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Introduction to Machine Learning
28-05-2020
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Introduction to Machine Learning

We are living in the primitive age of machines while the future of machines is enormous and is beyond the scope of imagination. In the present world, these machines or the automatons need to be programmed before they begin following the instructions. But what if the machine starts to learn on its own from experience, work like us, and feel like us, does things more accurately than us and might even start a war of their own. Now, these things sound fascinating right. Let’s just remember this is just the beginning of the new era. 

Machine learning is an idea where it permits the machine to be trained from instances and experience and that too exclusively of being explicitly programmed. So instead of you writing the code, what you do is feed the data to the generic algorithm and the algorithm or the machine will instil logic based on the given data. 

Features of Machine Learning

  • It uses the data to detect patterns in a data set and adjust the program action.
  • It concentrates on the advancement of computer programs with the intention of teaching themselves to develop and modify when exposed to new and different data.
  • It facilitates computers to find hidden insights using an iterative algorithm without being explicitly programmed.

 

Why Machine Learning?

We are surrounded by a lot of examples of machine learning and a lot of which is something that we cannot live without. For example,

1. Google Maps: Now Google Maps is probably the app we use whenever we go out and require assistance in the direction and traffic. In spite of the heavy traffic, you are on the best and fastest route. But how does it know that? Well, it's a combination of people currently using the service, the historic data of the route collected over time and a few tricks acquired from other companies. Now everyone using maps is providing their location, the average speed, the route in which they are travelling, which in turn has Google collect massive data about the traffic which makes them predict the upcoming traffic and adjusts your route according to it.

2. Amazon: The product recommendation application, but suppose you check an item on Amazon but you do not buy it then and there, but the next day you are watching videos on YouTube and suddenly you see an ad for the same item, you switch to Facebook chatting with your friends and there also you see the same ad. So how does this happen? Clearly these happen because Google traces your search history and suggests ads based on your search. This is one of the best applications of machine learning; in fact, you won't believe that 35% of Amazon’s revenue is generated just only by-product recommendation.

3. Self Driving Car: The coolest application of machine learning by far, it is here and people are already using it. Now machine learning plays an important role in the self-driving car. Tesla is the leader in this business and their current artificial intelligence is driven by the hardware manufacturer in media which is based on a type of machine learning that is the unsupervised learning algorithm.

 

Machine Learning Algorithm Steps

Data collection: This stage involves the collection of all the relevant data from various sources.

Data wrangling: The process of cleaning and converting the raw data into a format that allows convenient consumption.

Data analyzation: After the data have been cleaned and converted into a particular format, the data is analyzed to select and filter the data required to prepare them all.

Data selection: Not all the data is required for a particular model, so you have to select certain features.

Training: After selecting the features, the algorithm is trained on the training dataset through which the algorithm understands the pattern and the rules which govern the data.

Evaluation: After Training, the testing dataset determines the accuracy of our model.

Deployment: The final stage, if the speed and the accuracy of the model are acceptable then that model is deployed in the real system.

Maintenance: After the model is deployed based upon its performance, the model is updated and improved and if there is a dip in the performance, the model is retrained.

 

Machine Learning Classification

It is broadly classified into three major tasks:

1. Supervised Learning: The simplest form of machine learning is supervised learning and it is the one where you have input variables like X and an output variable Y. You use an algorithm to learn the mapping function from the input to the output, so in simple terms, it implies Y=f(X).

The objective is to fairly accurately map the mapping functions so fine so that every time you get some new input data X the machine can easily calculate the output variables Y for the data. In simple terms, in the supervised machine learning algorithm, every instance of the training data set consists of input attributes and expected outputs. The training data set can take any kind of data as input, like values of dataset’s rows, the pixel of an image or even audio frequency histogram.

This category of machine learning is termed as supervised learning because the process of, an algorithm learning from the training data set can be thought of as the teacher teaching his students, the algorithm continuously predicts the result on the basis of the training data and is continuously corrected by the teacher. The learning continues until the algorithm is an acceptable level of performance.

Speech Recognition: Any speech recognition or any speech automated system on your mobile phone trains your voice and then starts working. 

Biometric Attendance: You can train the machine with inputs of your biometric identity; it can be your thumb, your wrist, your face. As soon as the machine is trained it can authenticate your upcoming input and can simply recognize you. Nowadays this is being implemented in all these smartphones that we have.

The top algorithms presently used for supervised learning are:

  • Polynomial regression
  • Random forest
  • Linear regression
  • Logistic regression
  • Decision trees
  • K-nearest neighbours
  • Naive Bayes

 

2. Unsupervised Learning: Sometimes the command data is unstructured and unlabeled so it becomes very difficult to classify that data into different categories, so unsupervised learning helps to solve this problem. This learning is used to cluster the input data into classes on the basis of the statistical properties, now the training data is the collection of information without any label. Precisely unsupervised learning is where you just have the input data which is the X and no matching output variables.

The goal of unsupervised learning is to model the underlying structure or the distribution in the data in order, to learn more about the data. This is done by clustering models that concentrate on categorizing cluster of related records and grouping the records according to the group to which they fit in and this is completed without the help of prior knowledge in relation to the cluster and their characteristics.

In fact, we may not even know exactly how many groups to look for, but the models are often referred to as unsupervised learning model since there is no external pattern to judge the model’s categorization performance and there are no right or wrong answers to these models.

Market Basket Analysis: One of the key techniques used by large retailers to uncover the association between items and it works all on unsupervised learning. It works by looking for a combination of items that occurred together frequently in the transaction. To put in another way it allows retailers to identify the relationships between the items that people buy. 

The top algorithms presently used for unsupervised learning are:

  • Partial least squares
  • Fuzzy means
  • Singular value decomposition
  • K-means clustering
  • Apriori
  • Hierarchical clustering
  • Principal component analysis

3. Reinforcement Learning: This is a part of machine learning where an agent is put in an environment and it learns to behave in this environment by performing certain actions and perceiving the returns which it acquires from those actions. This reinforcement learning is all about taking appropriate action in order to maximize the reward in a particular situation.

Under supervised learning, the training data consist of the input and consequently, the model is trained with the anticipated output itself. But when it comes to reinforcement learning there is no expected output, the reinforcement agent decides what action to take in order to perform a given task in the absence of a training dataset. It is bound to learn from its own experience now.

 

Career Opportunities in Machine Learning

Machine Learning is incredibly popular because it eases a lot of human efforts and improves machine functioning by allowing machines to be trained for themself. Accordingly, there are lots of career paths in Machine learning that are in demand and paying well. 

  1. Machine Learning Engineer: Working on programming languages such as Python, Java, Scala etc., executes a variety of machine learning experiments using the right machine learning libraries which are used to make business decisions by the company executives. Few key skills essential in support of this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc. But, how is a Machine Learning Engineer not the same as a Data Scientist, it is because a Data Scientist evaluates data to create actionable insights. Analyzing data to design different machine learning algorithms to facilitate autonomous functioning with minimum human intervention. In a nutshell, a Data Scientists originate the necessary outputs for humans whereas a Machine Learning Engineer design them for machines 

  2. Data Scientist: Using advanced analytical skills, like Machine Learning and Predictive Modeling, Data Scientists gather, evaluate and understand large amounts of data and produce actionable insights. Therefore Machine Learning is actually an essential skill for a Data Scientist with addition to other skills like data mining, data of statistical research method etc. As well as, understanding of big data platforms and tools, such as Hadoop, Pig, Hive, Spark, etc., programming languages such as SQL, Python, Scala, Perl etc. are required for a Data Scientist.

  3. NLP Scientist: Natural language processing consists of providing machines with the capability to understand human language. As a result, machines can be able to eventually talk with humans in their own language. NLP Scientists basically assist in the making of the machine that is capable of learning various speech patterns of speech and also translate spoken words into other languages. NLP scientists must have excellent fluency in spelling, sentence structure and language rules of at least one language besides machine learning so that a machine can get hold of similar skills. 

  4. Business Intelligence Developer: Uses Data Analytics and Machine Learning to collect, analyze and interpret large amounts of data and produce information that can be acted upon or information that gives enough insight into the future that the actions that should be taken become clear for decision-makers or business executives which are used in making business decisions. Business Intelligence Developer requires data of both relational and multidimensional databases together with programming languages like SQL, Scala, Python, Perl, etc.

  5. Human-Centred Machine Learning Designer: Build up various systems that know how to carry out human-centred Machine Learning based on information processing and pattern recognition. Human-Centred Machine Learning links to Machine Learning algorithms centred among humans. For example, Netflix, a media-service provider, provides its viewers, movie selection as per their liking and establishes a smart viewer experience. It lets the machine study the likings of individuals without any need for bulky programs.

 

 Machine Learning Engineer Key Responsibilities

The key responsibilities include – 

  • Carry out statistical analysis
  • Fine-tuning test results 
  • Train and retrain systems 
  • Work on frameworks
  • Undertaking machine learning experiments and test
  • Designing machine learning programs  
  • Building deep learning systems to a variety of use case scenarios based on the business needs 
  • Executing suitable AI/ML algorithms 

Machine Learning Jobs Skill Requirements 

As an ML engineer, the industry looks upon the following necessities from candidates. 

  • Ability to write code in Java and Python 
  • Knowledge on basics of math and probability
  • Good understanding and strong knowledge in algorithms and statistics
  • Appreciation of data modelling, software architecture and data structures and 
  • Past experience of working in frameworks in last job or internship
  • Reasonable communication skills 
  • Working in teams 
  • The established skill of working in Machine Learning Jobs before is an added advantage

Written By



Rini

Rini is an auto, technology enthusiast who has majored in Automotive embedded systems. Currently, she is working as a technology career counsellor at Skill At Will to help individuals identify their career paths in tech and help them build a better career. She enjoys enlightening others through her technology blogs and articles.

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