The following topics will be covered in this Machine Learning blog:
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What is Machine Learning?
Even though there are various Machine Learning examples or applications that we use in our daily lives, people still get confused about Machine Learning, so let’s start by looking at the Machine Learning definition.
In layman’s terms, Machine Learning can be defined as the ability of a machine to learn something without having to be programmed for that specific thing. It is the field of study where computers use a massive set of data and apply algorithms for ‘training’ themselves and making predictions. Training in Machine Learning entails feeding a lot of data into the algorithm and allowing the machine itself to learn more about the processed information.
Answering whether the animal in a photo is a cat or a dog, spotting obstacles in front of a self-driving car, spam mail detection, and speech recognition of a YouTube video to generate captions are just a few examples out of a plethora of predictive Machine Learning models.
Machine Learning can also be defined as a subset of Artificial Intelligence that comprises algorithms programmed to gather information without explicit instructions at each step. It has experienced the colossal success of late.
We have often seen confusion around the use of the words Artificial Intelligence and Machine Learning. They are very much related and often seem to be used interchangeably, yet both are different. Confused? Let us elaborate on AI vs. ML vs. DL.
Go through these Top 40 Machine Learning Interview Questions and Answers to crack your interviews.
Why Machine Learning?
Let us start with an instance where a machine surpasses in a strategic game by self-learning. In 2016, the strongest Go player (Go is an abstract strategy board game invented in China more than 2,500 years ago) in the world, Lee Sedol, sat down for a match against Google DeepMind’s Machine Learning program, AlphaGo. AlphaGo won the 5-day long match.
Machine Learning has paved its way into various business industries across the world. This incredible impact of Machine Learning on propelling business growth, simplifying repetitive tasks, elevating customer satisfaction, and reaching corporate objectives is what fuels its success.
According to BCC Research, the global market for Machine Learning is expected to grow from $17.1 billion in 2021 to $90.1 billion by 2026 with a compound annual growth rate (CAGR) of 39.4% for the period of 2021-2026.
Moreover, Machine Learning Engineer is the fourth-fastest growing job as per LinkedIn.
Both Artificial Intelligence and Machine Learning are going to be imperative to the forthcoming society. Hence, this is the right time to learn Machine Learning.
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How Does Machine Learning Work?
Machine learning operates with a variety of algorithms and techniques, which are formulated using specific programming languages designed for machine learning purposes. Typically, these algorithms undergo training using a dataset to construct a model. Later, when fresh input is supplied to the machine learning algorithm, it produces a result or prediction based on the established model. If the prediction is correct, it is considered dependable, and the algorithm is employed. However, in case of an inaccurate prediction, the algorithm goes through additional training with the dataset until it can yield precise outcomes.
For example, If you wish to predict the weather patterns in a particular area, you can feed the past weather trends and patterns to the model through the algorithm. This will be the training dataset for the algorithm. Now if the model understands perfectly, the result will be accurate.
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Different Types of Machine Learning
Machine Learning algorithms run on various programming languages and techniques. However, these algorithms are trained using various methods, out of which three main types of Machine learning are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
1. Supervised Learning
Supervised learning, often called supervised machine learning, is a branch of artificial intelligence and machine learning. It stands out by using labeled datasets to teach algorithms to sort data or make accurate predictions. The model fine-tunes its parameters as it receives input data, which is a part of the validation process. Supervised learning is valuable for organizations dealing with various real-world challenges, like segregating spam emails into a different folder from your regular inbox.
2. Unsupervised Learning
Unsupervised learning, sometimes referred to as unsupervised machine learning, employs machine learning algorithms to examine and group unmarked datasets. These algorithms uncover concealed trends or data groupings independently, without requiring human input. Its knack for uncovering likenesses and disparities in data makes it an excellent choice for tasks like exploring data, developing cross-selling tactics, segmenting customers, and recognizing images.
3. Reinforcement Learning
Reinforcement Learning (RL) is all about making decisions wisely. It’s the process of figuring out the best way to behave in a certain situation to get the most significant reward. This smart behavior is learned by engaging with the surroundings and seeing how it reacts, kind of like kids exploring the world and figuring out what actions get them closer to their goals.
Without someone guiding the way, the learner has to figure out on its own which sequence of actions brings the best rewards. This figuring-out part is a bit like trying things out and learning from the results. The quality of actions isn’t just based on the immediate rewards; it’s also about the rewards that come later. Since it can learn how to succeed in a new place without any help, reinforcement learning is a pretty impressive method.
Machine Learning Algorithms and Processes
Machine Learning algorithms are sets of instructions that the model follows to return an acceptable result or prediction. Basically, the algorithms analyze the data fed to them and establish a relationship between the variables and data points to return the result.
Over time, these algorithms learn to become more efficient and optimize the processes when new data is fed into the model. There are three main categories in which these algorithms are divided- Supervised Learning, Unsupervised Learning, and Reinforcement Learning. These have already been discussed in the above sections.
ML Programming Languages
Now, when it comes to the implementation of Machine Learning, it is important to have a knowledge of programming languages that a computer can understand. The most common programming languages used in Machine Learning are given below.
According to the GitHub 2021report, the below-given table ranks as the most popular programming language for Machine Learning in 2021:
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Popular Machine Learning Libraries
- Keras: Keras, a deep learning API created by Google, simplifies the process of implementing neural networks. It’s written in Python and facilitates the creation of neural networks. Additionally, it can work with various backend systems for neural network computations.
- PyTorch: PyTorch is an open-source Machine Learning library for Python, based on Torch, used for applications such as Natural Language Processing.
- TensorFlow: Created by the Google Brain team, TensorFlow is an open-source library for numerical computation and large-scale Machine Learning.
- Scikit-learn: Scikit-learn, also known as Sklearn, is a Python library that has become very popular for solving Science, Math, and Statistics problems–because of its easy-to-adopt nature and its wide range of applications in the field of Machine Learning.
- Shogun: Shogun can be used with Java, Python, R, Ruby, and MATLAB. It offers a wide range of efficient and unified Machine Learning methods.
- Spark MLlib: Spark MLlib is the Machine Learning library used in Apache Spark and Apache Hadoop. Although Java is the primary language for working in MLlib, Python users are also allowed to connect to MLlib through the NumPy library.
Also, check our blog on K-Nearest Neighbour ( KNN ) Algorithm in Machine Learning.
Difference between AI and Machine Learning
There seems to be a lack of a bright-line distinction between what Machine Learning is and what it is not. Moreover, everyone is using the labels ‘AI’ and ‘ML’ where they do not belong and that includes using the terms interchangeably.
Let’s explore the significant contrasts between Artificial Intelligence (AI) and Machine Learning (ML). AI encompasses various computer systems that aim to replicate human-like behavior, while ML is a subset of AI that focuses on learning from data. In this summary, we’ll delve into these important distinctions and how they find practical applications in the real world.
|AI stands for Artificial intelligence, where intelligence is defined as acquisition of knowledge intelligence is defined as an ability to acquire and apply knowledge.
|ML stands for Machine Learning which is defined as the acquisition of knowledge or skill
|The aim is to increase the chance of success and not accuracy.
|The aim is to increase accuracy, but it does not care about success
|It work as a computer program that does smart work
|Here, machine takes data and learn from data.
|The goal is to simulate natural intelligence to solve complex problems.
|The goal is to learn from data on certain tasks to maximize the performance on that task.
|AI is decision making.
|ML allows systems to learn new things from data.
|It is developing a system which mimics humans to solve problems.
|It involves creating self learning algorithms.
|AI will go for finding the optimal solution.
|ML will go for a solution whether it is optimal or not.
|AI leads to intelligence or wisdom.
|ML leads to knowledge.
|AI is a broader family consisting of ML and DL as its components.
|ML is a subset of AI.
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Applications of Machine Learning
As mentioned earlier, the human race has already stepped into the future world with machines. The pervasive growth of Machine Learning can be seen in almost every other field. Let me list out a few real-life applications of Machine Learning.
Fraud detection refers to the act of unethically drawing out money from people by deceiving them. Machine Learning can go a long way in decreasing instances of fraud detection and save many individuals and organizations from losing their money.
For example- by feeding an algorithm into the model, spam emails can be easily detected. Also, the right machine learning models can easily detect fraudulent transactions or suspicious online banking activities.
In fact, fraud detection ML algorithms are nowadays being considered as much more effective than humans.
Moley’s Robotic Kitchen
Machine Learning can do wonders in the food and beverage industry too. Consider this example- The kitchen comes up with a pair of robotic arms, an oven, a shelf for food and utensils, and a touch screen.
Moley’s kitchen is a gift of Machine Learning: it will learn n number of recipes for you, will cook with remarkable precision, and will also clean up by itself. It sounds great, doesn’t it?
Netflix Movie Recommendation
The algorithm that Netflix uses to recommend movies is nothing but Machine Learning. More than 80 percent of the shows and movies are discovered through the recommendation system.
To recommend movies, it goes through threads within the content rather than relying on the genre board in order to make predictions. According to Todd Yellin, VP of Product at Netflix, the Machine Learning algorithm is one of the pillars of Netflix.
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The latest innovations of Amazon have the brain and the voice of Alexa. Now, for those who are not aware of Alexa, it is the voice-controlled Amazon ‘personal assistant’ in Amazon Echo devices.
Alexa can play music, provide information, deliver news and sports scores, tell you the weather, control your smart home, and even allow prime members to order products that they’ve ordered before. Alexa is smart and gets updated through the Cloud and learns all the time, by itself.
But, does Alexa understand commands? How does it learn by itself? Everything is a gift of the Machine Learning algorithm.
Amazon Product Recommendation
We are sure that you might have noticed while buying something online from Amazon, it recommends a set of items that are bought together or items that are often bought together, along with your ordered item.
Have you ever wondered how Amazon makes those recommendations? Well again, Amazon uses the Machine Learning algorithm to do so.
How does Google Maps predict traffic on a particular route? How does it tell you the estimated time for a certain trip?
Google Maps anonymously sends real-time data from the Google Maps users on the same route back to Google. Google uses the Machine Learning algorithm on this data to accurately predict the traffic on that route.
These are some of the Machine Learning examples that we see or use in our daily lives. Let us go ahead and discuss how we can implement a Machine Learning algorithm.
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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.
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.
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.
Check out our blog to learn more about the bias variance tradeoff in machine learning!
Disadvantages of Machine Learning
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.
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 Scope of Machine Learning
The scope of Machine Learning covers varied industries and sectors. It is expanding across all fields such as Banking and Finance, Information Technology, Media & Entertainment, Gaming, and the Automotive industry. As the Machine Learning scope is very high, there are some areas where researchers are working toward revolutionizing the world for the future.
The scope of Machine Learning in India, as well as in other parts of the world, is high in comparison to other career fields when it comes to job opportunities.
According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2023. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles. According to Forbes, the average salary of an ML Engineer in the United States is US$99,007.
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Prerequisites for Machine Learning
For those interested in learning what is Machine Learning, here are some important things to learn before starting a career in this field.
- Statistics– Knowledge of statistical tools and techniques is a basic requirement to understand Machine Learning. You should be well trained in using various types of statistics such as descriptive statistics and inferential statistics to extract useful information from raw data.
- Probability– Machine Learning is built on probability. The very possibility of the occurrence of an event is known as probability.
- Programming languages– It is very important that an ML engineer knows which machine-readable programming language to be used.
- Calculus– The working of Machine Learning algorithms depends on how Calculus and related concepts such as Integration and Differentiation are used. Hence, it is very important that you understand and are well acquainted with Calculus.
- Linear Algebra– Vectors, Matrices, and Linear Transformations form an important part of Linear Algebra and play an important role in dataset operations.
This module focuses on what is Machine Learning, common Machine Learning definitions, the difference between AI and Machine Learning, why Machine Learning matters, prerequisites, and types of machine learning. We have also highlighted different Machine Learning tools, as well as discussed some of the applications of Machine Learning. If you want to have a deeper understanding of Machine Learning, refer to the Machine Learning tutorial. See you there!
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