Machine Learning: Bner to Expert

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 ma 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

No prior knowledge required

In this course we start at the very bning 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.

Machine learning and AI



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