A brief description
Are you looking for a thorough Computer Learning course that will help you start a fruitful career in Data Science and Machine Learning?
You’ve come to the right spot for a Machine Learning lesson.
You will be able to do the following after completing this course:
Create predictive Machine Learning models with confidence to solve business challenges and develop business strategies.
Answer interview questions on Machine Learning.
Participate in and score well in online Data Analytics contests, such as those run by Kaggle.
To see what Machine Learning frameworks you’ll learn, look at the table of contents below.
How would this course benefit you?
Both students who complete this Machine Learning Basics course will receive a verifiable Certificate of Completion.
If you are a business manager or executive, or a student interested in studying and applying machine learning to real-world business issues, this course will provide you with a strong basis by showing you the most common machine learning strategies.
Why do you enroll in this program?
This course outlines all of the steps that should be followed by using linear regression to solve a business problem.
When it comes to data processing, computer learning, and deep learning, what’s the difference?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, with the exception of the types of forecasts that are made. Although data mining uncovers previously unknown patterns and information, machine learning replicates previously found patterns and knowledge—and then applies that knowledge to data and behavior automatically.
Deep learning, on the other hand, employs sophisticated computational power and specific forms of neural networks to study, understand, and classify complex patterns from vast quantities of data. Deep learning applications include automatic language translation and medical diagnosis.
This course is planned for the following individuals:
Those interested in a career in data science
Working professionals who are only starting out on their data path
Statisticians who want more hands-on training
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