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:

Self-driving cars
Language processing
Market prediction
Self-playing games
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:

Numpy (linear algebra)
Pandas (data manipulation)
Seaborn (data visualization)
Scikit-learn (optimized machine learning models)
Keras (neural networks)
XGBoost (gradient-boosted decision trees)

Here are the most important ML models you’ll use:

Linear Regression
Logistic Regression
Random Forrest Decision Trees
Gradient-Boosted Decision Trees
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

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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