Data Science and Machine Learning Series: Manipulating Matrices using NumPy

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 39m | 213 MB

Master NumPy in this course within the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice applying NumPy in working with matrices.

The following eight topics will be covered in this Data Science and Machine Learning course:

• Introducing NumPy. Develop a foundational knowledge of the Python library NumPy in this first topic in the Data Science and Machine Learning Series. NumPy is a powerful general purpose package for working with machine learning algorithms. Explore this extensive library in this session.
• Using Random Generators in NumPy. Use random generators in NumPy in this second topic in the Data Science and Machine Learning Series.
• Performing Statistical Computation with NumPy. Perform statistical computation with NumPy in this third topic in the Data Science and Machine Learning Series.
• Working with Linear Algebra Matrices and Tensors. Revisit linear algebra concepts such as vectors and afterwards work with matrices and tensors in this fourth topic in the Data Science and Machine Learning Series.
• Practicing Matrix Multiplication and the Hadamard Product. Practice matrix multiplication, which is called the Hadamard product, in this fifth topic in the Data Science and Machine Learning Series.
• Performing Norm of a Vector. Perform norm of a vector in this sixth topic in the Data Science and Machine Learning Series.
• Broadcasting in NumPy. Broadcast in NumPy in this seventh topic in the Data Science and Machine Learning Series. NumPy’s broadcasting feature allows us to add a scalar to any matrix.
• Finding the Inverse, Determinant, and Trace of a Matrix. Find the inverse, determinant, and trace of a matrix in this eighth topic in the Data Science and Machine Learning Series.