Beginner Machine Learning in Python + ChatGPT Prize [2024]
Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python
What you’ll learn
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Machine Learning
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The Machine Learning Process
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Regression
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Ordinary Least Squares
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Simple Linear Regression
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Splitting your data into a Training set and a Test set
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Multiple Linear Regression
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R-Squared
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Adjusted R-Squared
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Classification
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Maximum Likelihood
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Feature Scaling
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Confusion Matrix
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Accuracy
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Clustering
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K-Means Clustering
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The Elbow Method
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K-Means++
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Build Machine Learning models in Python
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Make Predictions
Requirements
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Every single line of code will be fully explained so there are no prerequisites for coding skills
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This is a foundational course, so no prior knowledge of Data Science is required
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Some high-school level mathematics knowledge is recommended but not required
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We use Google Colab for coding in Python which is very intuitive, but you can also use Jupyter or another IDE
Description
Do you want to learn Machine Learning but don’t know where to start?
Have you been looking for a beginner-friendly course that will equip you with powerful tools for your career?
You’ve come to the right place!
This is Machine Learning in Python Level 1… and we will help you get started.
My name is Kirill Eremenko, I’m a Data Science instructor with over 7 years of experience, and together with my co-instructor Hadelin de Ponteves we have taught over 2M students Worldwide.
And now, we’ve created this course to help YOU get on track with Machine Learning and start applying it in YOUR career.
This course has 3 main sections:
First, we will dive into Regression, where we will learn to predict continuous variables and we will cover foundational concepts like Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared.
In the second section you will master Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios… and you will build your very first Logistic Regression!
The third and final section is all about Clustering. We will investigate the concepts of unsupervised learning and you will practice using K-Means Clustering to discover previously unseen patterns in your data.
Sound exciting?
Well, in this course not only will you learn the theory behind all of these Machine Learning models, but you will also practice applying them in different scenarios so that you are prepared for the Real World.
Plus, you will get Python code templates which you can download and keep. These are invaluable tools which you can apply in your own projects right away.
So, what are you waiting for?
Sign up today and take your career to the next level with Machine Learning!
Who this course is for:
- Anyone interested in Data Science
- Anyone who wants to become a Data Scientist
- Anyone interested in Machine Learning
- Anyone who wants to become a ML or AI engineer
- Data Science professionals
- Machine Learning professionals
- Anyone who wants to add Machine Learning to their CV or career toolkit
Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Ligency Team
Last updated 3/2024
English
English [Auto]
Size: 694 MB
https://www.udemy.com/course/machine-learning-python-level-1/.