Introduction to Machine Learning

Hello all, welcome to the practical machine learning hub. Here you will be learning some of the projects on machine learning and deep learning. But in this post let us know what exactly is machine learning and types of machine learning.

What is Machine Learning?

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

According to Tom Mitchell, 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.

For example in Spam emails,

The task T is classifying emails.

The experience E is labeling emails as spam or not spam.

The performance P is how correctly it classified as spam or not spam

In traditional programming, you give data and program(logic) as input and the model gives output. For example, you have given X=2 and Y=2*X as input to model and it gives output as Y=4.

But in machine learning, if you give X=[1,2,3,4] for Y=[2,4,6,8] i.e for X=1,Y=2 , for X=2,Y=4, for X=3,Y=6 and similarly for X=4,Y=8 (this is in the form of Y=2*X).Now when you want to know the value of Y, when X=6, then machine learning will predict the pattern in the data given(X,Y) and gives output as Y=12(approx.)

Types of Machine Learning

There are mainly 3 types of Machine Learning.

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Supervised Learning:

In supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels. A typical supervised learning task is classification. The spam filter is a good example of this: it is trained with many example emails along with their class (spam or ham), and it must learn how to classify new emails.

Another typical task is to predict a target numeric value, such as the price of a car, given a set of features (mileage, age, brand, etc.) called predictors. This sort of task is called regression. To train the system, you need to give it many examples of cars, including both their predictors and their labels (i.e., their prices).

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

• Neural networks

Unsupervised Learning:

In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to learn without a teacher

Here are some of the most important unsupervised learning algorithms :

• Clustering

— K-Means

— DBSCAN

— Hierarchical Cluster Analysis (HCA)

• Anomaly detection and novelty detection

— One-class SVM

— Isolation Forest

• Visualization and dimensionality reduction

— Principal Component Analysis (PCA)

— Kernel PCA

— Locally-Linear Embedding (LLE)

— t-distributed Stochastic Neighbor Embedding (t-SNE)

Reinforcement Learning:

Reinforcement Learning is a very different beast. The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards). It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation.

There is also another type of machine learning named Semi-Supervised Learning

Semi-supervised Learning:

Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data. This is called semi-supervised learning.

Some photo-hosting services, such as Google Photos, are good examples of this. Once you upload all your family photos to the service, it automatically recognizes that the same person A shows up in photos 1, 5, and 11, while another person B shows up in photos 2, 5, and 7. This is the unsupervised part of the algorithm (clustering). Now all the system needs is for you to tell it who these people are. Just one label per person, and it is able to name everyone in every photo, which is useful for searching photos.

That's all for this post. Hope you got some information from this post. In the next post we will learn about Deep learning and what is the main difference between Machine Learning and Deep Learning. Thank you!

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