Udemy - Complete Machine Learning 11 in 1 course

What you'll learn:
Machine Learning
Supervised learning
Unsupervised learning
Model and cost function
Parameter learning
Linear algebra
Linear regression
Linear regression with multiple variables
Gradient descent
Features and polynomial regression
Computing parameters analytically
Logistic regression
Classification and representation
Hypothesis representation
Decision boundary
Multi class classification
Regularisation
Neural networks
Non linear hypothesis
Back propagation
Evaluating a learning algorithm
Advice for applying machine learning
Bias vs variance
Learning curves
Building a spam classifier
Machine learning system design
Handling skewed data
Using large data sets
Support vector machines
Large margin classification
Kernels
SVM in practice
Clustering
Dimensionality reduction
Motivation
Principal component analysis
Applying PCA
Anomaly detection
Density estimation
Gaussian distribution
Building an anomaly detection system
Recommender systems
Predicting movie ratings
Collaborative filtering
Low rank matrix factorisation
Large scale machine learning
Gradient descent with large data sets
Advanced topics
Online learning
Map reduction and parallelism
Photo OCR
Requirements
No prior knowledge required
Description
Welcome to Machine Learning! In this course we start at the very beginning by defining Machine Learning before we dive into it and get more practical with it. By the end of this course you will be an expert in Machine Learning because in this course we do not leave a single stone unturned. To start with, we introduce the core idea of teaching a computer to learn concepts using data; without being explicitly programmed. We end by looking at an application example. Everything else in between will teach you everything you need to know.
Who this course is for
Machine learning and AI
Homepage
https://www.udemy.com/course/complete-machine-learning-11-in-1-course/