Machine Learning Tutorial

When you explore the divisions of machine learning, the concept is extremely broad. There is always a beginning point even for most advanced technologies, and that is what this Machine Learning course will discuss.

Introduction to Machine Learning

Seems like you would have stumbled upon the term machine learning and must be wondering what exactly it is. Well, this machine-learning tutorial will clear out all of your confusion!

Machine learning is a field of artificial intelligence with the help of which you can perform magic! Yes, you read it right. Let’s take some real-life examples to understand this. I believe all of you must have heard of Google’s self-driving car. A car that drives by itself without any human support; is just amazing, isn’t it?

Now, how about virtual personal assistants such as Apple’s Siri or Microsoft’s Cortana? If you ask Siri what is the distance between Earth and the Moon, it will immediately reply that the distance is 384,400km.

You also must have used Google Maps. If you want to go from New Jersey to New York via road, Google Maps will show you the distance between these two places, the shortest route, and also how much traffic is there along the road.

Now, as you can see Machine learning is widespread. The concept behind the above applications is machine learning.  In simpler terms, machine learning is a subset of artificial intelligence, that powers various applications by enabling machines to learn from data and make decisions without explicit programming.

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Why do we need Machine Learning?

Machine learning is essential for handling complex data patterns beyond human capacity. It finds complex relationships in large datasets, automates decision-making, and permits predictive analysis. Applications for ml include healthcare diagnostics and personalized suggestions on internet platforms. It simplifies tasks that traditional programming would make difficult or impossible. Moreover, Machine learning is essential for handling complex issues and developing technology across a wide range of industries due to its flexibility and ability to learn and grow over time.

How Does Machine Learning Work?

Machine learning is made up of three parts:

• The computational algorithm is at the core of making determinations.
• Variables and features that make up the decision.
• Base knowledge for which the answer is known enables or trains the system to learn.

Let us consider that the model doesn’t know anything initially. My model needs to be fed data in order to be trained before it can begin to predict. The larger the data better the model will perform. We can evaluate the performance of the model once the data has been fed into it. We also know that the model may or may not work well. At such times adjustments are made until the model’s output agrees with the known answer.

History of Machine Learning

The term machine learning was first coined in 1959 by Arthur Samuel, an IBM employee, and pioneer in the field of computer gaming and artificial intelligence.

By the early 1960s an experimental “learning machine” with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. This machine was frequently trained by a human operator to recognize patterns and was equipped with a ‘goof’ button to reevaluate incorrect decisions.

In 1981, a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.

Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, and the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning to train it to classify cancerous moles.

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Features of Machine Learning

Automation: Your Gmail has a lot of emails in the spam folder. Most of these emails are either unwanted forwards or sometimes even potentially harmful online links. Now you must be wondering how Gmail knows which emails need to be segregated in the said folder. The answer is Machine learning. Gmail uses machine learning algorithms to recognize spam emails by analyzing various features such as content, sender information, user actions, and email metadata. By using labeled data to identify patterns, these algorithms are able to differentiate between correct and spam emails. As a result, they are able to filter away unwanted or questionable communications from users’ inboxes and continuously improve their accuracy by adjusting to new spam methods and user input. 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 need  to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent.

Improved customer experience: One of the primary ways used by businesses 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 companies nowadays have a chatting bot that is  available 24×7. An example of this is Eva from AirAsia Airlines .

These bots provide intelligent answers, sometimes making it hard to tell if you’re talking to a bot. They use both Machine Learning and Artificial Intelligence to deliver a flawless and enhanced user experience, adapting and learning from interactions to simulate human-like conversations.

Automated data visualization: In the past, we have seen a huge amount of data being generated by companies and individuals. Take the  example of companies like Google, Twitter, and 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 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 to increase productivity in their processes as AutoViz helps in business growth by enabling quick and efficient data visualization, facilitating informed decision-making and strategy development

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.

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. To build a strong foundation and cover basic concepts you can enroll in a Python machine learning course that will help you power ahead in your career.

Types of Machine Learning Algorithms

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 recognize different breeds of dogs that 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 can find the underlying pattern in the data.

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).

Since the algorithm’s output is known previously, modifications are made to improve its performance during the supervised learning process. With the help of the dataset, the algorithm is trained and then adjusted until it performs well enough.

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

Types of Supervised Machine Learning Algorithms

One of the most time-consuming and difficult processes in your journey of Machine Learning is learning about the diverse range of algorithms.  There are many algorithms in Machine Learning and you don’t need to know them all to get started. But I would suggest, once you start practicing Machine Learning, start learning about the most popular algorithms out there such as

SVM

Support Vector Machine (SVM) is a popular machine learning algorithm used for both classification and regression tasks. It maximizes the distance between several classes by determining the optimal decision boundary between data points. SVM is a flexible and effective tool in the field of machine learning because it works well in high-dimensional spaces and can handle complex datasets. It does this by transforming data and classifying points into different categories using a method known as the kernel trick. It is predicated on three concepts:

1. Maximum margin classifiers
2. Support vector classifiers
3. Support vector machines

Decision Tree Classifier

A decision tree is a popular machine learning classifier. So, a decision tree as the name states has an inverted tree-like structure. The topmost node in the tree is known as the root node and the nodes at the bottom of the tree are known as the leaf nodes. Every node has a test condition and based on that test condition, the tree splits into either its left child or right child.

Let’s go through this example on a decision tree. Here, we are trying to determine whether a person would watch the movie “Avengers” based on a series of test conditions.

Here, the test condition on the root node is “likes action films”. If the result is true, you go to the left child, else to the right child. If you like action films, then on the left child, there is another test condition, “Movie length greater than 2 hours”. So, if this evaluates to true, you go again to the left child, i.e., you are fine watching a movie which is greater than 2 hours. Again, when you go to the left child, there is another test condition, “Likes Robert Downey Jr”. Again, if this is true, it means you are looking forward to watching “Avengers”. So, this is how a decision tree classifier works.

KNN Algorithm 

KNN belongs to a group of lazy learners. As opposed to eager learners such as logistic regression, SVM, and neural nets, lazy learners just store the training data in memory. During the training phase, KNN arranges the data (sort of indexing process) to find the closest neighbors 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 the 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 KNNs who do not involve many computations and hence train faster.

K-means Clustering

K-means clustering is a non-hierarchical approach to forming good clusters. For K-Means modeling, the number of clusters needs to be determined before the model is prepared. These K values are measured by certain evaluation techniques once the model is run. K-means clustering is widely used in large dataset applications.

As this algorithm is based on distance calculation from each observation to the centroids present and this is an iterative process, the data needs to be in a proper format. In case the dataset has variables with different units of measure, one should undertake the process of Scaling to bring all the variables into one unit/ measure, for further algorithm processing. There are 2 methods of Scaling:  Z Scaling and Min-Max Scaling. In Z Scaling, features will be rescaled and it has the properties of a standard normal distribution. In Min Max Scaling, the data is scaled to a fixed range – 0 to 1. Although the cost of having this bounded range of smaller standard deviations can suppress the effect of outliners.

The formula for Z scaling is:

The formula for min-max scaling is:

Random Forest Algorithm

As the name suggests, a random forest is nothing but a collection of multiple decision tree models. Random forest is a supervised Machine Learning algorithm. This algorithm creates a set of decision trees from a few randomly selected subsets of the training set and picks predictions from each tree. Then using voting, the random forest algorithm selects the best solution.

Random Forest Example

Let us understand the concept of a random forest with the help of a pictorial example.

Say, we have four samples as shown below:

A random forest algorithm will create four decision trees taking inputs from subsets, for example,

The random forest algorithm works well because it aggregates many decision trees, which reduces the effect of noisy results, whereas the prediction results of a single decision tree may be prone to noise.

Random forest algorithms can be applied to build both classification and regression models.

• In the case of a random forest classification model, each decision tree votes; then to get the final result, the most popular prediction class is chosen.
• In the case of a random forest regression model, the mean of all decision tree results is considered the final result.

Advantages and Disadvantages 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, e-commerce websites like Amazon and Flipkart serve to understand the browsing behaviors and purchase histories of their 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 it 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 when 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 this model doesn’t need to be the best for the problem.

• High error-susceptibility

Machine Learning works on its own, but it can make mistakes. Consider teaching a machine with a limited amount of data. Because it didn’t learn from a variety of examples, it can begin to make incorrect predictions. This may lead to people seeing things that are unexpected to them. These errors can lead to other issues in machine learning and may be ignored for some time. But when these ignored issues start causing problems, it takes so much time to identify and then implement the solution.

Applications of Machine Engineering

Machine learning is a buzzword for today’s technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:

Image Recognition

Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion:

Facebook provides us with a feature of auto friend tagging suggestions. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning’s face detection and recognition algorithm.

It is based on the Facebook project named “Deep Face,” which is responsible for face recognition and person identification in the picture.

Speech Recognition

While using Google, we get an option of “Search by voice,” which comes under speech recognition, and it’s a popular application of machine learning.

Speech recognition is a process of converting voice instructions into text, and it is also known as “Speech to text”, or “Computer speech recognition.” At present, machine learning algorithms are widely used in various applications of speech recognition. Google Assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow voice instructions.

Traffic prediction

If we want to visit a new place, we take the help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions.

It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:

• Real-Time location of the vehicle from Google Map app and sensors
• Average time has taken on past days at the same time.

Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends it back to its database to improve its performance.

Product recommendations

Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendations to the user. Whenever we search for some product on Amazon, then we start getting an advertisement for the same product while internet surfing on the same browser, and this is because of machine learning.

Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest.

Similarly, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.

Self-driving cars

One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company, is working on self-driving cars. It is using an unsupervised learning method to train the car models to detect people and objects while driving.

Email Spam and Malware Filtering

Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail:

• Content Filter
• Header filter
• General blacklists filter
• Rules-based filters
• Permission filters

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, and 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 the future of Machine Learning.

We hope this tutorial helps you gain knowledge of Machine Learning Training. If you are looking to learn Online Machine Learning Course in a systematic manner with expert guidance and support then you can enroll to our Machine Learning Course Online.