Professional Courses
Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.
Categories
Interactive Games
Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.
Typing
Memory
Math
English Adventures
Knowledge
NumPy is a powerful library used in Machine Learning for working with numerical data. It enables efficient creation and manipulation of arrays, which are faster and more versatile than traditional Python lists. The chapter covers basic operations, common functions, and practical applications of NumPy in ML, emphasizing its importance in performing fast calculations and managing datasets.
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.
Sections
References
Untitled document (35).pdfClass Notes
Memorization
What we have learnt
- NumPy is essential for spee...
- Creating and manipulating a...
- Common NumPy functions like...
Final Test
Revision Tests
What we have learnt
- NumPy is essential for speed and efficiency in numerical computations in Machine Learning.
- Creating and manipulating arrays with NumPy is simpler and performs better compared to traditional Python lists.
- Common NumPy functions like zeros(), mean(), and dot() facilitate various ML tasks.
Key Concepts
-
Term: NumPy
Definition: A Python library used for numerical computations that allows for efficient handling of arrays and mathematical operations.
-
Term: Array Operations
Definition: Math operations on NumPy arrays that allow for vectorized calculations essential for Machine Learning.
-
Term: Common Functions
Definition: Functions like np.zeros(), np.ones(), np.mean(), and np.dot() that provide essential operations for data manipulation.
-
Term: Shape and Reshape
Definition: Methods to alter the structure of arrays to meet specific data requirements.