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Python for Machine Learning: The Complete Beginner's Course (Udemy.com)

Learn to create machine learning algorithms in Python for students and professionals

Created by: Meta Brains

Last updated January 2024

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What you will learn

  • Learn Python programming and Scikit learn applied to machine learning regression
  • Understand the underlying theory behind simple and multiple linear regression techniques
  • Learn to solve regression problems (linear regression and logistic regression)
  • Learn the theory and the practical implementation of logistic regression using sklearn
  • Learn the mathematics behind decision trees
  • Learn about the different algorithms for clustering

How it compares

Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:

  • Rating4.0 out of 5higher than 16% of them
  • Reviews1,403#49 of 156 by number of reviews
  • Length2.5 hoursshorter than 81% of them
  • Last updatedJanuary 2024more recently than 58% of them

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Course Description

To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! 

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. 

You'll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!

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Instructor Details

Meta Brains

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel.

We bring together both professional and educational experiences to create world-class training programs accessible to everyone.

Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together!

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Reviews

4.0

1,403 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Nisheeta Desai on 3/31/2025

The course is appropriately paced for those already familiar with Python. The explanations of functions are clear and helpful. However, there are a few concepts that could benefit from more detailed explanations. For example, it would be useful to briefly explain what one-hot encoding is, as not everyone may be familiar with that term.

By Agustín González Méndez on 2/6/2025

It gives you plenty of tools to understand how Machine Learning works, however, these tools are not fully supported by theory in this course, wich might be helpful to better understand which mathematical principles they follow, specially in clustering and classification sections. There are some errors in the coding that are not noted on the slides or the video, be mindful of that. Despite this, it's a good exercise for beginners.

By SAHIL MOHAMMED ALI NAGARJI on 12/16/2024

This is an excellent course for learning and creating machine learning algorithms. It covers all the fundamental key aspects of machine learning, from understanding concepts to implementing them in Python. This course equips us, as aspiring machine learning developers, to create our own projects and experiments with confidence.

By Scott Massey on 12/5/2023

The latter sections are a bit messy and rushed, but overall, it's a good and quick overview of common M/L stuff. It probably moves too fast for a beginner in Python.

By Tim Jovers on 9/25/2023

Ganz okay... gibt einen groben Überblick. Das bereitgestellte Jupyter Notebook enthält zum Teil Fehler und die Erklärung sowie Reihenfolge des Codes ist auch verbesserungswürdig.

By Maxim Sluzki on 9/13/2023

Really good, but not deep enough in some ways.. I wished more graphical result insights into KNN, Decision Tree etc.. And Section 9 was too chopped up, meaning that the content in the videos was too small and it could be summed up in less videos. With every video, I have to watch the title again and so on, leading to a loss of time. But overall, this course was good

By Bijan Piri Pishikloo on 8/15/2023

very clear in concept and also pronunciation ;). I knew a little about data science and machine learning and this tutorial really helps review.

By Norbert Lewna on 6/4/2023

Overall good Python ML course for beginners, however it is a trimmed version of the other course. Easy to follow lecture blocks with concept introduction and coding examples.

By Boukary Diallo on 4/19/2023

I really enjoyed this machine learning algorithms in Python training. The instructions were clear and concise, and the practical examples helped to reinforce my understanding of each concept. The practical projects were very well designed and gave me the confidence to apply this knowledge to real-world projects. I would highly recommend this training to anyone looking to get started in the field of machine learning using Python

By Danny Ng on 2/10/2023

It took awhile to understand how to do the coding necessary in Python for the various elements discussed but towards the end of the course the coding became much easier to understand.

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Overall Score : 80 / 100