Online Live Training
Real-time coding & model-building.
House Price Prediction Model
Train regression model & interpret feature importance.
Machine Learning
Machine Learning Training & Placement Program
Learn to build predictive models, automate decision-making, and solve real business problems using Machine Learning algorithms and data-driven techniques.
Beginner to Advanced ML Learning Path
4,500+ Students Trained
Machine Learning enables systems to learn patterns from data and make predictions. This course trains you to analyze datasets, build ML models, tune performance, evaluate metrics, and deploy solutions. You will learn Python, Pandas, NumPy, Scikit-Learn, Feature Engineering, Model Selection, Hyperparameter Tuning, and Practical Deployment. Ideal for Data Science and AI career tracks.
Designed for freshers & IT/non-IT career changers
Hands-on projects using real business datasets
100%
Practical Model Training Labs
3 Live
End-to-End Machine Learning Projects
60+ Hrs
Feature Engineering & Model Exercises
24/7
Lifetime Access to Notes & Recordings
What You Will Learn in the Machine Learning Course
A structured journey to build, train, evaluate, and optimize predictive models.
Python for Machine Learning (Foundation)
1.5 Weeks
Python Syntax & Data Structures
Working with Pandas & NumPy
Data Cleaning, Merging & Transformation
Statistics & Data Understanding
1–1.5 Weeks
Mean, Variance, Probability
Distributions & Hypothesis Testing
Data Patterns & Interpretation
Exploratory Data Analysis (EDA)
1.5 Weeks
Outlier Detection & Handling
Missing Data Treatment
Feature Relationships & Insight Extraction
Data Visualization (Matplotlib, Seaborn)
Machine Learning Algorithms (Supervised & Unsupervised)
2.5–3 Weeks
Linear & Logistic Regression
Decision Trees & Random Forest
Gradient Boosting Models (XGBoost / LightGBM)
Support Vector Machine (SVM)
K-Means Clustering (Unsupervised)
Model Evaluation & Performance Tuning
1 Week
Confusion Matrix & Accuracy Metrics
Precision, Recall, F1 Score & ROC-AUC
Hyperparameter Tuning (GridSearch / RandomSearch)
Cross-Validation Strategies
Capstone Project + Interview Preparation
1–2 Weeks
Build and improve a real ML model
Create insights & present business outcomes
Upload project to GitHub + Resume Case Study
Mock Interviews + ML Scenario Questions
TRAINER PROFILE
Senior Machine Learning Engineer & Data Science Mentor
8+ Years’ experience in predictive modeling, ML-driven automation, financial analytics & enterprise AI systems.
Trained 4,500+ learners transitioning into ML & data science careers.
Focus: Model clarity, step-by-step workflow, and interview success.
Speak with Mentor @ +91 9344259572
CURRICULUM BREAKDOWN
Core Subjects
- Python
- Statistics
- Data Cleaning & EDA
- Machine Learning Algorithms
- KPI Interpretation
- Insight Presentation
- Dashboard Narrative
Advanced Concepts
- Feature Engineering
- Model Tuning & Validation
- ML Deployment Basics (Flask/Streamlit)
MODES OF TRAINING
Flexible learning formats to suit your schedule & goals
Classroom Training
System-based hands-on practice sessions.
One-to-One Mentoring
Personalized learning & project review.
Corporate Upskilling
Corporate ML Upskilling Domain-specific ML training for organizations.
CAREER PATHS
This course prepares learners for job-ready ML & Data Science roles.
Machine Learning Engineer
Data Scientist (Entry-Level)
Data Analyst (with ML capability)
AI Associate (with Deep Learning add-on)
RELEVANT TOOLS & TECHNOLOGIES
PROGRAM FEATURES
Comprehensive learning experience designed for end-to-end ML mastery
End-to-End Practical ML Workflows
Learn model building from raw data to deployment and gain complete project exposure.
Real Business Use-Case Projects
Implement machine learning projects based on real-world datasets for your portfolio.
Resume, Interview & Mock Sessions
Get HR, managerial, and technical interview preparation along with mock sessions.
Beginner Friendly Step-by-Step Training
No coding background required — learn machine learning concepts from zero to expert level.
ACCORDION DETAILS
Top Skills
Top Skills You Will Learn
Top Skills You Will Learn
Data cleaning, exploratory data analysis (EDA), ML algorithm selection, model evaluation, hyperparameter tuning, presentation, and deployment fundamentals.
Jobs
Job Opportunities
Job Opportunities
Strong demand across IT, finance, healthcare, telecom, e-commerce, SaaS, analytics, and consulting industries for data and ML professionals.
Audience
Who Should Enroll
Who Should Enroll
Freshers, working professionals, aspiring data analysts, science and computer graduates, and non-IT aspirants looking to move into data-driven roles.
Eligibility
Eligibility Criteria
Eligibility Criteria
No prior coding or statistics background required — this course starts from absolute fundamentals.
PROJECTS YOU WILL WORK ON
Customer Churn Prediction
Customer Churn Prediction Classification model to identify retention risk.
Capstone ML Project
End-to-end pipeline → GitHub → Resume explanation → Interview practice.