What is Machine Learning…?

Machine learning is the subfield of computer science, that provides computers the ability to automatically learn on their own and improve from their experiences without being explicitly programmed.

Though it has been hidden in the recent past but still machine learning has become a basic pillar of IT. We are constantly being surrounded by several ML-based applications like search engines, anti-spam filters, credit card fraud detection system, etc etc.

ML is the subset of Artificial Intelligence, which deals with structured and semi-structured data. To understand the concept behind ML, a precise overview of AI is necessary.

When AI was coined first in 1955, it’s aim was to make machines that are able to perform unique human-based tasks that require intelligence.

At that time, researchers initiated to work on problems that require human intelligence like, solving logic problems and playing checkers.

Take a check playing system as an example of Artificial intelligence in action. While playing against the computer, you’ll notice that there’s “AI” behind the actions of those checkers when computer defeats you.

These successes paved the way for further discoveries. The researchers mainly focused on the output system of the computer. In simple words, as long as computer is doing something intelligent, then it is exhibiting intelligence that is artificial.

Most AI researchers wrongly believed that, to achieve intelligence in machines they have to conduct so many hard-coded rules.

Imagine how you start learning and reading…?

You didn’t just sit in the class and mastered your subject at once. Your teacher probably introduced to a simple book where you started your learning process.

Overtime, you progressed toward more complex books. In this whole process, you learned how to read, how to write, how to pronounce words, which began from a simple book graduating over time to more complex books.

In simple words, you processed a lot of data and learned from them. This is the exact concept behind ML.

In ML, we feed an algorithm (some input data) and allow the computer to figure out output data on it’s own. The learning process starts with observing the input data that may include examples or instructions where patterns are observed in the data to make better decisions from them.

The main objective of ML is to allow machines to learn on their own capacity without any human intervention and make better future decisions. For example, in the banking sector we could feed an algorithm regarding financial transactions and then tell the system which transactions are fraudulent. By learning on it’s own, the algorithm can predict fraud in the future.

Most probably, you have noticed that when you get recommendations from most popular platforms such as Google, Facebook, Instagram or any shopping site, you get them on the basis of your own interests. All this achieved with the ML algorithms.

Types of Machine Learning…

Machine Learning is divided into four main types :

1. Supervised ML

2. Unsupervised ML

3. Semi-supervised ML

4. Reinforcement ML

1. Supervised Learning…

In supervised ML, the machine mainly focuses on the classification type of problems. It deals with labeled data sets and algorithms.

As input data is fed in the model, it adjusts it’s weights until model has been fitted appropriately. The models are placed in machines to make prediction. It is further categorized into two parts :

1. Classification

2. Regression

Classification…

Classification is the process in which the input data is labeled on the basis of past data experiences. It is called supervised learning because the way an algorithms learning process is done, it is training dataset, and while using training dataset, the process can be thought of as a teacher supervising learning process. The correct answer is known and stored in the system already.

Machines are also trained with algorithms about the data format. The example of classification is weather forecasting, and specify tomorrow will be hot or cold.

Regression…

Regression is the process to identify the labeled data and calculate the data on the basis of prediction. These results are based on independent values. Prediction of temperature of tomorrow on the basis of past data is one of good example of regression.

Examples of SL Algorithms…

1. Naive Bayes Classification
2. Support Vector machine for classification problems
3. Random Forest for classification and Regression problems
4. Linear regression for regression problems
5. Ordinary Least Squares Regression
6. Logistics Regression
7. Ensemble Methods

2. Unsupervised Learning…

The 2nd type of Machine learning is termed as unsupervised learning because unlike supervised learning, there are no correct answers and there’s no teacher to this process. Algorithms are left to their own devices to help, discover and present the interesting structure that is present in the data.

These algorithms discover hidden patterns or data grouping without the need for human mingling. It has ability to discover similarity and differences in information, making it ideal solution for exploratory data analysis, customer segmentation, image and pattern recognition and cross- selling strategies.

It’s results are very reliable when compared with supervised learning. For example, we present images of vegetables to USL model, this model makes clusters and separates them on the basis of a given pattern.

Unsupervised Learning is divided in to two main areas :

1. Clustering

2. Dimensionality Reduction

Clustering…

In clustering, the data is found in the segments and meaningful groups. These groups have their own patterns through which data is segmented and arranged.

Dimensionality Reduction…

The technique for reduction of input variables in training data are referred to as dimensionality reduction. The unnecessary data is removed in this phase.

Examples of USL Algorithms…

1. K- means for clustering problems
2. Apriori algorithm for association rule learning problems
3. Principal Component Analysis
4. Singular Value Decomposition
5. Independent Component Analysis

3. Semi-Supervised ML

Semi-supervised learning offers a middle path between supervised and unsupervised learning. It is also known as hybrid. The data in this model has fewer shares of labeled data and more shares of unlabeled data. The labeled data is very cheap in contrary to the unlabeled data. Here the algorithm firstly uses unsupervised learning algorithms to cluster the labeled data and then uses the supervised learning algorithm.

4. Reinforcement ML…

Reinforcement learning belongs to a bigger class of machine learning algorithm. In reinforcement learning, there’s a mapping from input to output. It is a type of machine learning approach which allows an agent to learn by trial and error in an interactive environment using feedback from their actions and experiences.

However, both supervised and reinforcement ML techniques use the mapping between input and output. Where supervised ML provides an accurate set of tasks feedback to an agent. On the other side, reinforcement learning approach for positive and negative signals behavior, it uses rewards and punishment.

Usage of Machine Learning…

. Image recognition

. Speech recognition

. Traffic prediction

. Product recommendations

. Self-driven cars

. Email spam and malware filtering

. Virtual personal assistant

. Online fraud detection

. Stock market trading

. Medical diagnosis

. Automatic language translation

Career in Machine Learning…

Salaries of ML specialists depend on various factors such as geographical location, role, and years of experience. ML specialists in USA make about $150,000 per year. Some of the top companies like eBay, Twitter, AirBnB, and Wish are ready to pay developers anything from $200,000 to $335,000. So surely the future career of ML is very bright and vivid.