Learning in Machine Learning (ML)

Introduction of Machine Learning

Machine Learning is the science and art of programming computers so they can learn from data. Machine learning is a subset of artificial intelligence.

Or

Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.   Arthur Samuel, 1959

Or

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Tom Mitchell, 1997

Machine Learning
Machine Learning

For example, your spam filter is a Machine Learning program that can learn to flag spam given examples of spam emails and examples of regular emails or ham.

Types of Machine Learning

There are so many different types of Machine Learning systems that it is useful to classify them in broad categories based on:

Instance-based versus model-based learning

Online versus batch learning

Supervised, unsupervised, and Reinforcement Learning 

Instance-based versus model-based learning

Most Machine Learning tasks are about making predictions. This means that given a number of training examples, the system needs to be able to generalize to examples it has never seen before. There are two main approaches to generalization: instance-based learning and model-based learning.

Instance-based Learning

Possibly the most trivial form of learning is simply to learn by heart. If you were to create a spam filter this way, it would just flag all emails that are identical to emails that have already been flagged by users.

Model-based learning

Another way to generalize from a set of examples is to build a model of these examples, then use that model to make predictions. This is called model-based learning.

Online versus batch learning

In online learning, you train the system incrementally by feeding it data instances sequentially, either individually or by small groups called mini-batches.

In batch learning, the system is incapable of learning incrementally: it must be trained using all the available data. This will generally take a lot of time and computing resources, so it is typically done offline.

Supervised, unsupervised, and Reinforcement Learning

Machine Learning systems can be classified according to the amount and type of supervision they get during training. There are four major categories: supervised learning, unsupervised learning, and Reinforcement Learning

Supervise Learning

Supervised learning occurs when a machine or program learns from example data and associated target responses that can consist of numeric values. In supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels. Here are some of the most important supervised learning algorithms:

K-Nearest Neighbors

Linear Regression

Logistic Regression

Support Vector Machines (SVMs)

Decision Trees and Random Forests

Unsupervised learning

In Unsupervised Machine Learning, we do not have labeled data and outcome variables to predict. Under the umbrella of unsupervised learning, fall -: Clustering, Visualization and Dimension reduction, and Association rule learning.  Here are some of the most important unsupervised learning algorithms:

Clustering

K-Means

Hierarchical Cluster Analysis (HCA)

Expectation Maximization

Visualization and dimensionality reduction

Principal Component Analysis (PCA)

Kernel PCA

Locally-Linear Embedding (LLE)

t-distributed Stochastic Neighbor Embedding (t-SNE)

Association rule learning

Apriori

Eclat

Reinforcement Learning

Reinforcement learning teaches the machine trial and error. Reinforcement learning the agent (Algorithm) continuously learns from the environment in an iterative fashion.

The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards and penalty in return.

Challenges in ML

Underfitting the Training Data

Machine Learning challenges
Machine Learning types

Overfitting the Training Data

The best Fit in ML

Non representative Training Data

Insufficient Quantity of Training Data


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