Groot Academy · Jaipur

Artificial Intelligence Course in Jaipur

Build a broad foundation in Artificial Intelligence, from Python and data preparation to evaluated machine-learning models, neural networks, text and image applications. The course also introduces Generative AI and deployment; focused Data Science and Generative AI paths offer related study options.

Practical learning
Project practice
Jaipur

Get Course Details View Curriculum

Data & AI

Artificial Intelligence

FocusSkills & Projects

LocationJaipur, Rajasthan

Course Overview

Build your foundation in Artificial Intelligence

Start by framing a problem in terms of inputs, outputs, constraints and measurable success criteria. Work through Python, mathematics and data preparation before selecting and evaluating models.

The curriculum connects supervised and unsupervised learning to deep learning, Natural Language Processing and computer vision. Projects help you document the data, modelling choices and limitations rather than judge an application by its output alone.

Later topics introduce Generative AI, prediction APIs, model monitoring and responsible use. The related Generative AI course goes further into LLM applications, while the Data Science and Machine Learning course emphasizes analysis and predictive modelling workflows.

Who can join?

Students, graduates and professionals who want a structured introduction to AI. Basic Python and school-level mathematics are helpful before moving into model-building topics.

What You Learn

Skills and tools

AI problem framingPython, NumPy and PandasMathematics, probability and statisticsData preparation and feature engineeringSupervised and unsupervised learningNeural networks, TensorFlow and KerasNatural Language ProcessingComputer vision and OpenCVGenerative AI foundationsPrediction APIs and model deploymentModel evaluation and responsible AI

Learning Path

Course curriculum

Explore the published learning outline below. Confirm the detailed syllabus and module coverage for your chosen batch with the course team.

Introduction to Artificial Intelligence

Understand AI terminology, common problem types, intelligent systems and how AI, machine learning and data science relate.

Introduction to Artificial Intelligence
History and Evolution of AI
Types of Artificial Intelligence
Narrow AI vs General AI
Artificial Intelligence vs Machine Learning
Artificial Intelligence vs Data Science
Real-World Applications of AI
AI Industry and Career Opportunities
Understanding Intelligent Systems
Setting Up the AI Learning Environment

Python for Artificial Intelligence

Use Python collections, functions, NumPy and Pandas to load and work with datasets.

Introduction to Python for AI
Python Variables and Data Types
Conditional Statements and Loops
Functions and Modules
Lists, Tuples, Sets and Dictionaries
NumPy Fundamentals
Working with NumPy Arrays
Pandas Fundamentals
Working with DataFrames
Loading and Processing Datasets
Functions and OOP
File Handling
APIs and JSON
Data Structures and Algorithms
Matplotlib
Automation with Python

Mathematics and Statistics for AI

Review vectors, matrices, probability, distributions and statistics before modelling.

Introduction to Mathematics for AI
Linear Algebra Fundamentals
Vectors and Matrices
Matrix Operations
Probability Fundamentals
Statistics for AI
Mean, Variance and Standard Deviation
Probability Distributions
Correlation and Covariance
Mathematics and Statistics Practice
Calculus Basics

Data Preparation for AI

Clean datasets, encode inputs, scale features and prepare a usable modelling dataset.

Understanding AI Datasets
Data Collection and Data Sources
Data Cleaning
Handling Missing Values
Handling Duplicate Data
Encoding Categorical Data
Feature Scaling
Normalization and Standardization
Feature Selection and Engineering
Data Preparation Project

Machine Learning Fundamentals

Frame a task, separate training and test data and recognize overfitting and underfitting.

Introduction to Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning Overview
Machine Learning Workflow
Training and Testing Data
Overfitting and Underfitting
Introduction to Scikit-learn
Machine Learning Fundamentals Project

Supervised Learning Algorithms

Compare regression and classification methods using suitable evaluation measures.

Introduction to Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors
Naive Bayes
Support Vector Machines
Model Evaluation
Supervised Learning Project

Unsupervised Learning Algorithms

Use clustering and dimensionality reduction to explore patterns and document their interpretation.

Introduction to Unsupervised Learning
Clustering Concepts
K-Means Clustering
Hierarchical Clustering
DBSCAN Overview
Dimensionality Reduction
Principal Component Analysis
Customer Segmentation
Cluster Evaluation
Unsupervised Learning Project

Deep Learning Fundamentals

Study neural-network layers, activations, loss functions and optimization with TensorFlow and Keras.

Introduction to Deep Learning
Artificial Neural Networks
Neurons and Activation Functions
Forward Propagation
Backpropagation
Loss Functions
Optimizers
Introduction to TensorFlow
Introduction to Keras
Building Your First Neural Network

Advanced Deep Learning

Explore convolutional and recurrent networks, transfer learning, validation and tuning.

Introduction to Convolutional Neural Networks
Image Data and CNN Architecture
CNN Layers and Filters
Introduction to Recurrent Neural Networks
Sequence Data
LSTM Networks
Transfer Learning
Model Training and Validation
Hyperparameter Tuning
Deep Learning Project
PyTorch
GPU Training
Speech Recognition

Natural Language Processing

Prepare text inputs and practise sentiment analysis and text classification.

Introduction to Natural Language Processing
Text Data Collection
Text Cleaning and Preprocessing
Tokenization
Stop Words and Stemming
Text Vectorization
Bag of Words and TF-IDF
Sentiment Analysis
Text Classification
NLP Project

Computer Vision

Work with image inputs, OpenCV and image-recognition concepts, including classification, detection and segmentation.

Introduction to Computer Vision
Understanding Image Data
Image Preprocessing
Image Classification
Object Detection Concepts
Image Segmentation Concepts
OpenCV Fundamentals
Building Image Recognition Models
Computer Vision Applications
Computer Vision Project
Facial Recognition
OCR Systems
Real-Time Vision Systems

Generative AI Fundamentals

Introduce language models, transformers and prompt engineering while recognizing limitations and responsible-use needs.

Introduction to Generative AI
Generative AI vs Traditional AI
Understanding Large Language Models
Introduction to Transformers
Prompt Engineering Fundamentals
AI Content Generation
AI Image Generation Concepts
AI-Powered Applications
Responsible Generative AI
Generative AI Mini Project

AI Model Deployment

Save models, create a prediction API, test application integration and explore deployment and monitoring concepts.

Introduction to AI Model Deployment
Saving Trained Models
Pickle and Joblib
Creating AI Prediction APIs
Introduction to Flask
Integrating AI Models with Applications
API Testing
Introduction to Cloud Deployment
AI Model Monitoring Concepts
AI Deployment Project
Docker
Kubernetes
CI/CD
AI Architecture
Feature Stores
Model Versioning
AI Infrastructure Design
Distributed Systems
AI Scalability

AI Ethics and Responsible AI

Identify bias, privacy, explainability, security and governance concerns, and recognize where human review is necessary.

Introduction to AI Ethics
AI Bias and Fairness
Data Privacy in AI
Responsible AI Development
Explainable AI Concepts
AI Security Fundamentals
Ethical AI Decision Making
AI Governance Concepts
Legal and Social Impact of AI
Responsible AI Best Practices

AI Live Projects and Capstone

Combine data preparation, modelling, evaluation and deployment in a practical project with documented limitations.

AI-Based Prediction Project
Customer Churn Prediction
Image Classification Project
Sentiment Analysis Project
NLP Chatbot Project
Computer Vision Application
Generative AI Application
AI Model Deployment Project
End-to-End Artificial Intelligence Project
Final Artificial Intelligence Capstone Project
Fraud Detection
Recommendation Systems
AI SaaS Platforms
AI Portfolio Development

Apply Your Skills

Project practice ideas

Use these examples to discuss suitable practice work with your trainer.

AI classification prototype

Prepare a small dataset, train a simple model and explain the evaluation results.

Applied AI case study

Define an AI problem for a practical domain and document the data, model choice, risks and success criteria.

Image classification project

Prepare a practice image dataset, train or adapt a model and report validation results and limitations.

Sentiment analysis project

Prepare text data, compare classification results and document the effects of preprocessing and dataset choices.

AI model deployment project

Expose a saved model through a prediction API, test representative inputs and describe monitoring needs.

Final Artificial Intelligence capstone

Define an end-to-end AI problem, justify the data and model choices, evaluate outputs and document responsible-use considerations.

Career Direction

Roles to explore

Build a portfolio and practise explaining your work. Role requirements depend on the employer, your skills and experience.

AI trainee
Machine learning intern
Junior AI application developer

Common Questions

Frequently asked questions

How is this Artificial Intelligence course different from Data Science?

Artificial Intelligence focuses on building systems that perform tasks such as prediction, classification and decision support. Data Science includes broader data preparation, analysis, statistics and modelling workflows.

How is this different from Generative AI?

This course covers broad AI and machine-learning foundations. Generative AI focuses more specifically on language models, prompting, retrieval and AI-assistant applications.

What should I know before starting the modelling topics?

Basic Python and school-level mathematics are helpful. Python, linear algebra, probability and statistics foundations are included before data preparation and modelling.

Are text and image applications included?

Yes. Natural Language Processing includes text preparation, sentiment analysis and classification. Computer vision topics introduce image preprocessing, classification, detection, segmentation and OpenCV.

Does the course cover deployment?

Yes. Later topics cover saving models, prediction APIs, Flask integration, API testing, cloud deployment and monitoring concepts.

How is responsible AI covered?

The curriculum includes bias, privacy, explainability, security and governance. Project work should record limitations and situations that require human review.

Who is this course for?

Students, graduates and professionals who want a structured introduction to AI. Basic Python and school-level mathematics are helpful before moving into model-building topics.

How can I confirm fees, duration and batch timings?

Contact Groot Academy for the current syllabus, fees, duration and available learning modes before enrolling. These details depend on the selected course and batch.

Ready to discuss Artificial Intelligence?

Ask the course team about syllabus coverage, current batch timings, fees, duration and learning modes before you decide.

Send an enquiry Call course team WhatsApp

Continue exploring

Explore all IT courses