Exploratory Data Analysis for Machine Learning

Go to Course: https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning

Introduction

**Course Review and Recommendation: Exploratory Data Analysis for Machine Learning** **Overview:** The "Exploratory Data Analysis for Machine Learning" course, part of the IBM Machine Learning Professional Certificate, serves as an essential introduction to the world of machine learning and the dual realities of data-centric practices and analytical thinking. As the first course in this professional certificate, it lays a solid foundation for those looking to navigate the complexities of machine learning in real-world applications. A critical take-away from this course is the realization that high-quality data is the backbone of successful machine learning projects. The course emphasizes various processes involved in retrieving and cleaning data, enhancing its usability through techniques like feature engineering, and preparing it for meaningful analysis and hypothesis testing. **What You Will Learn:** By the conclusion of this course, participants will possess practical skills in the following areas: 1. **Data Retrieval:** Students will become adept at retrieving data from various sources, including SQL and NoSQL databases, equipping them to work with data in its many forms. 2. **Data Cleaning:** The importance of clean, high-quality data cannot be overstated, and this course teaches foundational strategies to achieve that. 3. **Exploratory Data Analysis (EDA):** Participants will learn how to conduct EDA, using visual methods to confirm readiness for machine learning modeling and apply feature engineering and necessary transformations. 4. **Inferential Statistics and Hypothesis Testing:** Engaging with these topics allows learners to quickly glean insights about their data's quality and develop sound hypotheses to drive future analyses. **Syllabus Breakdown:** - **A Brief History of Modern AI and its Applications:** This introductory module contextualizes AI in today's business landscape, making it easier for students to conceptualize its applications in personal projects. - **Retrieving and Cleaning Data:** This module emphasizes the practical aspects of acquiring and preparing data, a fundamental skill for any aspiring data scientist or machine learning practitioner. - **Exploratory Data Analysis and Feature Engineering:** Here, students engage in hands-on learning to visually assess data and prepare it for machine learning algorithms. - **Inferential Statistics and Hypothesis Testing:** With these analytical techniques, learners gain the ability to draw insights about data quality and craft actionable business hypotheses. - **(Optional) HONORS Project:** For those eager to apply what they've learned, this project allows the student to undertake a detailed analysis of a dataset of their choosing, applying data cleaning, visualization, and hypothesis testing skills. **Recommendation:** I wholeheartedly recommend "Exploratory Data Analysis for Machine Learning" to anyone grappling with the world of data science, whether you are a beginner or with some experience looking to solidify your understanding of foundational techniques. The course is well-structured, informative, and engaging, striking an excellent balance between theory and practical application. Moreover, the optional HONORS project adds tremendous value by allowing students to showcase their proficiencies, making it an excellent addition to your professional portfolio. In a landscape increasingly dominated by data-driven decision-making, mastering the art of data retrieval, cleaning, and exploratory analysis is not just beneficial—it's vital. Embarking on this course will undoubtedly enhance your skill set and lay the groundwork for successful endeavors in machine learning and artificial intelligence. Overall, if you're looking to elevate your data analysis skills and are keen on harnessing the power of machine learning, look no further than this enlightening course offered by Coursera.

Syllabus

A Brief History of Modern AI and its Applications

Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.

Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.

In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.

Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.

In this optional HONORS project you will apply your skills and knowledge learned throughout the course. You can select a dataset from the ones used in this Course or any other dataset of interest and apply all of the demonstrated techniques including, Data Cleaning, Feature Engineering, Exploratory Data Visualization, and Hypothesis Testing.