Artificial Intelligence

AI & Machine Learning

A practical AI and machine learning course for people who want to prepare data, train models, and build small AI applications. You work in Python, then move through classical machine learning into deep learning, language models, embeddings, and retrieval-augmented generation.

Learn by building real-world software with working engineers.

Level: Beginner → Advanced
Duration: 8–12 Weeks
Mode: Online
Fee: Contact for current batch fee

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Who should join

Developers who want to add machine learning and LLM features to software.
Students aiming at AI application or junior data roles.
Analysts who want to move from spreadsheets toward Python and models.
Professionals evaluating how AI fits a product they already understand.

Prerequisites

Basic comfort with a computer and installing software.
Python Programming or equivalent basics will help. Foundations are revisited where the batch needs them.
No prior machine learning job is required.

Technologies

Python
NumPy
Pandas
Matplotlib
Scikit-learn
Machine Learning
Deep Learning
PyTorch/TensorFlow
NLP
Computer Vision
LLMs
Embeddings
RAG

Curriculum

Modules are taught through code, review, and projects. The project chosen for a batch comes from the list below.

Module 1Python for AI

Python for data work
Functions and modules
Virtual environments
Notebooks and scripts

Module 2NumPy

Arrays
Vectorized operations
Broadcasting
Numerical workflows

Module 3Pandas

DataFrames
Cleaning data
Filtering and grouping
Preparing a dataset for modeling

Module 4Data visualization

Matplotlib
Charts that answer a question
Reading a plot before trusting it

Module 5Statistics fundamentals

Distributions
Mean, median, and spread
Correlation
Basic probability for modeling

Module 6Machine Learning

Regression
Classification
Clustering
Feature engineering
Model evaluation
Cross validation

Module 7Scikit-learn

Train and test splits
Estimators
Pipelines
Comparing simple models

Module 8Deep Learning

Neural network ideas
Training and loss
Overfitting
When a deep model is unnecessary

Module 9PyTorch/TensorFlow fundamentals

Tensors
A small model
Training loop concepts
Inference

Module 10NLP

Text as data
Cleaning text
Classical NLP ideas
Where embeddings take over

Module 11Computer Vision

Images as data
A classification workflow
Limits of a small dataset

Module 12LLMs

What a language model does
Calling an LLM API
Limits, cost, and failure modes

Module 13Prompt engineering

Instructions
Examples
Checking outputs
Prompts inside an application

Module 14Embeddings

Vectors
Similarity
Storing embeddings for search

Module 15RAG

Retrieval
Chunking documents
Grounding an answer in source text

Module 16AI application development

Wrapping a model in an API
A small product surface
Error handling and a deployment concept

Projects

One of these becomes the main project for the batch. The list is configuration, so a future batch can add or replace a project without redesigning the page.

AI chatbot

A task-specific assistant with a defined boundary.

Python
LLMs
APIs

What you learn: How a chat interface sits on top of a model call.

Document Q&A system

Questions answered from a folder of documents.

Python
Embeddings
RAG

What you learn: How retrieval keeps answers tied to source material.

RAG application

A small retrieval-augmented application with a clear corpus.

Python
LLMs
RAG

What you learn: How chunking, search, and generation fit together.

Recommendation system

A simple recommender on a prepared dataset.

Python
Pandas
Scikit-learn

What you learn: How similarity and evaluation work on tabular data.

Image classification system

Classify a small image set and inspect mistakes.

Python
Computer Vision

What you learn: How a vision model is trained and where it fails.

AI assistant

An assistant for one workflow, with logging of what it did.

Python
LLMs
APIs

What you learn: How to constrain an assistant to a real task.

Learning outcomes

Skills you can develop in this course.

Prepare data with NumPy, Pandas, and clear charts.
Train and evaluate classical machine learning models in scikit-learn.
Explain deep learning, NLP, and computer vision at a working level.
Call an LLM, use embeddings, and build a small RAG flow.
Finish with an AI project you can demonstrate.

Course duration

8–12 Weeks. This is a planning range until the batch calendar is confirmed.

Training mode

Online.

Certificate of Completion

Students who satisfy the defined course completion requirements, including the assigned project work, may receive an SR Infotech Certificate of Completion. The certificate is issued by SR Infotech. It is not a government accreditation or a university degree.

Related engineering work

Interested in professional software development? Explore our AI Development services.

FAQ

Is this an AI ML course for beginners?

It starts from Python for data work and statistics, then moves into models and LLM applications. Some Python experience helps, and we will tell you if a Python Programming batch should come first.

Will I only study theory?

No. You write code, evaluate models, and build an application-style project.

Do you cover RAG and LLMs?

Yes. Later modules cover language models, prompts, embeddings, and retrieval-augmented generation, then a small AI application.

What is the fee?

Contact for current batch fee.

Training

Enroll in AI & Machine Learning

Tell us your background and preferred batch. We will contact you with the next steps. This is not instant admission.