AI Foundation Course in Chennai

Master Python, Data Science & Machine Learning with Real-Time Projects

Hands-on Training

Placement Assistance

Expert Mentorship

Master Artificial Intelligence with Industry - Focused Training

DreamsPlus offers a comprehensive AI Foundation Course in Chennai designed for students, fresh graduates, and working professionals looking to build a successful career in Artificial Intelligence. Our industry-focused curriculum covers Python Programming, Data Science, Machine Learning, Deep Learning, NLP, SQL, and Backend Development through expert-led training and hands-on projects.

Whether you’re starting from scratch or looking to upskill, our AI Foundation course equips you with practical knowledge, real-world experience, industry-recognized certification, and placement assistance to help you become job-ready and excel in today’s AI-driven industry.

At DreamsPlus, we focus on practical learning rather than just theory. Our curriculum is designed by industry experts to help learners gain job-ready AI skills.

What You’ll Learn

Python Programming from Basics to Advanced
Backend Development using FastAPI, Flask & Django
Data Science Fundamentals
Mathematics for AI & Machine Learning
Data Analysis using Pandas
Exploratory Data Analysis (EDA)
Feature Engineering
Machine Learning Algorithms
Deep Learning
Natural Language Processing (NLP)
SQL for Data Science
Data Visualization using Matplotlib & Plotly
Hands-on Projects

AI Foundation Course Highlights

Course Curriculum

Module 1 – Python Programming

Build a strong programming foundation with Python.

Topics Covered:

Python Basics
Variables & Data Types
Operators
Loops
Functions
Object-Oriented Programming
Exception Handling
File Handling
Modules & Packages
Decorators
Generators & Iterators
Multithreading
Multiprocessing
Async Programming
Memory Management
Garbage Collection
Python Internals
Coding Practice

Module 2 – Backend Development

Learn to build scalable backend applications.
Topics Covered:

FastAPI
Flask
Django
REST API Development
GraphQL
WebSockets
OAuth2
JWT Authentication
API Gateway
Microservices
Service Discovery
Load Balancing
Redis Cache
Celery
RabbitMQ
Kafka
gRPC

Module 3 – Data Science Fundamentals

Understand how data is collected, processed, and transformed into business insights.
Topics Covered:

Data Science Lifecycle
CRISP-DM Methodology
Business Understanding
Data Collection
Data Cleaning
Data Preparation
Feature Engineering
Model Building
Model Evaluation
Deployment
Monitoring
Data Types
Data Scientist Roles & Responsibilities

Module 4 – Mathematics & Statistics for AI

Develop the mathematical knowledge required for Machine Learning.
Topics Covered:

Linear Algebra
Calculus
Probability
Bayes Theorem
Statistics
Descriptive Statistics
Inferential Statistics
Hypothesis Testing
Confidence Intervals
A/B Testing
Central Limit Theorem

Module 5 – Data Wrangling & Exploratory Data Analysis

Learn how to clean, prepare, and analyze datasets.
Topics Covered:

Pandas
DataFrames
Missing Value Handling
Merge & Join
GroupBy
Pivot Tables
Data Cleaning
Data Transformation
Correlation Analysis
Histograms
Boxplots
Scatterplots
Outlier Detection

Module 6 – Feature Engineering

Transform raw data into meaningful features for Machine Learning.
Topics Covered:

Feature Selection
Feature Extraction
Encoding Techniques
Data Scaling
Polynomial Features
Date Features
Text Features
Image Features

Module 7 – Machine Learning

Master supervised and unsupervised learning algorithms.
Topics Covered:
Supervised Learning

Linear Regression
Logistic Regression
Decision Trees
Random Forest
Extra Trees
XGBoost
LightGBM
CatBoost
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes

Unsupervised Learning

K-Means Clustering
Hierarchical Clustering
DBSCAN
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Uniform Manifold Approximation and Projection (UMAP)

Reinforcement Learning

Fundamentals
Overview

Module 8 – Model Evaluation

Evaluate Machine Learning models effectively.
Topics Covered:

Accuracy
Precision
Recall
F1 Score
ROC-AUC
Confusion Matrix
Log Loss
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
R² Score
Mean Absolute Percentage Error (MAPE)
Clustering Metrics

Module 9 – Deep Learning

Build intelligent AI applications using Neural Networks.
Topics Covered:

Neural Networks
CNN
RNN
LSTM
GRU
Transformers
Transfer Learning
Fine-Tuning
Autoencoders

Module 10 – Natural Language Processing (NLP)

Work with human language using AI.
Topics Covered:

Tokenization
Stemming
Lemmatization
TF-IDF
Word2Vec
FastText
GloVe
BERT
Sentence Transformers
Topic Modeling
Named Entity Recognition (NER)
Sentiment Analysis
Question Answering

Module 11 – SQL for Data Science

Learn SQL for data extraction and analysis.
Topics Covered:

SELECT
WHERE
GROUP BY
HAVING
ORDER BY
JOINs
Window Functions
Common Table Expressions (CTEs)
Views
Stored Procedures
Query Optimization

Module 12 – Data Visualization

Present data effectively using visualization tools.
Topics Covered:

Matplotlib
Plotly
Interactive Dashboards
Business Reporting

Who Can Enroll?

This course is suitable for:

Students
Fresh Graduates
Software Developers
Testing Professionals
IT Professionals
Data Analysts
Business Analysts
Engineers
Career Switchers
Anyone interested in Artificial Intelligence

Learning Outcomes

After completing this AI Foundation Course, you will be able to:

Write Python programs confidently.
Build backend APIs using modern frameworks.
Perform data analysis and preprocessing.
Develop Machine Learning models.
Understand Deep Learning concepts.
Build NLP applications.
Work with SQL databases.
Create professional data visualizations.
Solve real-world AI problems using industry best practices.

Career Opportunities

After completing this course, you can pursue roles such as:

AI Engineer
Machine Learning Engineer
Data Scientist
Python Developer
Backend Developer
NLP Engineer
Data Analyst
AI Application Developer
Business Intelligence Developer

Why Learn AI in 2026?

Artificial Intelligence is transforming every industry, including healthcare, finance, retail, manufacturing, education, logistics, and cybersecurity. Organizations are actively hiring professionals with AI, Machine Learning, Python, and Data Science skills. Building a strong AI foundation today prepares you for high-demand career opportunities and future-ready roles.

Start Your AI Journey Today

Become job-ready with practical Artificial Intelligence training at DreamsPlus. Learn Python, Data Science, Machine Learning, Deep Learning, NLP, SQL, and Backend Development through expert-led sessions and real-world projects.

Enroll Online

Frequently Asked Question

Yes. The course starts with Python basics and gradually progresses to advanced AI concepts.

No prior programming experience is required. Python fundamentals are covered from scratch.

Yes. The course includes practical assignments, hands-on labs, and industry-oriented projects.

Yes. Participants who successfully complete the course will receive a DreamsPlus Course Completion Certificate.

Yes. DreamsPlus offers resume preparation, interview guidance, and placement assistance to eligible learners.