Machine Learning Fundamentals
Core machine learning concepts, algorithms and evaluation, explained with practical examples.
- ✓ 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: Sat, 16 Jan 2027 · 40 hours · 5 days
Machine Learning & MLOpsMachine Learning Fundamentals
Duration: 40 hours · 5 days
Next batch: Sat, 16 Jan 2027
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 Fundamentals | Sat, 16 Jan 2027 | Online live | Weekend (Sat) | 6 Saturdays | Enquire |
| Machine Learning Fundamentals | Mon, 22 Mar 2027 | Bengaluru classroom | Weekday | 5 days | Enquire |
Soaring demand, and where it takes you
Machine learning literacy is increasingly expected of analysts, engineers and product teams.
Become an expert
Roles this programme prepares you for:
Become a Junior ML Engineer
Builds and evaluates baseline models.
- Explain supervised, unsupervised and reinforcement learning
- Apply regression, classification and clustering
- Engineer features from raw data
About the course
Core machine learning concepts explained practically: supervised and unsupervised learning, key algorithms, feature engineering, evaluation and avoiding common mistakes such as leakage and overfitting.
Core machine learning concepts, algorithms and evaluation, explained with practical examples.
Why should I attend?
Analysts and engineers new to ML
Product managers
Developers
Graduates
Prerequisites
Basic Python; high-school mathematics
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
- Explain supervised, unsupervised and reinforcement learning
- Apply regression, classification and clustering
- Engineer features from raw data
- Evaluate models with the right metrics
- Detect overfitting and data leakage
- Choose an algorithm for a problem
Skills you will gain
Algorithms
Practice
Judgement
Tools and platforms covered
Python
scikit-learn
pandas
JupyterCourse curriculum
M01 What ML is
- Types of learning
- ML workflow
- Use cases
- Data and labels
- Bias-variance
🧪 Hands-on: Frame ML problems
M02 Regression
- Linear regression
- Regularisation
- Metrics
- Polynomial features
- Residual analysis
🧪 Hands-on: Predict a numeric outcome
M03 Classification
- Logistic regression
- Decision trees
- Metrics
- Precision, recall, ROC
- Class imbalance
🧪 Hands-on: Build a classifier
M04 Ensembles and clustering
- Random forests
- Gradient boosting
- K-means
- Hierarchical clustering
- PCA
🧪 Hands-on: Compare models
M05 Features and validation
- Feature engineering
- Cross-validation
- Leakage
- Feature selection
- Hyperparameters
🧪 Hands-on: Improve a model safely
M06 Responsible ML
- Bias
- Explainability
- Monitoring basics
- Fairness metrics
- Model documentation
🧪 Hands-on: Review a model for fairness
M07 Neural networks primer
- Perceptrons
- Training with gradient descent
- Overfitting controls
- When deep learning helps
🧪 Hands-on: Train a small neural network and compare with a tree model
M08 Capstone: ML project
- Problem
- Model
- Evaluate
- Present
🧪 Hands-on: Deliver an end-to-end ML project with a written evaluation
Hands-on projects
Predictive model
A validated classification model with an explanation of its drivers.
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 Fundamentals. [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
Is coding required?
Basic Python helps; notebooks are provided.
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
Core machine learning concepts explained practically: supervised and unsupervised learning, key algorithms, feature engineering, evaluation and avoiding common mistakes such as leakage and overfitting.
What is Machine Learning Fundamentals?
Core machine learning concepts, algorithms and evaluation, explained with practical examples.
Who this course is for
Analysts and engineers new to ML, Product managers, Developers, Graduates.
What you will be able to do
Explain supervised, unsupervised and reinforcement learning. Apply regression, classification and clustering. Engineer features from raw data. Evaluate models with the right metrics. Detect overfitting and data leakage. Choose an algorithm for a problem.
Career paths
Roles this course supports include Junior ML Engineer, Data Analyst with ML, ML-literate Product Manager. Machine learning literacy is increasingly expected of analysts, engineers and product teams.
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