Machine Learning Course

Simplilearn’s Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

Key Features of Machine Learning Course

• 44 hours of instructor-led training with certification
• Gain expertise with 25+ hands-on exercises
• 4 real-life Industry projects with integrated labs
• Lifetime access to self-paced learning
• Dedicated mentoring session from our faculty of industry experts
• 24*7 support with dedicated project mentoring sessions

What are the objectives of Machine Learning Course?
This Machine Learning online course will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

What skills will you learn with this Machine Learning Course?

• Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
• Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
• Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
• Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
• Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
• Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

Who should take this Machine Learning Training Course?