Machine Learning

Hands-on projects, Real industry tools, Career-focused training.
Everything you need to break into tech with confidence.

MACHINE LEARNING

Master Machine Learning with Python through a practical, industry-focused training program designed for students, freshers, and working professionals. Learn how to build intelligent systems using Python and modern Machine Learning techniques while working on real-world datasets and hands-on projects.

This course covers the complete Machine Learning workflow, including data preprocessing, data visualization, feature engineering, regression, classification, clustering, model evaluation, and neural networks. Gain practical experience with industry-standard tools such as Python, NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. build a strong portfolio that prepares you for careers in AI, Data Science, and Machine Learning.

Explore Our Course Modules

Module 1: Python Programming Fundamentals

  • Python Basics & Syntax
  • Variables & Data Types
  • Operators & Expressions
  • Lists, Tuples, Sets & Dictionaries
  • Functions & Modules
  • Python Best Practices

Module 2: Python Libraries for Machine Learning

  • Introduction to NumPy
  • Data Manipulation with Pandas
  • Data Visualization with Matplotlib
  • Data Visualization with Seaborn
  • Hands-on with Python Libraries

Module 3: Control Flow & Data Structures

  • Conditional Statements
  • Loops (For & While)
  • Functions & Lambda Expressions
  • Exception Handling
  • File Handling

Module 4: Data Analysis & Visualization

  • Introduction to Data Analysis
  • Exploratory Data Analysis (EDA)
  • Data Cleaning
  • Statistical Summaries
  • Visualizing Insights
  • Mini Data Analysis Project

Module 5: Machine Learning Fundamentals

  • Introduction to Machine Learning
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Model Development Workflow

Module 6: Regression Algorithms

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Model Evaluation Metrics
  • Regression Hands-on

Module 7: Classification Algorithms

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Naïve Bayes
  • Support Vector Machine (SVM)
  • Decision Tree Classifier
  •  

Module 8: Clustering Techniques

  • K-Means Clustering
  • Hierarchical Clustering
  • Cluster Evaluation
  • Real-World Clustering Examples

Module 9: Advanced Machine Learning Models

  • Decision Trees
  • Random Forest
  • Bagging
  • Boosting
  • Ensemble Learning

Module 10: Feature Engineering & Data Preprocessing

  • Data Cleaning
  • Handling Missing Values
  • Outlier Detection
  • Feature Scaling
  • Feature Selection
  • Data Transformation

Module 11: Model Evaluation & Optimization

  • Train-Test Split
  • Cross Validation
  • Hyperparameter Tuning
  • Performance Metrics
  • Model Optimization Techniques

Self Paced

Learn at your own pace

INR 7142
  • Recorded Sessions
  • Hands-on Projects
  • Certifications
  • Doubt Clear Sessions
  • Live Sessions
  • Mentor Guidence
  • Placement Support
  • 1:1 Mentoring

Mentor Led

Guided Learning with mentor support

INR 8570
  • Recorded Sessions
  • Hands-on Projects
  • Certifications
  • Doubt Clear Sessions
  • Live Sessions
  • Mentor Guidence
  • Placement Support
  • 1:1 Mentoring

Professional

Be placement ready

INR 14285
  • Recorded Sessions
  • Hands-on Projects
  • Certifications
  • Doubt Clear Sessions
  • Live Sessions
  • Mentor Guidence
  • Placement Support
  • 1:1 Mentoring

🏆 CAREER BOOSTER

Earn Industry-Recognized Certificates

Showcase your skills, strengthen your resume, and stand out to employers with industry-recognized certificates.

Tools and Technologies

Learn the industry’s most in-demand technologies

Curriculum

  • 13 Sections
  • 59 Lessons
  • 12 Weeks
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Yes, most of our courses offer a certificate of completion, provided you meet all the course requirements, such as passing quizzes and submitting assignments.
Courses often include quizzes, assignments, and sometimes final exams to assess your understanding of the material. The specific assessment methods will be detailed in the course syllabus.
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