The Complete Intro to Machine Learning
The Complete Intro to Machine Learning, available at $39.99, has an average rating of 4.15, with 30 lectures, based on 257 reviews, and has 27470 subscribers.
You will learn about Learn the basics of data visualization and pre-processing (Python basics, Numpy, Pandas, Seaborn) Gain theoretical and practical experience with fundamental machine learning algorithms (Linear and Logistic Regression, K-NN, Decision Trees, Neural Networks) Understand advanced ML topics (encoding, ensemble learning techniques, etc.) Submit to your first Kaggle Machine Learning Competition This course is ideal for individuals who are Anyone interested in machine learning, data science, and artificial intelligence. No experience required. It is particularly useful for Anyone interested in machine learning, data science, and artificial intelligence. No experience required.
Enroll now: The Complete Intro to Machine Learning
Summary
Title: The Complete Intro to Machine Learning
Price: $39.99
Average Rating: 4.15
Number of Lectures: 30
Number of Published Lectures: 30
Number of Curriculum Items: 30
Number of Published Curriculum Objects: 30
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn the basics of data visualization and pre-processing (Python basics, Numpy, Pandas, Seaborn)
- Gain theoretical and practical experience with fundamental machine learning algorithms (Linear and Logistic Regression, K-NN, Decision Trees, Neural Networks)
- Understand advanced ML topics (encoding, ensemble learning techniques, etc.)
- Submit to your first Kaggle Machine Learning Competition
Who Should Attend
- Anyone interested in machine learning, data science, and artificial intelligence. No experience required.
Target Audiences
- Anyone interested in machine learning, data science, and artificial intelligence. No experience required.
Interested in machine learning but confused by the jargon? If so, we made this course for you.
Machine learning is the fastest-growing field with constant groundbreaking research. If you’re interested in any of the following, you’ll be interested in ML:
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Self-driving cars
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Language processing
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Market prediction
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Self-playing games
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And so much more!
No past knowledge is required: we’ll start with the basics of Python and end with gradient-boosted decision trees and neural networks. The course will walk you through the fundamentals of machine learning, explaining mathematical foundations as well as practical implementations. By the end of our course, you’ll have worked with five public data setsand have implemented all essential supervised learning models. After the course’s completion, you’ll be equipped to apply your skills to Kaggle data science competitions, business intelligence applications, and research projects.
We made the course quick, simple, andthorough. We know you’re busy, so our curriculum cuts to the chase with every lecture. If you’re interested in the field, this is a great course to start with.
Here are some of the Python libraries you’ll be using:
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Numpy (linear algebra)
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Pandas (data manipulation)
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Seaborn (data visualization)
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Scikit-learn (optimized machine learning models)
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Keras (neural networks)
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XGBoost (gradient-boosted decision trees)
Here are the most important ML models you’ll use:
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Linear Regression
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Logistic Regression
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Random Forrest Decision Trees
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Gradient-Boosted Decision Trees
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Neural Networks
Not convinced yet? By taking our course, you’ll also have access to sample code for all major supervised machine learning models. Use them how you please!
Start your data science journey today with The Complete Intro to Machine Learning with Python.
Course Curriculum
Chapter 1: Welcome to the Course
Lecture 1: Introduction
Lecture 2: Google Colab Tour
Chapter 2: Python Review
Lecture 1: Variable Types
Lecture 2: Lists and Functions
Lecture 3: Implementation
Chapter 3: Numpy
Lecture 1: Numpy Basics
Lecture 2: Implementation
Chapter 4: Pandas
Lecture 1: Pandas Basics
Lecture 2: Implementation
Chapter 5: Seaborn
Lecture 1: Distribution and Matrix Plots
Lecture 2: Categorical Plots, Regression Plots, and Grids/Style
Lecture 3: Implementation
Chapter 6: Introduction to ML
Lecture 1: Goals and Types of Machine Learning
Chapter 7: Linear Regression
Lecture 1: Linear Regression Theory
Lecture 2: Ordinary Least Squares (OLS)
Lecture 3: Implementation Part 1
Lecture 4: Implementation Part 2
Chapter 8: Logistic Regression
Lecture 1: Logistic Regression Theory
Lecture 2: Logistic Regression Metrics and Implementation
Chapter 9: Decision Trees
Lecture 1: Terminology
Lecture 2: Splitting Algorithms
Lecture 3: Random Forests
Lecture 4: Implementation
Chapter 10: Neural Networks
Lecture 1: What are neural networks?
Lecture 2: Activation Functions
Lecture 3: Gradient Descent
Lecture 4: Backpropagation
Lecture 5: Implementation
Lecture 6: Intro to Neural Networks
Lecture 7: Origins of Neural Networks
Instructors
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Student ML Coalition
Community Organization -
Michael Lutz
Part-Time Researcher at NASA Ames Research Center -
Arjun Rajaram
Instructor at Udemy -
Saurav Kumar
AI Researcher at Stanford Medicine -
Aswin Surya
AI Researcher at MIT, Stanford, and NASA -
Chatanya Sarin
Instructor at Udemy -
Aadi Chauhan
Instructor at Udemy
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 5 votes
- 3 stars: 36 votes
- 4 stars: 88 votes
- 5 stars: 122 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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