasets.

Feature Scaling

Normalization, Standardization.

Feature Selection & Extraction

Choosing and transforming relevant features.

Deep Learning

Module 1

Topics:

What is Deep Learning?

Introduction to deep learning concepts.

Perceptron & Multi-Layer Perceptron (MLP)

Basic neural network structures.

Activation Functions

ReLU, Sigmoid, Tanh, Softmax.

Forward & Backpropagation

How neural networks learn.

Module 2

Topics:

Architecture of ANNs

Structure of artificial neural networks.

Training Process

Gradient Descent, Optimizers like Adam, SGD.

Loss Functions

MSE, Cross-Entropy.

Module 3

Topics:

CNN Architecture

Convolution, Pooling, Fully Connected Layers.

Padding, Strides, and Feature Maps

Techniques in CNN processing.

Transfer Learning & Pretrained Models

VGG, ResNet.

Module 4

Topics:

What is NLP?

Introduction to natural language processing.

NLP Pipeline

Tokenization, Lemmatization, Stopword Removal.

Bag of Words (BoW)

Basic text representation.

TF-IDF

Term Frequency-Inverse Document Frequency.

Word Embeddings

Word2Vec, GloVe.

Module 5

Topics:

Basics of RNNs & Their Limitations

Introduction to recurrent neural networks.

Long Short-Term Memory (LSTM)

Advanced RNN architecture.

Gated Recurrent Units (GRU)

Simplified LSTM variant.

Transformers & GEN AI

Module 1

Topics:

What is Generative AI?

Overview of generative AI concepts.

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.

Module 2

Topics:

What is Prompt Engineering?

Crafting inputs for AI models.

Importance of Effective Prompt Design

Improving model outputs.

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.

Module 3

Topics:

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.

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.

Module 4

Topics:

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.

AI Agents & Applications

Module 1

Topics:

What are AI Agents?

Overview of AI agents.

Difference Between AI Agents and Traditional AI Systems

Comparing agent-based and traditional AI.

Key Characteristics

Autonomy.

Goal-Oriented Behavior.

Tool Usage & Execution.

Real-World Applications of AI Agents

Examples of AI agent use cases.

Module 2

Topics:

CrewAI

Overview: How CrewAI enables multi-agent workflows.

Components: Roles, Tasks, Tools, Memory.

Use Case: Automating research and content generation.

N8N

What is N8N?

Connecting AI Agents with APIs and automation.

Use Case: AI-driven task execution with n8n.

Langflow

Introduction to Langflow.

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

Use Case: Rapid prototyping and deployment of AI Agents.

Module 3

Topics:

Creating AI Agents

Using CrewAI + Lang Flow.

Automating tasks

With CrewAI + N8N.

Multi-agent collaboration

For business workflows.

Physical AI (Robotics)

Module 1

Topics:

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.

Module 2

Topics:

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.

Module 3

Topics:

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.

Module 4

Topics:

Autonomous Vehicles

Enhanced perception and decision-making capabilities.

Robotics

Improved interaction with complex and unpredictable environments.

Augmented Reality

Optimized video sequences for AR applications.

TOOlS & PLATFORMS

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Work on our Real-time Projects , Task Based

LMS Project

An LMS project develops a digital platform for online learning,featuring course creation, content management, user tracking,assessments, and reporting, aimed at enhancing educational interaction.

HRMS Project

The HRMS project develops a digital system for managing HR functions like employee data, payroll, recruitment, and performance, aiming to streamline processes and enhance organizational efficiency.

CRM Project

A CRM project develops a system to manage company interactions with customers, incorporating tools for contact, sales, productivity, and support to enhance service, drive sales, and boost retention.

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