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Understanding Machine Learning Algorithms

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The most trending concept, machine learning is the learning demonstrated by machines based on some data by which they function on their own and improve over time by experiences. Some examples where machine learning can be used are in data mining, natural language processing, image recognition, video games, expert systems, etc.

Machine Learning was coined in 1959 by Arthur Samuel, an American.

It is a science of getting computers to learn and improve their performance when they are not explicitly programmed. In layman terms, it is the way of teaching computers to learn concepts using data and improve from experiences rather than programming it. It focuses on developing programs that access data and uses it to learn and improve on its own.

Data collection, Data preparation, model building, training are few important steps used in machine learning. The models used in machine learning can be customized and can be made flexible or complicated depending on the need.

Machine learning is a subset of Artificial intelligence, a term which is commonly used these days for various processes. AI is the intelligence displayed by machines to automate systems and processes. AI is used in speech recognition, mobile applications, interactive voice responses etc.

Usage and implementation of Machine Learning

The first question which often crops up in the minds of many people is that what is the need of studying machine learning, how is it going to ease one’s life, so here are the answers to it:

  • Global demand in automating things
  • Harnessing the power of data which is leading to transformation and innovation in anything and everything we do. This will eradicate redundancies and increase efficiencies.
  • A vibrant tool which is fun to learn, it is a combination of innovation and business process engineering.
  • Machine learning is the future of technology
  • Automation of models which can be used in problem-solving, reengineering, improving a process or even auditing.

Machine Learning Algorithms

Machine learning constructs algorithms which can make predictions on data and analyze it on its own. These algorithms are a set of rules, processes to be followed by machines in calculations or other operations while learning. R and Python are widely and commonly used languages which provide machine learning capabilities.

Types of Machine Learning Algorithms

1. Supervised learning

This involves creating rules to map inputs and outputs, the inputs are fed into the machines, and the desired output is also entered. A general rule is then learned which maps the two. E.g. Neural Networks, Linear Regression, Decision trees, Naïve Bayes, Support Vector Machines,

2. Semi-supervised learning

This falls in between the supervised and unsupervised learning. The inputs are given. However, the output is partially known. The training given doesn’t have the complete target output dimensions.

3. Unsupervised learning

It is an open learning tool where only no patterns or labels are given. The goals and means are left open for the machine to explore and found. Eg. K Means clustering, association rules.

4. Reinforcement learning

Data are given as feedback in the form of punishment or rewards is known in a dynamic environment. Basis the reinforcement method, the methods are decided. Eg. Temporal Difference, Q-learning, Deep Adversarial Networks. Example of applications are self-driving cars, robotic hands, computer led board games.

5. Active Learning

A special case in machine learning which is interactive, basis the desired output the algorithm can question the user and decide the way.

Best ways to master Machine Learning Algorithms

Machine learning algorithms cannot be mastered unless they are studied and experienced by oneself. While there are sufficient data and resources available to read and understand algorithms, one must choose different types of algorithms and try applying it before reaching a conclusion.

There are many online courses available to read and understand machine learning processes, and there are many classroom-based teaching methods also to gain machine learning capabilities.

However, if a student is looking at mastering machine learning through self-starter way then he or she should ensure that he or she is absolutely clear with the following concepts before jumping on the basics of machine learning:

  • Statistics – Learn the foundations of statistics and programming – python theories, statistical concepts like Bayesian probability, maths concepts like algebra, calculus.
  • Absorb and apply – absorb knowledge as much as possible and apply it in various scenarios. Ask questions and logic behind scenarios and try to solve it your way.
  • Practice – Practice in various forms, model building tuning and evaluation, real datasets, etc.
  • Take up machine learning projects – once we are clear with foundation and sufficient practice, take up machine learning projects. Write algorithms using R and Python theories.

Here are a few tips to undertake the self-study of machine learning and be successful in it:

  1. Set timelines and milestones
  2. Go one step at a time, gain confidence step by step and then move to advanced techniques
  3. Alternate between practice and theories
  4. Do algorithms yourself from the scratch
  5. Try situations and use probabilities so as to test the success of algorithms in various situations.
  6. Don’t shirk from whys because the more you ask you more become clearer with concepts.

How to choose the right machine algorithms

As we have been discussing time and again in this article that machine learning algorithms have to be tried and tested multiple times to reach to an appropriate one. Choosing the right Machine Algorithm may seem like a humungous task but again going back and referring to the cheat sheets available on the internet will make your task a lot easier.

What are cheat sheets in machine learning?

These are brief visual and diagrammatic explanation of machine learning concepts with codes, examples and link to resources. There are a plethora of cheat sheets available for ML, however, choosing the right one is very critical and important to crack the right code for ML. Cheat sheets have diagrams and codes for R, Python, Probability, etc. One example of a cheat sheet is present in Annexure I.

Machine Learning Concept explained in the flowchart:

Understanding Machine Learning Algorithms

Machine Learning Concept

Examples of machine learning

One of the widely experienced and boasted about examples of Machine learning in today’s era is facebook. The New feed function of Facebook uses learning to personalize the feed of each user of Facebook. The user gets to see the news which is of his interest and the recent postings of his friends present in the friend’s list.

If you google about a product or an article, the similar results will show up on the news feed of facebook which indicates that the system is working to identify the patterns of using the internet by a user, it is using statistical analysis and predictive analytics to populate and update new feed everytime the user logs on to Facebook. The new feed is different for different users and is never the same for a user everytime he logs in to Facebook.

Similarly, in consumer-based online websites, the recommendations are based on the user’s searches on various products. There are many factors involved in recommendations like price, choice, category, colours, recency, etc.

Machine learning plays a huge role in consumer marketing also. Business intelligence uses machine learning to help consumers make the right choices and saving their time by already giving them recommendations by filtering their likings for their convenience and time.

Nowadays machine learning is used in recruitment and selection also; many web portals use machine learning to screen and shortlist people based on the skillset and job description fed into the system and filter the right candidates out from the system. This saves a lot of time of the recruiter, and he identifies the best fit for the job.

Future of Machine Learning

Machine learning has a promising future, as the artificial intelligence is making its space in the market, machine learning automatically is gaining popularity among the companies. It is a revolutionary technology bringing in positive changes in the business outcomes. The best part of the concept is that it is logical and based on predictive analysis rather than random behaviour.

The machine learning algorithm learns from the empirical data thus drawing patterns and analysis leading to results, therefore nothing can be really judged as wrong in the machine learning. The drawn-out patterns are similar to the expectations. This way if machine learning collaborates with human minds, it is ONLY going to bring out the best of the results, never explored in the past.

Example, in medical sciences, if a large amount of data is stored and analyzed on a regular basis, the results which come out will be far more realistic than a 10-minute consultation with doctor basis a short-term analysis. These synergies will help in maximizing human efforts and work in a smarter way.

Many companies have started using ML including Google, Amazon, Microsoft, IBM, etc and still researching on the right tools and methods to use the correct algorithms to devise the most meaningful results for machine learning.

Guest Blogger (Data Science) at Digital Vidya. A passionate writer who dedicated the last 10 years of his life to content, marketing and business strategy. He graduated from Shaheed Sukhdev College of Business Studies (Delhi University) as a Bachelor of Business Studies majoring in Finance. He is a die-hard audiophile & Gourmet foodie.

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