Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

4.49 (22028 reviews)

Udemy

platform

English

language

Data & Analytics

What you will learn

Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn

Understand and code using the Numpy stack

Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms

Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course Gallery

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) – Screenshot 1 — Screenshot 1 – Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) – Screenshot 2 — Screenshot 2 – Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) – Screenshot 3 — Screenshot 3 – Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) – Screenshot 4 — Screenshot 4 – Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

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Comidoc Review

Our Verdict

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) offers valuable insights into the essential libraries used in deep learning and machine learning, but be prepared to face some outdated content and a lack of solutions for exercises. Enthusiastic instructors explain complex concepts with simplicity and provide hands-on experience through coding exercises, making it a solid foundation course despite its minor shortcomings.

What We Liked

Comprehensive coverage of Numpy, Scipy, Pandas, and Matplotlib libraries
Explains machine learning concepts in simple terms
Includes real-world examples and code for experimentation
Provides insights into various machine learning models

Potential Drawbacks

Exercises at the end of each section lack solutions
Some outdated information related to Python 2
No answers or explanations provided for certain exercises
Last sections appear marketing-oriented and outdated