Machine Learning

Machine learning is a process of letting Machine Learning to learn from the knowledge collected as human do. Machine Learning has the potential to transform each and every Industries. Due to the growth of dynamic flow of data in several fields, Machine Learning techniques are getting several heaps in optimization & Reliability.

Machine Learning is taking over the world and with that there is a growing need among companies for professionals to know in and outs of Machine learning. This course provides a concise introduction to the fundamentals concepts in Machine learning and vital Machine learning algorithms. This course also provides basics of all other supportive topics of Machine learning such as Data Cleaning, feature extraction, Startistical Inference and deploying ML model in real time.

What are the course objectives?

Machine Learning is one of the chaotic field involving several dynamical Changes over algorithms and setups but the fundamental principles and knowledge kept unchanged irrespective of its evolution. This course will help people to experience those mathematica principle with real time experience and practices.

Who should do this course?

This Course is been designed for people with some intermediate knowledge on computer science engineering and well-suited basics on Mathematics concepts with some logical thinking. This course will help you in familiarizing sufficient theoretical concepts and applying those in some real-life datasets.

Job Opportunity for information?

Machine Learning is one of the buzz word that circulating IT industries and also other companies operating in digital side and hence making it one of the hottest and promising job for an another decade.

Course Duration Information

Program Schedule

• Mode: Face to face training in batches with a maximum batch size of 5
• Total number of hours: 60 hours; Weekday Classes; 2 hours/ Day; 5days/Week

Part 1: Introduction and Basics

• What is Learning?
• Types of Learning.
• Mathematical Basics of Machine Learning.
• Course Plan.

Part 2: Python for Machine Learning:

• Basics of Python.
• Python for Modelling Mathematical Concepts.
• Working with Data.
• Frameworks & Libraries for Machine Learning.

Part 3: Regression:

• Linear Algebra Review.
• Simple & Linear Regression.
• Logistic Regression.
• Multivariate Regression.

Part 4: Classification

• Decision Trees
• KNN
• Naive Bayes
• Kernel Methods.

Part 5: Unsupervised

• Intro to Unsupervised learning.
• K-Means Clustering.
• Hierarchical Clustering.
• Dimensionality Reduction.

Part 6: Advanced

• Performance Optimization.
• Neural Networks.
• Deploying ML models in Real Time.

Part 7: Conclusion

• How to Work with GIT
• Research Paper on Machine Learning.
• How to proceed further.

At end of our course, you will be assigned to work a real-time project. Once you completed assigned project with expected results we (Experts Team from Relearn Pro) will verify and issue information Certificate. If you are not able to deliver expected results in project we will support you by clarifying doubts and help you to re-attempt the project.