AI & Machine Learning Professional Program
AI & Machine Learning Professional Program
Build industry-ready skills with practical training, expert guidance and career-focused learning.
Apply Now3 Months
120 Hours
Monday – Friday
Offline / Online
17 Modules
Course Overview
If you’re searching for AI & ML courses in Coimbatore that go past slides and theory, this program is built around real work. You spend most of your time on hands-on projects, building models that predict outcomes and AI solutions that solve actual business problems.
The machine learning course modules take you from the fundamentals to building your own models. The deep learning & computer vision course modules then teach you how to make systems recognize images and patterns. The MLOps course part covers what happens after a model is built: how it gets deployed, monitored, and scaled in the cloud so it keeps working long after launch. You finish with a certified AI ML course credential and a project portfolio to show for it.
By the end of the programme, students will:
- Build machine learning models
- Run predictive analytics on real data
- Apply deep learning concepts
- Deploy and monitor AI solutions in the cloud
COURSE MODULES
01 | Introduction to Artificial Intelligence & Python Programming
AI is already reshaping healthcare, finance, retail, and manufacturing, and this is where you start learning how it works. This first module of our machine learning course gives you a clear picture of what AI, Machine Learning, and Data Science really mean and how they connect. Alongside that, you begin learning Python, the language most AI work is built on, so every module that follows feels easier to handle.
What You'll Learn
- Introduction to Artificial Intelligence, Machine Learning and Data Science
- Applications of AI across industries
- Python installation and development environment
- Variables, Data Types and Operators
- Conditional Statements and Loops
- Functions and Modules
- Object-Oriented Programming (OOP)
- File Handling
- Exception Handling
- Working with Python Libraries
02| Data Science Fundamentals
Every AI model is only as good as the data behind it. In this module you learn how to collect, clean, organize, and prepare datasets so they’re ready for machine learning, using the same Python Libraries working data professionals rely on everyday. It's the unglamorous part of AI that ends up deciding whether a model works or not, so we spend proper time on it.
What You'll Learn
- Introduction to Data Science
- NumPy Arrays
- Pandas DataFrames
- Reading CSV and Excel files
- Data Cleaning Techniques
- Handling Missing Values
- Data Transformation
- Feature Engineering Basics
- Data Aggregation
- Exploratory Data Analysis
03| Statistics for Machine Learning
Every machine learning algorithm runs on statistics. This module covers the math you need to understand how predictive models actually learn, kept practical and easy to follow.
What You'll Learn
- Descriptive Statistics
- Mean, Median and Mode
- Standard Deviation
- Probability
- Probability Distributions
- Correlation
- Covariance
- Hypothesis Testing
- Sampling Techniques
- Statistical Significance
04| Data Visualization
Numbers alone rarely convince anyone. Here you learn to turn raw data into clear visuals that show patterns at a glance and help people make better business decisions.
What You'll Learn
- Introduction to Data Visualization
- Matplotlib
- Seaborn
- Line Charts
- Bar Charts
- Scatter Plots
- Histograms
- Heatmaps
- Distribution Plots
- Business Dashboard Basics
05| Beginner Project
Time to put everything to work. You’ll finish this phase by tackling practical projects built on real datasets, so the skills you’ve learned turn into work you can show.
Projects
- Student Performance Analysis
- Sales Dashboard
- COVID Data Analysis
- Employee Dataset Analysis
06 | Machine Learning Fundamentals
This is where the machine learning course really begins. You’ll see how computers learn from data and make predictions on their own, without being told every rule step by step.
What You'll Learn
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Training and Testing Data
- Feature Selection
- Model Evaluation
- Bias and Variance
- Overfitting and Underfitting
- Cross Validation
07| Machine Learning Algorithms
Now you start building. You'll create predictive models using the algorithms companies actually rely on and learn how to compare them to pick the one that works best.
What You'll Learn
- Linear Regression
- Multiple Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
- Support Vector Machine
- Naive Bayes
- K-Means Clustering
- Model Accuracy Comparison
08| Feature Engineering
Better inputs mean better models. This module shows you how to shape raw data into features that help a model perform at its best.
What You'll Learn
- Feature Selection
- Feature Scaling
- Normalization
- Standardization
- Encoding Categorical Variables
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Feature Extraction
09| AI Tools & Automation
Today's developers don’t work alone, they work alongside AI. You'll learn how to use modern AI tools to write code faster, debug smarter, and automate the repetitive parts of you work.
What You'll Learn
- ChatGPT for Python Development
- GitHub Copilot
- AI Code Generation
- Prompt Engineering
- AI Productivity Tools
- AI Documentation
- Workflow Automation
- AI-assisted Debugging
10| Intermediate Projects
This is where your learning turns into proof. You'll work through hands-on projects from start to finish, and each one adds to the portfolio you’ll take into interviews.
Projects
- Student Performance Analysis
- Sales Dashboard
- COVID Data Analysis
- Employee Dataset Analysis
11 | Deep Learning
This is the point where the deep learning & computer vision course side of the program begins. You'll learn how neutral networks let computers recognize images, speech, and complex patters, and you’ll build and train your own.
What You'll Learn
- Introduction to Deep Learning
- Artificial Neural Networks
- Activation Functions
- Forward & Back Propagation
- TensorFlow
- Keras
- Model Training
- Model Optimization
- Hyperparameter Tuning
- Model Evaluation
12| Computer Vision
Here you teach machines to see. You’ll build applications that can understand and analyze images, from spotting objects to recognizing faces, as the computer vision half of our deep learning & computer vision.
What You'll Learn
- Image Processing Basics
- OpenCV
- Image Classification
- Object Detection
- Face Detection
- Face Recognition
- Image Segmentation
- OCR Basics
- CNN Introduction
13| Natural Language Processing (NLP)
Learn how AI understands and Ever wondered how a chatbot understands what you type? This module shows you how AI reads and makes sense of human language, and you’ll use it to build chatbots and tools that analyze text.
What You'll Learn
- Text Preprocessing
- Tokenization
- Stop Words Removal
- Stemming
- Lemmatization
- Sentiment Analysis
- Text Classification
- Named Entity Recognition
- Large Language Model Basics
- AI Chatbot Fundamentals
14| Model Deployment
A model sitting in a notebook helps no one. You'll learn how to package your machine learning models and turn them into real applications people can use, which is also your first step into the MLOps course side of the program.
What You'll Learn
- Flask Framework
- REST APIs
- API Integration
- Model Serialization
- Deployment using Streamlit
- Cloud Deployment Basics
- Docker Introduction
- Model Versioning
- Application Testing
15| AI Engineering & MLOps
Launching a model is only the start, because it needs watching, updating, and fixing overtime. This part of the MLOps course teaches you to keep AI systems running reliably in production through monitoring and automation.
What You'll Learn
- Introduction to MLOps
- Machine Learning Lifecycle
- CI/CD for ML
- Model Monitoring
- Model Retraining
- Experiment Tracking
- Data Version Control
- AI Infrastructure Basics
16| Generative AI & Large Language Models
Generative AI is behind most of the tools everyone is talking about right now. You’ll learn how large language models work and how to build your own applications on top of them, with a clear sense of how to use them responsibly.
What You'll Learn
- Introduction to Generative AI
- Large Language Models (LLMs)
- Prompt Engineering Techniques
- AI Content Generation
- AI Coding Assistants
- Retrieval-Augmented Generation (RAG) Concepts
- AI Ethics and Responsible AI
- Building AI-powered Applications
- AI Agents and Workflow Automation
Software & AI Tools Covered
Throughout the Professional Program, students will gain practical experience with:
- OpenCV
- Flask
- Streamlit
- Git & GitHub
- Docker (Introduction)
- VS Code
- ChatGPT
- GitHub Copilot
- Hugging Face
- Google Gemini
- Ollama (Introduction to Local LLMs)
Career Opportunities
After completing the Professional Program, learners can pursue roles such as:
- AI Engineer
- Machine Learning Engineer
- Data Scientist
- Data Analyst
- Deep Learning Engineer
- NLP Engineer
- Computer Vision Engineer
- AI Application Developer
- MLOps Engineer (Entry Level)
- Business Intelligence Analyst
- Python Developer (AI Focus)
- Generative AI Developer
Enquire Now
Ready to build your IT career? Learn AI-powered software courses from Jigsaw through practical projects, real-world datasets and career-focused training.
Placements
Simple steps, Limitless Possibilities
Joseph Price
Data Science
Paul Shajan
Digital Marketing
Thomas
Digital Marketing
Course Cetificate
Certificate
The AI & Machine Learning Professional Program at Jigsaw TechnoHub is built to give learners real, working skills in machine learning, deep learning, data preprocessing, modelbuilding, AI tools and deploying AI solutions for real world use.
- Understand machine learning and deep learning fundamentals
- Work with data preprocessing and feature engineering
- Build and train machine learning architectures
- Use python and ML libraries effectively
- Evaluate and optimize model performance
- Work with real world datasets and projects
- Deploy AI and ML models for practical use
- Work with Infrastructure as Code
Ready to Build Your Future?
Your journey from graduate to professional starts here. Join Jigsaw TechnoHub and gain the skills, confidence, and industry exposure needed to succeed in today's competitive job market.