Machine Learning is the process of creating models that can perform a certain task without the need for a human explicitly programming it to do something


Your smartphone, your house, your car, and your bank all use artificial intelligence on a daily basis.

Sometimes it’s easy to understand, when you ask Siri, Cortana or OK Google to get you directions.

Sometimes it’s less obvious, like when you make an abnormal purchase on your credit card and don’t get a fraud alert from your bank.

AI, ML and DL are everywhere and Data Science is the interdisciplinary field of methods to extract the knowledge needed.

These technologies are making a huge difference in our lives every day and evolving fast by a magnitude of people working to improve them consistently.

Definitions

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, including learning, reasoning and self-correction

Machine learning (ML) is an application of AI generating systems that can learn and improve without being programmed

Deep Learning (DL) is a subset of Machine Learning and Artificial Intelligence. The term refers to a particular approach used for creating and training neural networks that are considered highly promising decision-making nodes

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Machine Learning algorithms

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

Supervised Learning

The area of Machine Learning where you have a set of independent variables which helps you to analyse the dependent variable and the relation between them

Whatever you want to predict is called as Dependent Variable, while variables that you use to predict are called as Independent Variables

You want to predict the age of the person 
based on the person’s height and weight, 
then height and weight will be the independent 
variables, while age will be the dependent variable

Supervised learning is the most popular paradigm for machine learning.
It is the easiest to understand and the simplest to implement

Types of Supervised Learning

Supervised Learning can be classified into 2 types

  • Regression
  • Classification

Regression

Regression is the kind of Supervised Learning that learns from the Labelled Datasets and is then able to predict a continuous-valued output for the new data given to the algorithm.

It is used whenever the output required is a number such as money or height etc.

  • Linear Regression
  • Logistic Regression

Linear Regression

This algorithm assumes that there is a linear relationship between the 2 variables, Input (X) and Output (Y), of the data it has learnt from. The Input variable is called the Independent Variable and the Output variable is called the Dependent Variable.

When unseen data is passed to the algorithm, it uses the function, calculates and maps the input to a continuous value for the output.

Linear Regression Python

Logistic Regression

This algorithm predicts discrete values for the set of Independent variables that have been passed to it.

It does the prediction by mapping the unseen data to the logit function that has been programmed into it.

The algorithm predicts the probability of the new data and so it’s output lies between the range of 0 and 1.

Classification

Classification is the kind of machine learning where the algorithm needs to map the new data that is obtained to any one of the 2 classes that you have in the dataset.

The classes need to be **mapped to either 1 or 0 **which in real-life translated to Yes or No, Rains or Does Not Rain

The output will be either one of the classes and not a number as it was in Regression.

  • Decision Tree
  • Naive Bayes Classifier
  • Support Vector Machines

Decision Tree

Decision Trees classify based on the feature values. They use the method of Information Gain and find out which feature of the dataset gives the best of information, make that as the root node and so on till they are able to classify each instance of the dataset.

Every branch in the Decision Tree represents a feature of the dataset. They are one of the most widely used algorithms for classification.

Naive Bayes Classifier

Naive Bayes algorithms assume that the features of the dataset are all independent of each other.

Works great on large datasets.

Support Vector Machines

Support Vector Machines algorithms are based on the statistical learning theory of Vapnik.

They use Kernal functions which are a central concept for most of the learning tasks.
These algorithms create a hyper-plane that is used to classify the two classes from each other.

Supervised Learning - Example

Predicting house prices

First, we need data about the houses: square footage, number of rooms, features, whether a house has a garden or not, and so on.

We then need to know the prices of these houses, i.e. the corresponding labels.

By leveraging data coming from thousands of houses, their features and prices, we can now train a supervised machine learning model to predict a new house’s price based on the examples observed by the model.

Logistic Regression Python

Unsupervised Learning

Unsupervised Learning

You don’t have any dependent variable. You just have collection of variables and try to find out similarity between them and classify them into clusters

Unsupervised Learning - Examples

  • Customer segmentation, or understanding different customer groups around which to build marketing or other business strategies
  • Recommender systems, which involve grouping together users with similar viewing patterns in order to recommend similar content.
  • Anomaly detection, including fraud detection or detecting defective mechanical parts - predictive maintenance

Reinforcement Learning

Reinforcement Learning

It is the training of machine learning models to make a sequence of decisions.

The machine learns to achieve a goal in an uncertain, potentially complex environment. The machine employs trial and error to come up with a solution to the problem.

Reinforcement Learning - Example

Facebook has developed an open-source reinforcement learning platform — Horizon.
The platform uses reinforcement learning to optimize large-scale production systems.

Facebook has used Horizon internally

  • To personalize suggestions
  • Deliver more meaningful notifications to users
  • Optimize video streaming quality

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