JFH Foundation
Description
About The Course
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.
This course is brought to you by JobsForHer Foundation in association with Simplilearn.
Course Key Features:
- 58 hours of blended learning
- 14 hours of Online self-paced learning
- 44 hours of instructor-led training
- Four industry-based course-end projects
- Interactive learning with Jupyter notebooks integrated labs
- Dedicated mentoring session from faculty of industry experts
Course Duration: 4-5 weeks
Course Start Date: Shortlisted candidates will be notified via email
Certificate: Upon completion of the course
Program Fees: This course is priced at Rs. 23625. But those who are shortlisted for the scholarship will get this FREE of cost.
Online or Offline: Online Bootcamp: Online self-learning and live instructor-led classes
Who Should Enroll
- Data analysts looking to upskill
- Data scientists engaged in prediction modeling
- Any professional with Python knowledge an interest in statistics and math Business intelligence
- Developers
Prerequisites: This course requires an understanding of:
Statistics
Mathematics
Python programming
Knowledge of these fundamental courses:
Python for Data Science
Math Refresher
Statistics for Data Science
Takeways
Course Takeaways
- 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
Certification Details and Criteria:
85 percent completion of online self-paced learning
