Crash Course in Machine Learning

Offered byGreat Learning

Crash Course in Machine Learning
at
Great Learning
Overview

Enrol for this Free Crash Course in Machine Learning to learn from our experts on Machine Learning, Supervised and Unsupervised Learning, and more. Start your journey towards the mastery of Machine Learning today!

Duration	42 hours
Total fee	Free
Mode of learning	Online
Difficulty level	Beginner
Official Website	Explore Free Course
Credential	Certificate

Crash Course in Machine Learning
at
Great Learning
Highlights

Earn a certificate of completion

Crash Course in Machine Learning
at
Great Learning
Course details

What are the course deliverables?

Introduction to Machine Learning
Preparing and Learning about Data
Data Visualization
Supervised and Unsupervised Machine Learning
Use Cases
Career Opportunities - ML

More about this course

This free crash course on machine learning is designed for anyone who is interested in learning the basics of machine learning and wants to gain hands-on experience in building and deploying ML models. The course is suitable for beginners who have no prior knowledge of machine learning as well as for those who have some programming background. The course begins with an introduction to machine learning and its applications. You will learn about the different types of machine learning and the basic concepts behind them. Next, you will learn how to use Python for machine learning. You will be introduced to popular Python libraries used for ML, such as NumPy, Pandas, and Scikit-Learn.
Preparing and learning about data is an important step in the ML pipeline. The course covers data preparation techniques such as data cleaning, data normalization, and feature engineering. Data visualization is also covered, which is crucial in understanding the underlying patterns in data. The course then delves into supervised and unsupervised learning. You will learn about the different algorithms used in supervised learning, such as regression, classification, and decision trees. You will also be introduced to clustering algorithms, which are used in unsupervised learning.
The course concludes with ML model deployment, use cases, and projects. You will learn about the various deployment options available for ML models and how to choose the right one for your project. The course also covers career opportunities in machine learning and provides interview questions to help you prepare for a job in this field. Overall, this course provides a comprehensive introduction to machine learning and equips you with the skills to start building your own ML models.