Machine Learning Intro for Python Developers
Machine Learning Intro for Python Developers, available at $39.99, has an average rating of 4.22, with 19 lectures, based on 176 reviews, and has 22820 subscribers.
You will learn about Machine Learning Basics with Python How to apply ML algorithms to your own problems How some algorithms work internally (kmeans, decision tree) Classify data automatically Predict prices with algorithms Group data points automatically This course is ideal for individuals who are Python developers curious about Machine Learning It is particularly useful for Python developers curious about Machine Learning.
Enroll now: Machine Learning Intro for Python Developers
Summary
Title: Machine Learning Intro for Python Developers
Price: $39.99
Average Rating: 4.22
Number of Lectures: 19
Number of Published Lectures: 19
Number of Curriculum Items: 19
Number of Published Curriculum Objects: 19
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Machine Learning Basics with Python
- How to apply ML algorithms to your own problems
- How some algorithms work internally (kmeans, decision tree)
- Classify data automatically
- Predict prices with algorithms
- Group data points automatically
Who Should Attend
- Python developers curious about Machine Learning
Target Audiences
- Python developers curious about Machine Learning
Learn how to use Machine Learning with Python Scikit!
This is a getting started course for Machine Learning. If you are new to Machine Learning, this course is for you!
We’ll teach you how to get started with Machine Learning including these topics:
-
What is Machine Learning
-
Automatically classify new data
-
Predict prices with regression
-
Group your data automatically with clustering
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: What is Machine Learning?
Lecture 3: Example Problems
Lecture 4: Supervised Learning
Chapter 2: First Program (Classifier)
Lecture 1: Installing Scikit and Numpy
Lecture 2: Important Note
Lecture 3: First Program (Classifier)
Lecture 4: Lecture Notes
Lecture 5: Exercise
Chapter 3: Iris Classifier
Lecture 1: Iris Classifier
Lecture 2: Accuracy Score
Lecture 3: See a Decision Tree
Chapter 4: Regression
Lecture 1: Linear Regression
Lecture 2: Lecture Notes
Lecture 3: Regression Example
Lecture 4: Polynomial Regression
Chapter 5: Clustering
Lecture 1: Clustering
Lecture 2: Lecture Notes
Lecture 3: Kmeans explained
Instructors
-
Frank Anemaet
Supercharge Your Career
Rating Distribution
- 1 stars: 5 votes
- 2 stars: 13 votes
- 3 stars: 32 votes
- 4 stars: 53 votes
- 5 stars: 73 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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