Introduction to Machine Learning
Duration: 14h 31m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 6.48 GB
Genre: eLearning | Language: English [/align]
Linear and Logistic Regression and Neural Networks Using Python
What you'll learn
Introduction to Machine Learning: math, algorithms, and Python coding for Linear and Logistic Regression and Neural Networks
Requirements
Linear Algebra (matrix multiplication), Multivariable Calculus (gradients), Python programming
Description
Goal:
Provide an introduction to machine learning focusing on linear and logistic regression and neural network approaches
Course Covers:
Underlying mathematics and algorithms in detail
Development in Python of a machine learning framework emphasizing how algorithms translate into code
Approaches for improving performance of machine learning systems
Application to regression, binary and multi-class classification problems
Case studies: house price prediction, spam classification, digits identification
Tensorflow examples
Course Approach
Lecture videos with many examples to illustrate the theory
Jupyter Notebook demos to complement lectures
Walk through of development of machine learning framework and running of programs
50+ exercises (with solutions) including math problems, Jupyter notebook based exercises, and programming problems
Github site with all course materials (course framework code, Jupyter notebook demos, exercises & solutions, pdf of presentations)
Who this course is for:
Students without any previous experience with machine learning
Students with previous experience who would like to revisit the math, algorithms and coding for machine learning in detail
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