Overview
After completing this course, you will have an array of abilities to secure your dream career. Furthermore, you will gain free access to all of the materials in this course for a whole year and will learn a plethora of highly sought-after abilities to advance your career.
With this complete and thorough course, you’ll have all of your questions addressed and all of the knowledge you need. Aside from preparing you for success, this course provides various benefits to make your learning experience more enjoyable and less tiring. So, add this course to your cart and boost your expertise!
Why Choose Study Booth?
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Learn using compelling voice-over and graphic elements to help you along the way. -
Get a year’s worth of unlimited access to the course. -
Get expert assistance 24 hours a day, seven days a week. -
Take part in quick assessments and receive immediate feedback on your progression. -
You can study on your own time and from the comfort of your own home.
Who Is This Course For?
Those who want to improve their professional skills while preparing for a specific job can take advantage of this opportunity! This course will assist you to stay updated on the newest advancements and will coach you accordingly. So, if you want the best training possible, invest in this course.
Requirements
No formal entry or relevant experience is required for this course!
This training is open to everyone who is willing to learn. All you need is –
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A smart device that has a constant internet connection -
A strong desire to learn the topic -
And being over the age of 16
Career Path
Use this all-inclusive course to gain the knowledge and abilities you’ll need to succeed in the job.
Course Curriculum
| Module 01: Introduction to Machine Learning Algorithms | |||
| Introduction to types of ML algorithm | 00:02:00 | ||
| Module 02: Preprocessing | |||
| Importing a dataset in python | 00:02:00 | ||
| Resolving Missing Values | 00:06:00 | ||
| Managing Category Variables | 00:04:00 | ||
| Training and Testing Datasets | 00:07:00 | ||
| Normalizing Variables | 00:02:00 | ||
| Normalizing Variables – Python Code | 00:03:00 | ||
| Summary | 00:01:00 | ||
| Module 03: Regression | |||
| Simple Linear Regression – How it works? | 00:04:00 | ||
| Simple Linear Regreesion – Python Implementation | 00:07:00 | ||
| Multiple Linear Regression – How it works? | 00:01:00 | ||
| Multiple Linear Regression – Python Implementation | 00:09:00 | ||
| Decision Trees – How it works? | 00:05:00 | ||
| Random Forest – How it works? | 00:03:00 | ||
| Decision Trees and Random Forest – Python Implementation | 00:04:00 | ||
| Module 04: Classification | |||
| kNN – How it works? | 00:02:00 | ||
| kNN – Python Implementation | 00:10:00 | ||
| Decision Tree Classifier and Random Forest Classifier in Python | 00:10:00 | ||
| SVM – How it works? | 00:04:00 | ||
| SVM – Python Implementation | 00:06:00 | ||
| Resources | |||
| Resources – Machine Learning with Python | 00:00:00 | ||
| Assignment | |||
| Assignment – Level-3 Machine Learning Course with Python | 00:00:00 | ||
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Offer Ends in
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Access01 Year
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LevelLevel 3
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Students2
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Duration1 hour, 32 minutes