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
Created by Lazy Programmers Inc. | 6 hours on-demand video course
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read. This Deep Learning Prerequisites: The Numpy Stack in Python (V2+) course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.
Deep Learning Prerequisites: The Numpy Stack in Python (V2+) Course Review
If you’re serious about learning deep learning, machine learning, or artificial intelligence, then you need to have a strong foundation in the Numpy stack. This course, Deep Learning Prerequisites: The Numpy Stack in Python (V2+), is a great way to get that foundation.
The course is taught by Lazy Programmer, who is a well-known and respected instructor in the machine learning community. He does a great job of explaining the concepts in a clear and concise way, and he provides plenty of examples and exercises to help you learn.
The course covers all of the essential Numpy topics, including:
- Numpy arrays
- Vector and matrix operations
- Indexing and slicing
- Data manipulation
- Statistical functions
- Plotting and visualization
In addition to Numpy, the course also covers the other basic libraries in the Numpy stack, including Scipy, Pandas, and Matplotlib.
Overall, Deep Learning Prerequisites: The Numpy Stack in Python (V2+) is a great course for anyone who wants to learn the basics of the Numpy stack. It’s well-taught, comprehensive, and affordable.
The course has been fully optimized for deep learning, machine learning, and artificial intelligence. This means that the instructor focuses on the specific topics and skills that you need to know for these fields. He also provides examples and exercises that are relevant to deep learning, machine learning, and AI.
Here are some unique aspects of the course that make it stand out from other Numpy courses:
- The instructor has a very practical approach to teaching. He focuses on showing you how to use Numpy in real-world applications.
- The course is well-paced and easy to follow. The instructor breaks down the concepts into small, manageable chunks.
- The course is packed with examples and exercises. This is a great way to learn and solidify your understanding of the material.
- The course is up-to-date and covers the latest version of Numpy.
What you’ll learn in Numpy Course
- 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!
Recommended Course by Lazy Programmers
Who this course is for:
- Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
- Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code
If you’re serious about learning deep learning, machine learning, or artificial intelligence, then I highly recommend Deep Learning Prerequisites: The Numpy Stack in Python (V2+). It’s a great course that will give you the foundation you need to succeed in these fields.