What is Machine Learning – A Complete Guide

 

• Definition
• Why Machine Learning?
• Its  advantages
• Applications
• Types
• Best Programming language to go with Machine learning
• Difference between ML, AI and DL
• How to get started

Definition

Machine learning is the branch of Artificial Intelligence (AI) that provides the system the ability to automatically learn from the data, predict from the data, make decisions and improve performance with experiences.

Its main aim is to allow the computers to learn automatically without human interventions and adjust actions accordingly.

In short, the area of Machine Learning deals with the design of programs that can learn rules from data, adapt to change and improve its performance efficiently. Through the course of this blog, we will learn more about what is machine learning, and why it is important, the different types of ML and how to get started.

Why Machine Learning?

Now that we have a basic understanding of what is machine learning, let us learn why ML is used. Nowadays, Machine learning has become crucial as computers are expected to solve increasingly complex problems and become more integrated into our daily lives. Machine learning is important because of its wide range of applications, ability to adapt and provide solutions to complex problems efficiently and quickly.

The objective of Machine learning is to develop skills of utilizing recent machine learning software for solving practical problems and gain experience of doing independent study and research.

Every part of these complex websites like Facebook, Amazon, or Netflix also contains multiple machine learning models. Outside of business applications, Machine learning has had amazing influence on the manner data-driven analysis is completed these days.

The automatic recommendations of which movies to watch, what food to order or which product to buy and recognizing friends in your photo, many devices, modern websites have machine learning algorithms at their core.

Advantages of Machine Learning

• Machine learning makes your computer give solutions faster and efficiently.
• It doesn’t require any translator to translate the code. The code is directly understood by the computer.
• There is no need for human intervention.
• Algorithms of Machine learning gain experience, they keep improving in accuracy.

Difference between ML, AI and DL

Artificial Intelligence is the technique which enables computers to mimic human behaviour or the ability of machines to imitate human behaviour. It is able to resolve problems in a smart way, from the simplest to the most complex of the algorithms. It consists of devices or systems that can handle all sorts of tasks on their own. In this, a program can sense, reason, act and adapt.

Machine learning is a subset or a subpart of Artificial intelligence that allows a system to automatically learn and improve from experience. It uses statistical methods to enable machines to improve performance. It is the algorithms whose performance is improved as they are exposed to more data over time.

Deep learning is the subset or a subpart of Machine learning that uses complex algorithms and deep neural nets to train a model. It makes the computation of multi-layer neural networks feasible. It is able to adapt themselves through repetitive training to uncover hidden patterns and insights.

Applications of Machine Learning

1. Image and Speech Recognition-

Image recognition is used to identify objects, persons, places, digital images etc. Automatic friend tagging suggestion provided by Facebook comes under Image recognition and face detection.

Speech recognition is the process of converting voice instructions into text. It is also called “Computer speech recognition”. Option of ‘Search by voice’ while using Google comes under speech recognition. Alexa and Contana are also using speech recognition technology to follow voice instructions.

2. Medical Diagnosis –

The inputs are the relevant information about the patient and the classes are the illnesses. The input contains the patient’s age, gender, past medical history and current symptoms. Some tests may not have been applied to patients and thus inputs would be missing. Tests may take time, may be costly.

Machine learning is used for Disease diagnoses. With this, Medical technology is also growing rapidly and able to build 3D models that can predict the exact position of lesions in the brain.

3. Automatic Language translation –

Google’s Google Neural Machine Translation (GNMT) provides the feature of converting text into our known language, which is Neural Machine Learning that translates the text into our familiar language that is called automatic translation.

4. Online Fraud Detection –

Machine learning makes our online transaction safe and secure by detecting fraud transactions. There are several ways that a fraudulent transaction can take place such as fake ids, steal money, fake accounts in the middle of transaction. So to detect all these fakes, Feed Forward Neural Network helps us by checking whether it is a genuine transaction or a fraud transaction.

5. Self-Driving cars –

The most popular car manufacturing company i.e., Tesla, is working on self driving cars. It uses unsupervised learning methods to train the car models to detect people and objects while driving.

Best Programming language to go with Machine learning

Python is the best programming language in preference for ML for some of the giants in the IT world including Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber, Amazon, and Reddit as it is a general purpose and high level language. Python is the best language for machine learning different from other languages because of its –

1. Vast Collection of Libraries and Packages

In-built libraries and packages of python provide base-level code so machine learning engineers need not to start writing from scratch. As ML requires data processing in continuity and Python has in-built libraries, packages for almost every task. This helps ML engineers in reducing development time and improving productivity when working with complex machine learning applications. The best part of these libraries and packages is that it contains zero learning curve, once you know the basics of Python programming.

Some of the python libraries for Machine learning are Numpy, Pandas, scikit-learn, keras etc.

2. Code Readability

The math behind machine learning is usually complicated. Thus, Code readability is necessary to successfully implement complicated machine learning algorithms and versatile workflows. Simple syntax of Python and the importance it puts on code readability makes it easy for machine learning engineers to focus on what to write instead of thinking about how to write. Code readability makes it easy for machine learning practitioners to easily exchange ideas and algorithms.

3. Flexibility

Python offers the best flexibility that helps machine learning engineers to choose the programming style based on problems like- sometimes it would be beneficial to capture the state in an object while other times the problem might require passing around  functions as parameters. Python basically helps in choosing either of the approaches and minimizes the likelihood of errors.

Types of Machine Learning

There are mainly 4 types of Machine learning:

• Supervised Learning: In this, data scientists provide input, output and feedback to build models. It means it reproduces outputs known from the training set. Example of Algorithms – Linear regressions (risk assessment), Decision tree (Pricing)
• Unsupervised Learning: It uses deep learning to arrive at conclusion and patterns through unlabeled training data. Example of Algorithms – A priori (searcher), K-means clustering (performance monitoring)
• Semi-supervised Learning: It builds a model through a mix of labelled and unlabeled data, a set of categories. Example of Algorithms – Generating adversarial networks (data creation), Self-trained Naive bayes classifier (Natural language processing)
• Reinforcement Learning: It is a kind of self interpreting but based on a system of rewards and punishments learned through trial and error. Example of Algorithms – Q-learning (policy creation), Model-based value estimation (linear tasks).

This brings us to the end of the blog on what is machine learning. We hope that you were able to gain comprehensive knowledge from the same. Happy Learning!