Industry Skills Classroom Training

Machine Learning

This course help you walk out not just with knowledge — but with the confidence, skills, and real project experience to land a Machine Learning Engineer role. You will be fully prepared to crack interviews, handle real-world ML problems, and deliver results from Day 1 on the job.

4.9 (12 ratings)
36 enrolled 6 Months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python & SQL for Machine Learning
  • Build & Deploy Real-World ML Models
  • Explore Data Like a Pro with EDA Techniques
  • Solve Complex Problems with Advanced ML Algorithms
  • Work on Industry-Based Case Studies & Live Projects
  • Go from Job Seeker to Job-Ready ML Engineer

Requirements

  • Basic Computer Skills
  • No previous coding knowledge needed
  • Suitable for both IT & Non-IT domain

Tools & Technologies

Python Python
PostgreSQL PostgreSQL
Pandas Pandas
NumPy NumPy
Matplotlib Matplotlib
Seaborn Seaborn
Scikit-Learn Scikit-Learn
Fast API Fast API
Streamlit Streamlit
Docker Docker

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about breaking into the Machine Learning field. This course is structured to take you from zero to job-ready — giving you the confidence to crack interviews, walk into any company, and start performing from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in Machine Learning
  • Job Seekers Who Want to Build Real ML Skills Beyond Online Courses
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready ML Engineer

Course Curriculum

15 modules • 138 lessons
Module 1: Python for ML Engineer 11 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Python Libraries Overview for ML
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: SQL for ML Engineer 11 lessons
  • Introduction to Databases & SQL
  • SQL Syntax & Query Structure
  • Filtering & Sorting Data
  • Aggregate Functions & Grouping
  • Join Operations
  • Subqueries & Nested Queries
  • Window Functions
  • Common Table Expressions (CTEs)
  • Data Cleaning & NULL Handling
  • Extracting ML-Ready Datasets from SQL
  • Module 2 Clearance Test
Module 3: Feature Engineering 13 lessons
  • Introduction to Feature Engineering
  • Data Collection & Loading with Pandas
  • Handling Missing Values & Outliers
  • Data Cleaning & Noise Removal
  • Data Type Conversion & Formatting
  • Encoding Categorical Variables
  • Feature Scaling & Normalization
  • Feature Selection Techniques
  • Feature Extraction & Dimensionality Reduction
  • NumPy for Numerical Feature Operations
  • Scikit-Learn Preprocessing Pipeline
  • Imbalanced Data Handling - SMOTE & Resampling
  • Module 3 Clearance Test
Module 4: Exploratory Data Analysis 10 lessons
  • Introduction to EDA & Its Importance in ML
  • Descriptive Statistics & Data Profiling
  • Univariate & Bivariate Analysis
  • Correlation & Relationship Analysis
  • Distribution & Outlier Analysis
  • Visualization with Matplotlib
  • Visualization with Seaborn
  • AutoViz & Automated EDA Tools
  • EDA Report Generation
  • Module 4 Clearance Test
Module 5: Regression Models 12 lessons
  • Introduction to Supervised Learning & Regression
  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Ridge & Lasso Regression
  • ElasticNet Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • Gradient Boosting & XGBoost Regressor
  • Model Evaluation - MAE, MSE, RMSE, R2 Score
  • Cross Validation & Hyperparameter Tuning
  • Module 5 Clearance Test
Module 6: Classification Algorithms 11 lessons
  • Introduction to Classification
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier
  • Decision Tree Classifier
  • Random Forest Classifier
  • Gradient Boosting & XGBoost Classifier
  • Model Evaluation - Confusion Matrix, Precision, Recall, F1, ROC-AUC
  • Cross Validation & Hyperparameter Tuning
  • Module 6 Clearance Test
Module 7: Clustering Techniques 8 lessons
  • Introduction to Unsupervised Learning & Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN Clustering
  • Gaussian Mixture Models
  • Cluster Evaluation - Silhouette Score & Elbow Method
  • Real-World Clustering Use Cases
  • Module 7 Clearance Test
Module 8: Recommendation Model 7 lessons
  • Introduction to Recommendation Systems
  • Content-Based Filtering
  • Collaborative Filtering
  • Matrix Factorization Techniques
  • Hybrid Recommendation Systems
  • Building a Real-World Recommendation Engine
  • Module 8 Clearance Test
Module 9: Time Series Algorithms 9 lessons
  • Introduction to Time Series Analysis
  • Time Series Components & Decomposition
  • Stationarity & Differencing
  • ARIMA & SARIMA Models
  • Exponential Smoothing Methods
  • Prophet Model
  • LSTM for Time Series Forecasting
  • Model Evaluation for Time Series
  • Module 9 Clearance Test
Module 10: NLP & LLM Foundation 12 lessons
  • Introduction to Natural Language Processing
  • Text Preprocessing & Cleaning
  • Tokenization, Stemming & Lemmatization
  • Bag of Words & TF-IDF
  • Word Embeddings - Word2Vec & GloVe
  • Sentiment Analysis
  • Text Classification
  • Named Entity Recognition (NER)
  • Introduction to Large Language Models (LLMs)
  • Prompt Engineering Basics
  • Using Pre-trained LLMs via API
  • Module 10 Clearance Test
Module 11: Neural Networks - CNN & RNN 12 lessons
  • Introduction to Deep Learning & Neural Networks
  • Perceptron & Multilayer Neural Networks
  • Activation Functions, Loss Functions & Optimizers
  • Backpropagation & Gradient Descent
  • Introduction to TensorFlow & Keras
  • Building & Training ANN Models
  • Convolutional Neural Networks (CNN)
  • CNN for Image Classification
  • Recurrent Neural Networks (RNN)
  • LSTM & GRU Networks
  • Overfitting, Dropout & Regularization
  • Module 11 Clearance Test
Module 12: ML Pipeline & Model Deployment 7 lessons
  • Introduction to ML Pipelines
  • Building End-to-End ML Pipeline with Scikit-Learn
  • Model Saving & Loading - Pickle & Joblib
  • Building ML API with FastAPI
  • Deploying ML Model with Streamlit
  • Introduction to Docker for ML
  • Module 12 Clearance Test
Module 13: Model Monitoring & Performance 6 lessons
  • Introduction to Model Monitoring
  • Data Drift & Concept Drift Detection
  • Model Performance Tracking & Logging
  • Retraining Strategies & Model Versioning
  • Introduction to MLflow for Experiment Tracking
  • Module 13 Clearance Test
Module 14: Version Control & ML Project Structure 7 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • ML Project Folder Structure Best Practices
  • Writing README & Documenting ML Projects
  • Building a GitHub Portfolio for ML
  • Module 14 Clearance Test
Module 15: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • End-to-End ML Training, Not Just Theory
  • Classroom Training with Real Mentor Guidance
  • Build a Portfolio That Gets You Hired

Your Instructors

Leelavathi Ramesh

Leelavathi Ramesh

Business Intelligence Analyst

9 Courses

4+ years of experience as a Business Intelligence Analyst, with hands-on expertise at Variablz Technology and Schneider Electric. Statistics graduate with practical experience across Marketing, Production, and Sales Analytics.

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

17 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

Course Outcomes

What Our Students Build

Articles, projects, research papers and more — real work by our learners applied beyond the classroom.

View All Outcomes
FAQ

Frequently Asked Questions

Is this course suitable for freshers with no ML experience?

Yes. This course starts from the basics and gradually takes you to advanced ML concepts u2014 no prior ML experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as a Machine Learning Engineer.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world business problems.

Will this course help me crack ML interviews?

Absolutely. Every module is structured around what recruiters and companies actually test u2014 from core algorithms to model deployment and GitHub portfolio building.

What kind of companies can I apply to after this course?

You will be job-ready for service-based companies, product startups, and analytics firms across Tamil Nadu and South India hiring for Junior ML Engineer, ML Developer, Data Scientist, and AI Engineer roles.

Do I need to know programming before joining?

No. Python is covered from scratch in the first module u2014 you will be guided step by step from the basics to advanced ML coding.

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