Machine Learning – ML

Course Overview

• Course Duration:1.5 Months (Including Lab Work, Internship, and real-world assignment).
• Modes of Training: Online Classes/Offline Training (at selected centers).
• Projects:Available – Real-world ML applications and projects.

Machine Learning Certification Training Course

• Learn the core concepts and workflow of Machine Learning (ML).
• Understand supervised, unsupervised, and reinforcement learning techniques.
• Implement algorithms such as Linear Regression, Decision Trees, and K-Means Clustering.
• Train, test, and optimize ML models using Python and Scikit-Learn.
• Work on real-world datasets and build predictive systems.
• Prepare for the Global Machine Learning credential preparation assessment with hands-on projects.

Overview of Machine Learning Certification Training Course

The Machine Learning credential preparation Training Course is designed for data enthusiasts, software engineers, and professionals who want to build smart applications using AI and ML techniques. The course provides in-depth training on algorithms, model evaluation, and data pre-processing, providing you with the specialized technical expertise needed to advance your career in the rapidly evolving world of artificial intelligence.

Through real-world projects and guided exercises, you'll gain hands-on experience in predictive modeling, classification, and clustering. By the end of the program, you'll be ready to earn your Machine Learning Professional credential preparation and advance your career in AI, Data Science, or Analytics.

1.Introduction to Machine Learning

• Types of Machine Learning.
• Real-life ML applications.
• Numpy, Pandas, Matplotlib.

3. Supervised Learning

• Regression Techniques.
• Classification Algorithms.

4. Unsupervised Learning

• Clustering Algorithms.
• Dimensionality Reduction.

5. Neural Networks & Deep Learning

• Basics of Neural Networks.
• Recommendation Systems.
• Time Series Forecasting.

7. Final Project & Internship

• Industry-based Internship.
• Beginner to advanced ML modules.
• Hands-on coding with real-world datasets.
• Tools: Scikit-learn, TensorFlow, Pandas.
• Case study-based learning approach.
• Interview prep and resume guidance.
• Machine Learning Engineer.
• Gain hands-on experience by working on predictive modeling, classification, and clustering problems using real datasets and Python libraries.
• Develop an end-to-end machine learning model and deploy it in a simulated business environment for hands-on exposure.
• Stay ahead with trending ML algorithms and hands-on business applications used in top tech companies.
• Get resume reviews, interview coaching, and career outcome support to start your career in Machine Learning and AI.
• Access one-on-one guidance from experienced machine learning professionals for doubt resolution and career tips.