Machine Learning Tutorial For New Starter

This Machine Learning tutorial provides basic and intermediate concepts of machine learning. It is designed for students and working professionals who are a complete starter. At the end of this tutorial, you won’t be an expert at Machine Learning but you will be able to make machine learning models that can perform complex tasks such as predicting the price of a house or recognising the species of an Iris from the dimensions of its petal and sepal lengths. If you are not a complete beginner and are a bit familiar with Machine Learning, I would suggest starting with subtopic eight i.e, Types of Machine Learning.

Before jumping into the tutorial, you should be familiar with Pandas and NumPy. This is important to understand the implementation part. There are no prerequisites for understanding the theory of it.

Here are the subtopics that we are going to discuss in this tutorial:

What is Machine Learning?

Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”.

In simple terms, Machine Learning is an application of Artificial Intelligence (AI) which enables a program(software) to learn from the experiences and improve their self at a task without being explicitly programmed. For example, how would you write a program that can identify fruits based on their various properties, such as colour, shape, size or any other property?

One approach is to hardcode everything, make some rules and use them to identify the fruits. This may seem the only way and work but one can never make perfect rules that apply on all cases. This problem can be easily solved using machine learning without any rules which makes it more robust and practical. You will see how we will use machine learning to do this task in the coming sections.

Thus, we can say that Machine Learning is the study of making machines more human-like in their behaviour and decision making by giving them the ability to learn with minimum human intervention, i.e., no explicit programming. Now the question arises, how can a program attain any experience and from where does it learn? The answer is data. Data is also called the fuel for Machine Learning and we can safely say that there is no machine learning without data.

You may be wondering that the term Machine Learning has been introduced in 1959 which is a long way back, then why haven’t there been any mention of it till recent years? You may want to note that Machine Learning needs a huge computational power, a lot of data and devices which are capable of storing such vast data. We have only recently reached a point where we now have all these requirements and can practice Machine Learning.

How is it different from traditional programming?

Are you wondering how is Machine Learning different from traditional programming? Well, in traditional programming, we would feed the input data and a well written and tested program into a machine to generate output. When it comes to machine learning, input data along with the output associated with the data is fed into the machine during the learning phase, and it works out a program for itself.

To understand this better, refer to the illustration below:

Don’t worry if you are not able to understand this completely, in the coming sections you will get a better understanding. You may want to come back to this figure once we discuss the steps that are involved in machine learning to clear all your doubts.

Why do we need Machine Learning?

Machine Learning today has all the attention it needs. Machine Learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning.

With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.

Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals.

History of Machine Learning

Nowadays, we can see some amazing applications of ML such as in self-driving cars, Natural Language Processing and many more. But Machine learning has been here for over 70 years now. It all started in 1943, when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons, and how they work. They decided to create a model of this using an electrical circuit, and therefore, the neural network was born.

In 1950, Alan Turing created the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human. In 1952, Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.

Just after a few years, in 1957, Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulates the thought processes of the human brain. Later, in 1967, the “nearest neighbour” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for travelling salesmen, starting at a random city but ensuring they visit all cities during a short tour.

But we can say that in the 1990s we saw a big change. Now work on machine learning shifted from a knowledge-driven approach to a data-driven approach.  Scientists began to create programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results.

In 1997, IBM’s Deep Blue became the first computer chess-playing system to beat a reigning world chess champion. Deep Blue used the computing power in the 1990s to perform large-scale searches of potential moves and select the best move. Just a decade before this, in 2006, Geoffrey Hinton created the term “deep learning” to explain new algorithms that help computers distinguish objects and text in images and videos.

Machine Learning at Present

The year 2012 saw the publication of an influential research paper by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever, describing a model that can dramatically reduce the error rate in image recognition systems. Meanwhile, Google’s X Lab developed a machine learning algorithm capable of autonomously browsing YouTube videos to identify the videos that contain cats. In 2016 AlphaGo (created by researchers at Google DeepMind to play the ancient Chinese game of Go) won four out of five matches against Lee Sedol, who has been the world’s top Go player for over a decade.

And now in 2020, OpenAI released GPT-3 which is the most powerful language model ever. It can write creative fiction, generate functioning code, compose thoughtful business memos and much more. Its possible use cases are limited only by our imaginations.

Features of Machine Learning

Automation: Nowadays in your Gmail account, there is a spam folder that contains all the spam emails. You might be wondering how does Gmail know that all these emails are spam? This is the work of Machine Learning. It recognises the spam emails and thus, it is easy to automate this process. The ability to automate repetitive tasks is one of the biggest characteristics of machine learning. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent.

Improved customer experience: For any business, one of the most crucial ways to drive engagement, promote brand loyalty and establish long-lasting customer relationships is by providing a customized experience and providing better services. Machine Learning helps us to achieve both of them. Have you ever noticed that whenever you open any shopping site or see any ads on the internet, they are mostly about something that you recently searched for? This is because machine learning has enabled us to make amazing recommendation systems that are accurate. They help us customize the user experience. Now coming to the service, most of the companies nowadays have a chatting bot with them that are available 24×7. An example of this is Eva from AirAsia airlines. These bots provide intelligent answers and sometimes you might even not notice that you are having a conversation with a bot. These bots use Machine Learning, which helps them to provide a good user experience.

Automated data visualization: In the past, we have seen a huge amount of data being generated by companies and individuals. Take an example of companies like Google, Twitter, Facebook. How much data are they generating per day? We can use this data and visualize the notable relationships, thus giving businesses the ability to make better decisions that can actually benefit both companies as well as customers. With the help of user-friendly automated data visualization platforms such as AutoViz, businesses can obtain a wealth of new insights in an effort to increase productivity in their processes.

Business intelligence: Machine learning characteristics, when merged with big data analytics can help companies to find solutions to the problems that can help the businesses to grow and generate more profit. From retail to financial services to healthcare, and many more, ML has already become one of the most effective technologies to boost business operations.

What is the best language for Machine Learning?

Although there are many languages that can be used for machine learning, according to me, Python is hands down the best programming language for Machine Learning applications. This is due to the various benefits mentioned in the section below. Other programming languages that could to use for Machine Learning Applications are R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala. R is also a really good language to get started with machine learning.

Python is famous for its readability and relatively lower complexity as compared to other programming languages. Machine Learning applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement. Python helps in reducing this burden with quick implementation for the Machine Learning engineer to validate an idea. You can check out the Python Tutorial to get a basic understanding of the language. Another benefit of using Python in Machine Learning is the pre-built libraries. There are different packages for a different type of applications, as mentioned below:

1. Numpy, OpenCV, and Scikit are used when working with images
2. NLTK along with Numpy and Scikit again when working with text
3. Librosa for audio applications
4. Matplotlib, Seaborn, and Scikit for data representation
5. TensorFlow and Pytorch for Deep Learning applications
6. Scipy for Scientific Computing
7. Django for integrating web applications
8. Pandas for high-level data structures and analysis

Python provides flexibility in choosing between object-oriented programming or scripting. There is also no need to recompile the code; developers can implement any changes and instantly see the results. You can use Python along with other languages to achieve the desired functionality and results.

Python is a versatile programming language and can run on any platform including Windows, MacOS, Linux, Unix, and others. While migrating from one platform to another, the code needs some minor adaptations and changes, and it is ready to work on the new platform.

Here is a summary of the benefits of using Python for Machine Learning problems:

Types of Machine Learning

Machine learning has been broadly categorized into three categories:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

What is Supervised Learning?

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it?

You may show him/her a dog and say “here is a dog” and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognise different breeds of dogs which he hasn’t even seen.

Similarly, in Supervised Learning, we have two sets of variables. One is called the target variable, or labels (the variable we want to predict) and features(variables that help us to predict target variables). We show the program(model) the features and the label associated with these features and then the program is able to find the underlying pattern in the data. Take this example of the dataset where we want to predict the price of the house given its size. The price which is a target variable depends upon the size which is a feature.

In a real dataset, we will have a lot more rows and more than one features like size, location, number of floors and many more.

Thus, we can say that the supervised learning model has a set of input variables (x), and an output variable (y). An algorithm identifies the mapping function between the input and output variables. The relationship is y = f(x).

The learning is monitored or supervised in the sense that we already know the output and the algorithm are corrected each time to optimise its results. The algorithm is trained over the data set and amended until it achieves an acceptable level of performance.

We can group the supervised learning problems as:

Regression problems – Used to predict future values and the model is trained with the historical data. E.g., Predicting the future price of a house.

Classification problems – Various labels train the algorithm to identify items within a specific category. E.g., Dog or cat( as mentioned in the above example), Apple or an orange, Beer or wine or water.

What is Unsupervised Learning?

This approach is the one where we have no target variables, and we have only the input variable(features) at hand. The algorithm learns by itself and discovers an impressive structure in the data.

The goal is to decipher the underlying distribution in the data to gain more knowledge about the data.

We can group the unsupervised learning problems as:

Clustering: This means bundling the input variables with the same characteristics together. E.g., grouping users based on search history

Association: Here, we discover the rules that govern meaningful associations among the data set. E.g., People who watch ‘X’ will also watch ‘Y’.

What is Reinforcement Learning?

In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period.

Reinforcement learning is different from supervised learning in the sense that there is no answer available, so the reinforcement agent decides the steps to perform a task. The machine learns from its own experiences when there is no training data set present.

In this tutorial, we are going to mainly focus on Supervised Learning and Unsupervised learning as these are quite easy to understand and implement.

Machine learning Algorithms

This may be the most time-consuming and difficult process in your journey of Machine Learning. There are many algorithms in Machine Learning and you don’t need to know them all in order to get started. But I would suggest, once you start practising Machine Learning, start learning about the most popular algorithms out there such as:

• Linear Regression
• Logistic Regression
• Decision Tree
• SVM
• Naive Bayes
• K-nearest neighbour
• K-Means
• Random Forest
• Gradient Boosting algorithms
  • GBM
  • XGBoost
  • LightGBM
  • CatBoost

Here, I am going to give a brief overview of one of the simplest algorithms in Machine learning, the K-nearest neighbour Algorithm (which is a Supervised learning algorithm) and show how we can use it for Regression as well as for classification. I would highly recommend checking the Linear Regression and Logistic Regression as we are going to implement them and compare the results with KNN(K-nearest neighbour) algorithm in the implementation part.

You may want to note that there are usually separate algorithms for regression problems and classification problems. But by modifying an algorithm, we can use it for both classifications as well as regression as you will see below

K-Nearest Neighbour algorithm

KNN belongs to a group of lazy learners. As opposed to eager learners such as logistic regression, SVM, neural nets, lazy learners just store the training data in memory. During the training phase, KNN arranges the data (sort of indexing process) in order to find the closest neighbours efficiently during the inference phase. Otherwise, it would have to compare each new case during inference with the whole dataset making it quite inefficient.

So if you are wondering what is a training phase, eager learners and lazy learners, for now just remember that training phase is when an algorithm learns from the data provided to it. For example, if you have gone through the Linear Regression algorithm linked above, during the training phase the algorithm tries to find the best fit line which is a process that includes a lot of computations and hence takes a lot of time and this type of algorithm is called eager learners. On the other hand, lazy learners are just like KNN which do not involve many computations and hence train faster.

Advantages of Machine Learning

Easily identifies trends and patterns

Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for e-commerce websites like Amazon and Flipkart, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.

Continuous Improvement

We are continuously generating new data and when we provide this data to the Machine Learning model which helps it to upgrade with time and increase its performance and accuracy. We can say it is like gaining experience as they keep improving in accuracy and efficiency. This lets them make better decisions.

Handling multidimensional and multi-variety data

Machine Learning algorithms are good at handling data that are multidimensional and multi-variety, and they can do this in dynamic or uncertain environments.

Wide Applications

You could be an e-tailer or a healthcare provider and make Machine Learning work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning

Data Acquisition

Machine Learning requires a massive amount of data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where we must wait for new data to be generated.

Time and Resources

Machine Learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.

Interpretation of Results

Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose. Sometimes, based on some analysis you might select an algorithm but it is not necessary that this model is best for the problem.

High error-susceptibility

Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of Machine Learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.

Future of Machine Learning

Machine Learning can be a competitive advantage to any company, be it a top MNC or a startup. As things that are currently being done manually will be done tomorrow by machines. With the introduction of projects such as self-driving cars, Sophia(a humanoid robot developed by Hong Kong-based company Hanson Robotics) we have already started a glimpse of what the future can be. The Machine Learning revolution will stay with us for long and so will be the future of Machine Learning.