AI5 PROFESSIONAL CERTIFICATE · BY TGC INDIA · UPDATED AUGUST 2026
Machine Learning with Python
Learn Python, data preparation, supervised learning, unsupervised learning and model deployment through projects.
Online + OfflineWeekdays + WeekendsRegular + Fast Track
22+years in training
20,000+learners across TGC
5classroom locations
Live onlinejoin from anywhere
NEXT START OPTIONS
Choose a live batch.
COURSE OVERVIEW
What this course
is built to do.
Move from raw data to trained, tested and deployed models through Python exercises and business-led projects.
Each module combines a live trainer demonstration, guided lab, assignment and review before the next stage.
CONNECTED CAREER DIRECTIONS
Machine Learning AssociateJunior Data ScientistPython Data AnalystModel Testing Associate
IS THIS COURSE RIGHT FOR YOU?
Choose it for the right reason.
You know basic Python and want applied ML
You need model evaluation, not only notebooks
You want projects you can explain in interviews
YOUR LEARNING ARC
From guided foundation to finished work.
Data
Clean, inspect and prepare datasets.
Models
Train baseline and stronger supervised models.
Evaluation
Compare metrics, errors and generalisation.
Delivery
Package a model and explain its use limits.
INDUSTRY TASKS
Practise the work, not only the tool.
- Build a prediction baseline
- Diagnose model errors and leakage
- Serve a trained model through an API
PRO
LED
LED
YOUR TRAINING TEAM
Learn from experienced working professionals.
Live demonstrations, guided practice and direct project feedback are part of the course. Trainers update examples and tool coverage as professional practice changes.
WHAT YOU WILL BE ABLE TO DO
Course outcomes
01
Prepare and investigate real data
A reproducible analysis notebook with data-quality decisions.
02
Train and compare machine-learning models
A baseline, improved model and justified selection.
03
Evaluate errors and limitations
A metric-led error analysis and explainability report.
04
Deploy and defend a working project
A usable ML application and interview-ready case study.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
DETAILED COURSE FLOW
5 learning modules
Progress from data preparation and baseline models to evaluation, deployment and an interview-ready machine-learning capstone.
Python, data and statistics foundation
Build the coding and quantitative base needed to work confidently with imperfect datasets.
CORE TOPICS
- Python, NumPy and Pandas workflows
- Data cleaning and exploratory analysis
- Probability and descriptive statistics
- Visualisation and reproducible notebooks
GUIDED PRACTICE
Clean and investigate a dataset, document assumptions and present its important patterns.
MODULE DELIVERABLE
Exploratory data analysis notebook
Supervised machine learning
Train useful prediction and classification models from a clear baseline.
CORE TOPICS
- Train, validation and test strategy
- Regression and classification algorithms
- Feature preparation and pipelines
- Underfitting, overfitting and regularisation
GUIDED PRACTICE
Build baseline and improved models for a business prediction problem.
MODULE DELIVERABLE
Supervised model comparison
Evaluation and model improvement
Choose metrics that match the real problem and diagnose why a model fails.
CORE TOPICS
- Classification and regression metrics
- Cross-validation and hyperparameter search
- Leakage, imbalance and error analysis
- Explainability and responsible interpretation
GUIDED PRACTICE
Analyse errors by customer or data segment and explain the trade-offs behind the selected model.
MODULE DELIVERABLE
Model evaluation and error report
Unsupervised learning and applied patterns
Discover structure in data and build common recommendation and anomaly workflows.
CORE TOPICS
- Clustering and dimensionality reduction
- Customer segmentation
- Recommendation approaches
- Anomaly and fraud-screening patterns
GUIDED PRACTICE
Create a segmentation or recommendation project and test whether the result is useful to a decision-maker.
MODULE DELIVERABLE
Applied unsupervised learning project
Deployment and ML capstone
Turn a notebook model into a usable application with documented limits.
CORE TOPICS
- Model packaging and inference pipelines
- Streamlit or API-based delivery
- Versioning, monitoring and drift concepts
- Portfolio narrative and interview defence
GUIDED PRACTICE
Deploy a model interface, test it with new data and present design choices, errors and limitations.
MODULE DELIVERABLE
Deployed ML application and capstone case study
HOW THE TRAINING WORKS
Learn it. Apply it. Get it reviewed. Improve it.
Every important skill moves through explanation, demonstration, guided use and independent application. Trainer feedback is used to revise the work before it becomes part of the final portfolio.
Concept briefing
The trainer explains the principle, use case, limitations and the quality standard expected.
Live demonstration
A complete task is demonstrated while the trainer explains decisions, checks and common mistakes.
Guided lab
Learners repeat the method with support, ask questions and correct problems during the session.
Applied assignment
The same method is used on a different brief so the learner must make independent decisions.
Review and revision
Work is checked against a rubric, revised after feedback and prepared for project presentation.
PROGRESS IS CHECKED THROUGHClass exercisesModule deliverablesProject reviewsFinal capstone presentation
PORTFOLIO WORK
Projects you can show
Customer churn
Plan, produce, test and present a finished piece with trainer feedback.
Sales forecast
Plan, produce, test and present a finished piece with trainer feedback.
Recommendation model
Plan, produce, test and present a finished piece with trainer feedback.
Fraud screening
Plan, produce, test and present a finished piece with trainer feedback.
Model dashboard
Plan, produce, test and present a finished piece with trainer feedback.
ML capstone
Plan, produce, test and present a finished piece with trainer feedback.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
TOOLS COVERED
PythonPandasscikit-learnJupyterStreamlit
Tool coverage may be updated when the industry changes. Core methods remain part of the course.COMMON QUESTIONS
Before you apply
How much mathematics is required?
School-level algebra and statistics are enough to begin; the required concepts are taught with code.
＋Will I use real datasets?
Yes. Labs and projects use imperfect data that requires checking and preparation.
＋Does the course cover deployment?
Yes. You will package and expose a model in a guided final stage.
＋Do I need coding experience?
No, unless the course level says otherwise. Your advisor will check the right starting level.
＋Are classes live or recorded?
Classes are trainer-led in the classroom or live online. Recordings may support revision but do not replace class.
＋Will I receive a certificate?
Yes. Course completion requires attendance, assignments and the final project.
＋Can working professionals join?
Yes. Weekday, weekend and selected fast-track schedules are available.
＋NEXT BATCH
Choose your course.
Choose your schedule.
Online or offline. Weekdays or weekends. Regular or fast track. Speak with an AI5 Academy advisor about the right starting level.