Exploratory Data Analysis for Machine Learning
This course ranks 8th in the top best online Machine Learning courses. This is the first course of the IBM Machine Learning Professional Certificate, and it introduces you to Machine Learning and the professional certificate's content. This course will teach you the value of solid, high-quality data. You'll learn how to acquire data, clean it, apply feature engineering, and prepare it for preliminary analysis and hypothesis testing using popular methodologies.
You should be able to do the following by the conclusion of this course:
- Obtain data from a variety of sources: APIs, Cloud, SQL, NoSQL databases
- Describe and use standard feature engineering and feature selection strategies.
- Handle missing values, as well as categorical and ordinal attributes.
- Outliers may be detected and dealt with using a number of ways.
- Explain why feature scaling is necessary and how to apply different scaling approaches.
This course is designed for aspiring data scientists who want to learn how to use Machine Learning and Artificial Intelligence in a corporate context. To get the most out of this course, you should be comfortable programming in the Python environment and have a basic grasp of Calculus, Linear Algebra, Probability, and Statistics.
Syllabus:
- WEEK1 (2 hours to complete): A Brief History of Modern AI and its Applications
- WEEK 2 (3 hours to complete): Retrieving and Cleaning Data
- WEEK 3 (5 hours to complete): Exploratory Data Analysis and Feature Engineering
- WEEK 4 (3 hours to complete) :Inferential Statistics and Hypothesis Testing
- WEEK 5 (1 hour to complete): (Optional) HONORS Project
Provider: Coursera
Cost: Premium
Rate: 4.6/5
Enroll here: https://tinyurl.com/yet7chmv
