Data Science
Beginner to Intermediate
3–6 months
End-to-end data science: statistics, Python, ML models, and how to present insights that drive business decisions.
View course detailsData & AI · Online / Offline
Machine Learning
Build and evaluate ML models — supervised and unsupervised learning with real datasets and Python libraries.
Duration3–5 months
LevelIntermediate
ModeOnline / Offline
LocationDombivli East & Online
About this course
Machine Learning lets computers learn patterns from data. This course goes deeper than introductory data science: you will build, tune and evaluate supervised and unsupervised models using Python and Scikit-learn on real datasets.
Training is available in Online and Offline modes from our Dombivli East centre at Navare Plaza. Batch timings can be discussed during counselling so the schedule fits students and working professionals.
Key highlights
Python and Scikit-learn
Build models with the most widely used ML library.
Supervised learning
Regression and classification on labelled data.
Unsupervised learning
Clustering and dimensionality reduction for unlabelled data.
Model evaluation
Measure and improve performance with the right metrics.
What you gain
- Confidence building end-to-end ML pipelines in Python.
- Understanding of when to use which algorithm.
- Skills in feature engineering, validation and tuning.
- A project demonstrating real-dataset model building.
Course syllabus
8 modules, taught step by step from fundamentals to practice.
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1
Machine Learning Overview and Python Refresher
ML types, workflow, Python, NumPy and Pandas essentials for modelling.
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2
Data Preprocessing and Feature Engineering
Handling missing values, encoding categorical data, scaling, feature selection, and creating new features.
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3
Regression Algorithms
Linear and polynomial regression, regularisation (Ridge, Lasso), and regression metrics such as MAE, MSE and R².
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4
Classification Algorithms
Logistic regression, k-NN, decision trees, random forests, SVM, naive Bayes, confusion matrix, precision, recall and ROC-AUC.
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5
Unsupervised Learning
K-means and hierarchical clustering, PCA for dimensionality reduction, and evaluating clusters.
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6
Model Validation and Tuning
Train-test split, cross-validation, overfitting and underfitting, bias-variance trade-off, grid search, and ensemble methods.
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7
Introduction to Deep Learning and Deployment Awareness
Neural network basics, saving models, and an overview of serving a trained model.
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8
Capstone Project
Choose a real dataset, build and compare models, document results, and present your findings.
Learning approach
Concepts
Coding labs
Real datasets
Model tuning
Capstone project
Tools & technologies
Py
Python
Primary language for ML.
Sc
Scikit-learn
Algorithms, pipelines and evaluation.
PN
Pandas & NumPy
Data preparation and numerical work.
MS
Matplotlib / Seaborn
Visual diagnostics and results.
JN
Jupyter Notebook
Experimentation and reporting.
Explore potential career paths
Machine Learning Engineer (Junior)
Build and test ML models.
Data Scientist (Junior)
Model data and share insights.
Data Analyst (ML-skilled)
Add predictive analysis to reporting.
AI / Analytics Associate
Support ML projects in business teams.
Career paths shown are illustrative roles that use these skills; outcomes depend on your effort, background and the job market.
Why choose CIT?
- Career-focused learning: A curriculum aligned with relevant Machine Learning skills.
- Practical approach: Exercises and hands-on practice to reinforce concepts.
- Industry-relevant tools: Learn commonly used tools for Machine Learning.
- Project-based practice: Opportunities to apply concepts to realistic problems, if included in the delivery.
- Learning for different backgrounds: A pathway designed for learners building or expanding their skills.
Who can join?
- Students with basic Python or statistics knowledge
- Data analysts moving into ML
- Working developers adding ML skills
- Learners who completed a data or Python course
Common questions
Can I attend Machine Learning Online?
Yes. This course is available in both Online (live sessions) and Offline (Navare Plaza classroom) modes.
Do I need prior experience?
This course is listed as Intermediate. Our counsellors will confirm the right starting point based on your background.
How do I get the full syllabus, fees and batch dates?
Fees, syllabus details and batch dates are shared during counselling. Call +91 99672 46501 or send an enquiry.
Start Machine Learning at CIT
Seats and batch timings vary — enquire today for the next available Online or Offline batch.