NumPy Crash Course for Machine Learning | Arrays, Indexing, Operations and More

Crash Course Tutorials Playlist

Welcome to the NumPy Crash Course for Machine Learning! In this series, you'll learn how to work with one of the most essential Python libraries for data science and machine learning: NumPy. This course covers everything from creating arrays to advanced operations like indexing, statistical functions, and working with NaN values.

Here’s what we’ll cover in this crash course:
- Creating 1D, 2D, and 3D NumPy Arrays
- Using Ones and Zeros to Create Arrays
- Indexing 1D, 2D, and 3D Arrays
- Handling Inf and NaN in NumPy
- Performing Statistical Operations
- Understanding Shape, Reshape, and Flattening Arrays
- Sequences, Repetitions, and Random Numbers in Arrays
- Using Where(), ArgMax(), ArgMin() in ML Models
- File Read/Write with Preprocessed Arrays
- Concatenation and Sorting in Arrays
- Working with Dates in NumPy

Whether you're just getting started with NumPy or looking to solidify your skills, this crash course will give you the hands-on practice you need for working on machine learning projects.

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