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