Machine Learning
This online course offers an in-depth overview of machine learning topics, including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. You will also learn how to use Python to draw predictions from data.
Program Features
Four industry-based course-end projects
Interactive learning with Jupyter notebooks integrated labs
Dedicated mentoring session from faculty of industry experts
Delivery Mode
This course consists of self-paced learning and live virtual classroom
This course requires an understanding of:
Python programming
Python for Data Science
Math Refresher
Statistics for Data Science
Target Audience
Data analysts looking to upskill
Data scientists engaged in prediction modeling
Any professional with Python knowledge and interest in statistics and math
Business intelligence developers
Key Learning Outcomes
Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
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 &and bagging techniques
Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning
Course Curriculum