Prerequisites to Machine Learning: A Beginners Guide
Prerequisites to Machine Learning: A Beginners Guide, available at $64.99, has an average rating of 4.2, with 15 lectures, based on 5 reviews, and has 20 subscribers.
You will learn about Basics of Python programming useful for machine learning Basics of Statistics useful for machine learning To import module and important packages for machine learning About the data set, which is important to implement machine learning This course is ideal for individuals who are Anyone who is curious about machine learning. or Anyone who isn't used to coding but is interested in Machine Learning and wants to learn and apply machine learning. or Anyone who doesn’t know the basics of Statistics and Python to get started with machine learning. or Any school or college students interested in pursuing a career in machine learning. It is particularly useful for Anyone who is curious about machine learning. or Anyone who isn't used to coding but is interested in Machine Learning and wants to learn and apply machine learning. or Anyone who doesn’t know the basics of Statistics and Python to get started with machine learning. or Any school or college students interested in pursuing a career in machine learning.
Enroll now: Prerequisites to Machine Learning: A Beginners Guide
Summary
Title: Prerequisites to Machine Learning: A Beginners Guide
Price: $64.99
Average Rating: 4.2
Number of Lectures: 15
Number of Published Lectures: 15
Number of Curriculum Items: 15
Number of Published Curriculum Objects: 15
Original Price: ₹799
Quality Status: approved
Status: Live
What You Will Learn
- Basics of Python programming useful for machine learning
- Basics of Statistics useful for machine learning
- To import module and important packages for machine learning
- About the data set, which is important to implement machine learning
Who Should Attend
- Anyone who is curious about machine learning.
- Anyone who isn't used to coding but is interested in Machine Learning and wants to learn and apply machine learning.
- Anyone who doesn’t know the basics of Statistics and Python to get started with machine learning.
- Any school or college students interested in pursuing a career in machine learning.
Target Audiences
- Anyone who is curious about machine learning.
- Anyone who isn't used to coding but is interested in Machine Learning and wants to learn and apply machine learning.
- Anyone who doesn’t know the basics of Statistics and Python to get started with machine learning.
- Any school or college students interested in pursuing a career in machine learning.
Hello Machine Learning Enthusiasts!
Are you making the same mistakes that 99% of fresh machine learning students do?
Yes…
Order is important to learn anything. And if you guessed correctly, you realize how important it is to understand certain things before moving on to others.
So, if you are interested to learn Machine Learning, but no idea where to start from, then this course is exactly for you.
This course “Prerequisites to Machine Learning: A Beginners Guide” has been designed in such a way that you will get clear path to move into learning the concepts and implementations of machine learning.
This course is entertaining and interesting, and it covers all the prerequisites to learn Machine Learning. It is organized in the following manner:
Section-1: Introduction
-
Course Overview
-
Meet Your Instructor
-
Introduction to Machine Learning
-
Google Colab Notebook
Section-2: Python for Machine Learning
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Print Statement and Variables
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Functions
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Range, Lists, Tuples
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Importing Modules
Section-3: Packages and Statistics for Machine Learning
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NumPy
-
Pandas
-
Matplotlib
-
Mean, Median and Mode
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Variance and Standard Deviation
Furthermore, the curriculum is dip with practical exercises. As a result, you’ll not only discover the theory, but you’ll also get some practice.
This course also provides the downloadable resources Python Code, executed on Google Colab Notebook, and Data Set that you can practice or use on your own projects as a bonus.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Overview
Lecture 2: Meet Your Instructor
Lecture 3: Introduction to Machine Learning
Lecture 4: Google Colab Notebook
Chapter 2: Python for Machine Learning
Lecture 1: Print Statement and Variables
Lecture 2: Functions
Lecture 3: Range, Lists and Tuples
Lecture 4: Importing Modules
Chapter 3: Packages and Statistics for Machine Learning
Lecture 1: NumPy
Lecture 2: Pandas
Lecture 3: Read CSV File
Lecture 4: Matplotlib
Lecture 5: Matplotlib Continue…
Lecture 6: Mean, Median and Mode
Lecture 7: Variance and Standard Deviation
Instructors
-
Dr. Hesam Akhtar
Educationist
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 2 votes
- 5 stars: 2 votes
Frequently Asked Questions
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