Program Overview:
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:
- 14 hours of Online self-paced learning
- Four industry-based course-end projects
- Interactive learning with Jupyter notebooks integrated labs
- Dedicated mentoring session from faculty of industry experts
Delivery Mode:
Online self-paced learning
Prerequisites:
This course requires an understanding of:
- Statistics
- Mathematicss
- Python programming
Knowledge of these fundamental courses:
- Python for Data Science
- Math Refresher
- Statistics for Data Science
Target Audience:
- Data analysts looking to upskil
- 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
- Business intelligence developers
- 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 or attendance of one live virtual classroom
- A score of at least 75 percent in the course-end assessment
- Successful evaluation in at least one project
Course Curriculum:
Lesson 01 – Course Introduction
- Course Introduction
Lesson 02 – Introduction to AI and Machine Learning
- Learning Objectives
- The emergence of Artificial Intelligence
- Artificial Intelligence in Practice
- Recommender Systems
- Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part A
- Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part B
- Definition and Features of Machine Learning
- Machine Learning Approaches
- Machine Learning Techniques
- Applications of Machine Learning – Part A
- Applications of Machine Learning – Part B
- Key Takeaways
Lesson 03 – Data Preprocessing
- Learning Objectives
- Data Exploration: Loading Files
- Demo: Importing and Storing Data
- Practice: Automobile Data Exploration I
- Data Exploration Techniques: Part 1
- Data Exploration Techniques: Part 2
- Seaborn
- Demo: Correlation Analysis
- Practice: Automobile Data Exploration II
- Data Wrangling
- Missing Values in a Dataset
- Outlier Values in a Dataset
