Python for Machine Learning & Data Science Masterclass

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

4.60 (17344 reviews)

Udemy

platform

English

language

Data Science

What you will learn

You will learn how to use data science and machine learning with Python.

You will create data pipeline workflows to analyze, visualize, and gain insights from data.

You will build a portfolio of data science projects with real world data.

You will be able to analyze your own data sets and gain insights through data science.

Master critical data science skills.

Understand Machine Learning from top to bottom.

Replicate real-world situations and data reports.

Learn NumPy for numerical processing with Python.

Conduct feature engineering on real world case studies.

Learn Pandas for data manipulation with Python.

Create supervised machine learning algorithms to predict classes.

Learn Matplotlib to create fully customized data visualizations with Python.

Create regression machine learning algorithms for predicting continuous values.

Learn Seaborn to create beautiful statistical plots with Python.

Construct a modern portfolio of data science and machine learning resume projects.

Learn how to use Scikit-learn to apply powerful machine learning algorithms.

Get set-up quickly with the Anaconda data science stack environment.

Learn best practices for real-world data sets.

Understand the full product workflow for the machine learning lifecycle.

Explore how to deploy your machine learning models as interactive APIs.

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This 4.6-star rated Udemy course with 120718 subscribers entitled 'Python for Machine Learning & Data Science Masterclass' promises an extensive overview of data science and machine learning techniques using Python, Numpy, Pandas, Matplotlib, Scikit-Learn, and other libraries. Though there's a heavy focus on theory, the instructor thoroughly explains concepts making it valuable even for beginners with limited math or statistics knowledge. Be warned, however, that outdated code and library versions may require extra effort as updating is necessary, presenting minor setbacks in an otherwise informative and engaging course.

What We Liked

Covers a wide range of machine learning topics with great depth and detail, making it an extensive resource for learning about data science and machine learning with Python.
Instructor explains concepts thoroughly with examples, providing clear understanding even for those without a strong math or statistics background.
Real-world datasets are used for projects, allowing students to gain experience in data pipeline workflows, feature engineering, and predictive modeling.

Potential Drawbacks

A majority of lectures rely on extensive theoretical explanations, meaning learners get less practical examples and assignments than desired.
The course's older library versions may cause compatibility issues when compared to the latest libraries. It is suggested to use the same library versions mentioned in the course.
Code requires updating for newer library versions, presenting a slight hurdle for learners adapting from examples.