Overview
Learning Path
Career Outlook
Course Overview:
The Machine Learning program helps you master the concepts of machine learning, data preprocessing, supervised and unsupervised learning, ensemble learning, regression, classification, recommendation engines, and time-series modeling. You will also learn how to implement machine learning models and use Python to draw predictions from data.
Course Highlights:
- 58 hours of applied learning
- Four industry-based course-end projects
- Interactive learning with Jupyter notebooks and integrated labs
- Dedicated mentoring session from industry experts
Course Delivery Method:
Online Bootcamp– Online self-paced Video-based learning and live virtual classroom conducted by Industry’s leading coach. This course includes Simpliearn’s Integrated lab platform.
Prerequisites:
For taking this Machine Learning program, you should have a basic understanding of statistics, mathematics, and python programming.
Skills Covered:
- Data preprocessing
- Supervised and unsupervised learning
- Time-series modeling
- Ensemble learning
- Regression
- k-means clustering
- Text mining
Learning Path
Who Will Benefit:
The Machine Learning program is best suited for analytics managers, business analysts, information architects, data scientists, and graduates looking for a career in the data science and machine learning field.
Key Learning Outcomes:
- Master the concepts of supervised and unsupervised learning, recommendation engines, and time-series modeling
- Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach
- 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 Criteria:
- At least 85 percent attendance of one live virtual classroom
- A score of at least 75 percent in course-end assessment
- Successful evaluation in the course-end project