Machine Learning with Python
Hands-on ML with pandas and scikit-learn, from data preparation to tuned, validated models.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Mon, 07 Dec 2026 · 40 hours · 5 days
Machine Learning & MLOpsMachine Learning with Python
Duration: 40 hours · 5 days
Next batch: Mon, 07 Dec 2026
Format: Online live · Bengaluru classroom
Modules: 8 · Topics: 38
Level: Beginner to Intermediate
Fee: ₹17,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Machine Learning with Python | Mon, 07 Dec 2026 | Bengaluru classroom | Weekday | 5 days | Enquire |
| Machine Learning with Python | Sat, 06 Feb 2027 | Online live | Weekend (Sat) | 6 Saturdays | Enquire |
Soaring demand, and where it takes you
Python and scikit-learn are a standard toolkit for applied machine learning in industry.
Become an expert
Roles this programme prepares you for:
Become a ML Engineer
Builds and tunes models in Python.
- Prepare data with pandas and scikit-learn
- Build leak-free pipelines with preprocessing
- Compare models with cross-validation
About the course
Hands-on machine learning in Python: data preparation with pandas, scikit-learn pipelines, model selection and hyperparameter tuning, handling imbalanced data and saving models for reuse.
Hands-on ML with pandas and scikit-learn, from data preparation to tuned, validated models.
Why should I attend?
Python users
Analysts
Data engineers
Developers moving into ML
Prerequisites
Python and pandas basics
Level
Beginner to Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 40 hours · 5 days
Course fee (open batch): ₹17,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Prepare data with pandas and scikit-learn
- Build leak-free pipelines with preprocessing
- Compare models with cross-validation
- Tune hyperparameters with grid and random search
- Handle imbalanced classes
- Save, load and reuse trained models
Skills you will gain
Data prep
Modelling
Delivery
Tools and platforms covered
Python
pandas
NumPy
scikit-learn
JupyterCourse curriculum
M01 Python ML stack
- NumPy and pandas
- scikit-learn API
- Workflow
- Matplotlib for EDA
- Reproducibility
🧪 Hands-on: Set up a project
M02 Data preparation
- Missing values
- Encoding
- Scaling
- Outliers
- Feature creation
🧪 Hands-on: Prepare a dataset
M03 Pipelines
- ColumnTransformer
- Pipeline
- Avoiding leakage
- Custom transformers
- Pipeline persistence
🧪 Hands-on: Build a pipeline
M04 Model selection
- Cross-validation
- Comparing algorithms
- Metrics
- Learning curves
- Gradient boosting libraries
🧪 Hands-on: Compare models
M05 Tuning
- Grid and random search
- Imbalanced data
- Thresholds
- Bayesian search overview
- Calibration
🧪 Hands-on: Tune a classifier
M06 Delivery
- Evaluation reports
- Saving models
- Simple API
- SHAP explanations
- Model cards
🧪 Hands-on: Package the model
M07 Text and time series
- TF-IDF
- Text classification
- Lag features
- Time-based validation
🧪 Hands-on: Build a text classifier and a demand forecaster
M08 Capstone: production-ready model
- Data
- Pipeline
- Tuning
- API demo
🧪 Hands-on: Ship a tuned pipeline behind a simple API
Hands-on projects
End-to-end ML pipeline
A tuned scikit-learn pipeline with an evaluation report and saved model.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Machine Learning with Python. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
How much Python is needed?
Comfort with basic Python and pandas.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹17,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
40 hours · 5 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
Hands-on machine learning in Python: data preparation with pandas, scikit-learn pipelines, model selection and hyperparameter tuning, handling imbalanced data and saving models for reuse.
What is Machine Learning with Python?
Hands-on ML with pandas and scikit-learn, from data preparation to tuned, validated models.
Who this course is for
Python users, Analysts, Data engineers, Developers moving into ML.
What you will be able to do
Prepare data with pandas and scikit-learn. Build leak-free pipelines with preprocessing. Compare models with cross-validation. Tune hyperparameters with grid and random search. Handle imbalanced classes. Save, load and reuse trained models.
Career paths
Roles this course supports include ML Engineer, Data Scientist, Applied Analyst. Python and scikit-learn are a standard toolkit for applied machine learning in industry.
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