The Complete Intro to Machine Learning

Hands-on ML with Python, Pandas, Regression, Decision Trees, Neural Networks, and more!

4.15 (255 reviews)
Udemy
platform
English
language
Data Science
category
The Complete Intro to Machine Learning
27,467
students
5 hours
content
Feb 2023
last update
$39.99
regular price

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

Description

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 sets and 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, and thorough. 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.

Content

Welcome to the Course

Introduction
Google Colab Tour

Python Review

Variable Types
Lists and Functions
Implementation

Numpy

Numpy Basics
Implementation

Pandas

Pandas Basics
Implementation

Seaborn

Distribution and Matrix Plots
Categorical Plots, Regression Plots, and Grids/Style
Implementation

Introduction to ML

Goals and Types of Machine Learning

Linear Regression

Linear Regression Theory
Ordinary Least Squares (OLS)
Implementation Part 1
Implementation Part 2

Logistic Regression

Logistic Regression Theory
Logistic Regression Metrics and Implementation

Decision Trees

Terminology
Splitting Algorithms
Random Forests
Implementation

Neural Networks

Intro to Neural Networks
Origins of Neural Networks
What are neural networks?
Activation Functions
Gradient Descent
Backpropagation
Implementation

Reviews

Seemab
February 26, 2022
Wonderful experience as I am beginning this course is awesome because it covers theory and practical parts. For the one who is not good at a theory part like me, it is a good start. Thanks for this amazing course.
A
January 22, 2022
Content is a bit of an information dump and the editing / recording could have been better in some of the videos (static in audio, filled pauses). Was difficult to find the usage of these concepts without real life examples.
Amir
December 11, 2021
the basic information is very good. that is to say, after the tutorial video, I test the codes in python and understand the whole concepts.
Trisno
November 24, 2021
The course volume is too low and/or inconsistent, also there might be some guide/instructions on the setup so the students know how to exactly follow the teacher.
Anthony
October 21, 2021
This was a great course, I definitely have a better understanding of Machine Learning now. I like how this includes links to code templates especially.
Nathan
October 19, 2021
Just started this course, and it's looking great so far! explanations and code walkthroughs are easy to understand.

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4348164
udemy ID
10/13/2021
course created date
10/20/2021
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