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



DOWNLOAD
uploadgig


rapidgator


nitroflare