Machine Learning with Python: A Practical Introduction

1. Machine Learning with Python: A Practical Introduction

2. Machine Learning with Python: A Practical Introduction

3. Course Course Overview Overview This machine learning course using Python focuses on helping individuals gain a solid understanding of supervised and unsupervised learning and statistical modeling. The course covers various popular algorithms including classification, regression, clustering, and dimensional reduction, and popular models like train/test split, RMSE, and random forests. Real-life examples of machine learning and its impact on society are also discussed, with hands-on labs to apply theoretical knowledge. The course is designed for those seeking to advance in the field of data science, using Python as the primary language, and ultimately earn an IBM Data Science Professional Certificate.

4. How How It Works It Works This machine learning with python course is structured into six distinct modules and is part of the IBM Data Science Professional Certificate Program. It is a self-paced program, meaning you can complete it at your own pace, and you are not restricted by a set schedule for completing modules or submitting assignments. On average, it is expected to take about 5 weeks to complete if you work 4-6 hours per week. The course materials are available from the start of the course and will remain accessible throughout your enrollment. Assessment methods will include videos, reading materials, quizzes, hands-on labs, and final assignments. Upon completion, you will receive an IBM Certificate. The course also includes mentoring services, providing you with guidance and support through a dedicated discussion space, where you can ask questions and connect with peers.

5. Skills Skills You Will Gain You Will Gain You will: Be conversant with the terminologies, libraries, and programming languages used to develop machine learning systems. Being able to use the proper type of regression to estimate a collection of data. Apply the proper classification technique to a given machine learning issue. Employ the appropriate clustering methods on various data sets. Understand how recommendation systems operate and be able to apply one to a piece of data. Have shown that you understand machine learning through a project that was graded. • • • • • •

6. Who Who Should Enroll On This Course Should Enroll On This Course Individuals looking to learn how to work with different kinds of data. Individuals wanting to perform analysis on data. Individuals wanting an introduction to Machine Learning with Python. • • •

7. THANKS THANKS Do you have any questions? Individual@skillup.tech +91 9319095982 https://skillup.online/ CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, infographics & images by Freepik