Machine Learning
Learn how machines can learn from data and make intelligent decisions. Understand the core concepts of supervised, unsupervised, and reinforcement learning. Gain hands-on experience building predictive models using Python and popular ML libraries. Work on real-world projects to solve business, healthcare, and finance problems. Equip yourself with in-demand skills to excel as a Machine Learning Engineer or Data Scientist.
Course Overview
You’ll start with foundational concepts including data preprocessing, regression, and classification.
Progress to advanced techniques such as clustering, dimensionality reduction, ensemble learning, and recommendation systems.
Hands-on projects allow you to apply algorithms to real-world datasets and evaluate model performance.
You’ll use Python libraries such as scikit-learn, Pandas, and NumPy to implement ML solutions.
By the end of the course, you’ll have the knowledge and practical experience to launch a career in machine learning and AI.
what you will learn
- Understand key Machine Learning concepts including supervised, unsupervised, and reinforcement learning.
- Build and optimize models for regression, classification, and clustering problems.
- Implement advanced algorithms like ensemble methods, PCA, and recommendation systems.
- Gain hands-on experience with Python, scikit-learn, Pandas, and NumPy.
- Work with real-world datasets to evaluate and improve model performance.
- Develop end-to-end ML projects to showcase in your professional portfolio.
- Understand model deployment and best practices for production-ready ML solutions.
- Prepare for roles such as Machine Learning Engineer, Data Scientist, or AI Specialist.
Next Cohort Starts On 30 Nov
Skilled Covererd
- Supervised and Unsupervised Machine Learning Techniques
- Regression, Classification, Clustering, and Recommendation Systems
- Model Evaluation, Optimization, and Feature Engineering
- Python and scikit-learn Implementation
- Working with Real-World Datasets
- End-to-End ML Project Development
Benefits
The certification can help you land lucrative roles in IT, manufacturing, finance, healthcare, and other exciting industries certified project managers drive better project performance and are often rewarded with substantial pay raises as shown below.
Designation
Annual Salary
Hiring Companies
Course Curriculum
Introduction to ML
Machine Learning definitions
Applications
Types of Machine Learning
Python for ML
EDA & Data Preprocessing
Feature Engineering
Linear & Logistic Regression
KNN & SVM
Decision Trees & Random Forests
Model Evaluation
Unsupervised Learning
PCA & t-SNE
Association Rule Mining
Ensemble Methods
Cross Validation & Tuning
Model Deployment Basics
Capstone Project – Part 1
Capstone Project – Part 2
Capstone Project – Part 3
ML Interview Prep
Machine Learning FAQ
No, the course starts with fundamentals and gradually progresses to advanced techniques.
You’ll work with Python, scikit-learn, Pandas, and NumPy for building and evaluating ML models.
Yes, the course includes multiple real-world projects to implement ML algorithms and test models.
Absolutely. It is designed to guide learners from basic concepts to advanced applications in machine learning.
You’ll acquire job-ready ML skills, enhancing opportunities in data science, AI, and analytics roles across industries.
Eligibility
- Gain in-demand skills for careers in AI, data science, and analytics.
- Build hands-on experience with real datasets and practical ML projects.
- Strengthen your technical portfolio with predictive modeling and data-driven solutions.
- Learn cutting-edge machine learning techniques used in top tech companies.
- Open doors to career opportunities in healthcare, finance, e-commerce, and more.
- Increase employability and earning potential with highly sought-after ML skills.
- Develop problem-solving and analytical thinking applicable across industries.
- Lay the foundation for advanced AI and deep learning specialization.
Pre-requisites
- Basic knowledge of Python programming.
- Familiarity with mathematics, especially linear algebra, probability, and statistics.
- Some exposure to data analysis concepts is helpful but not required.
- Curiosity and motivation to solve real-world problems using machine learning.
Contact Us
1800-212-7688
(Toll Free)
Why join this program
Develop skills for real career growth
Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
Learn from experts active in their field, not out-of-touch trainers
Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
Learn by working on real-world problems
Capstone projects involving real world data sets with virtual labs for hands-on learning
Structured guidance ensuring learning never stops
24×7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts
