Machine learning is ubiquitous in the industry these days. Organizations around the world are scrambling to integrate machine learning into their functions and new opportunities for aspiring data scientists are growing multifold.
But we have noticed a huge gap between what the industry needs and what’s on offer right now. Quite a large number of people are not clear about what machine learning is, machine learning and its types, and how machine learning works.
By the end of this page, you will understand not only machine learning but also its different types, its ever-growing list of applications, the latest machine learning developments, and the top experts in machine learning, among various other things.
This is your one-stop destination for understanding what is machine learning and machine learning analytics!
Jumping straight at the introduction to machine learning, machine Learning definition is the science of teaching machines how to learn by themselves. Now, you might be thinking – why on earth would we want machines to learn by themselves? Well – it has a lot of benefits when it comes to machine learning for analytics and machine learning applications.
Machines can do high-frequency repetitive tasks with high accuracy without getting bored.
To understand how machine learning works, let’s take an example – the task of mopping and cleaning the floor. When a human does the task – the quality of outcome would vary. We get exhausted/bored after a few hours of work and the chances of getting sick also impact the outcome.
Depending on the place – it could also be hazardous or risky for a human.
On the other hand, if we can teach machines to detect whether the floor needs cleaning and mopping and how much cleaning is required based on the condition of the floor and the type of the floor – machines would perform the same job far better. They can go on to do that job without getting tired or sick!
This is what Machine Learning aims to do – enable machines to learn on their own. In order to answer the questions like:
Machines need a way to think and this is precisely where machine learning models help. The machines capture data from the environment and feed it to the machine learning model. The model then uses this data to predict things like:
This video snippet from our popular course AI & ML for Business Leaders will help you understand how a machine learning model works at a high level:
Sadly, things which are usually intuitive to humans can be very difficult for machines. You only need to demonstrate cleaning and mopping to a human a few times – before they can perform it on their own.
But, that is not the case with machines. We need to collect a lot of data along with the desired outcomes in order to teach machines to perform specific tasks. This is where machine learning comes into play.
To breakdown how machine learning works, machine Learning would help the machine understand the kind of cleaning, the intensity of cleaning, and duration of cleaning based on the conditions and nature of the floor.
Now that you get the hang of it, you might be asking what are some of the examples of machine learning applications and how does it affect our life. Unless you have been living under a rock – your life is already heavily impacted by machine learning.
Let us look at a few examples where we use the outcome of machine learning already:
The machine learning applications are immense. You can read this article for a comprehensive list of machine learning applications driven by machine learning, which we use in our day-to-day life:
Sounds exciting! But this idea of teaching machines has been around for a while. Remember Asimov’s Three Laws of robotics? Machine Learning ideas and research have been around for decades. However, there has been a lot of action and buzz recently.
The obvious question is why is this happening now when machine learning has been around for several decades?
This development is driven by a few underlying forces:
These 4 forces combine to create a world where we are not only creating more data, but we can store it cheaply and run huge computations on it. This was was not possible before, even though machine learning techniques and algorithms were well known.
If you are thinking that machine learning is nothing but a new name of automation – you would be wrong.
Most of the automation which has happened in the last few decades has been rule-driven automation. For example – automating flows in our mailbox needs us to define the rules. These rules act in the same manner every time. On the other hand, machine learning helps machines learn by past data and change their decisions/performance accordingly.
Spam detection in our mailboxes is driven by machine learning. Hence, it continues to evolve with time.
The only relation between the two things is that machine learning enables better automation.
There are several tools and languages being used in machine learning. The exact choice of the tool depends on your need and scale of operations. But, here are the most commonly used tools in machine learning:
Check out the below articles expounding on a few of these popular tools (these are great for making your ultimate choice!):
Ah! So you have heard about Deep Learning!
Deep learning is actually a sub-field of Machine Learning. So, if you were to represent Machine Learning and Deep Learning by a simple Venn-diagram – it will look like this:
You can read this article for a detailed deep-dive into the differences between deep learning and machine learning:
If you are thinking that machine learning and statistical thinking are the same – again you are wrong! Read this article to understand the differences between Machine Learning and Statistical Learning:
Machine Learning problems can be divided into 3 broad classes:
For a high-level understanding of these algorithms, you can watch this video:
For knowing more about these popular algorithms along with their codes – you can look at this article:
There is no simple answer to this question. It depends on the problem you are trying to solve, the cost of collecting incremental data and the benefits coming from incremental data. To simplify data understanding in machine learning, here are some guidelines:
Everything which you see, hear and do is data. All you need is to capture that in the right manner.
Data is omnipresent these days. From logs on websites and smartphones to health devices – we are in a constant process of creating data. In fact, 90% of the data in this Universe has been created in the last 18 months.
Data can broadly be classified into two types:
Machine Learning models can work on both Structured as well as Unstructured Data. However, you need to convert unstructured data to structured data first.
Any machine learning model development can broadly be divided into six steps:
Some of the latest achievements of machine learning include:
We actually wrote a comprehensive article on the major AI and machine learning breakthroughs in the past year which everyone should go through:
At the current level of technological advancements, machines are only good at doing specific tasks. A machine that has been “taught” cleaning can only do cleaning (for now). In fact, if there is a surface of new material or form which the machine has not been trained on – the machine will not be able to work on it in the same manner.
This is usually not the case with humans. So, if a person is responsible for cleaning and mopping, he/she can also be a security guard. He/she can also help in planning logistics.
This phase of artificial intelligence is typically referred to as “Artificial Narrow Intelligence“.
While machine learning has made tremendous progress in the last few years, there are some big challenges that still need to be solved. It is an area of active research and I expect a lot of effort to solve these problems in the coming time.
You heard me there!
No – it is not. There are methods or algorithms within machine learning which can be interpreted well. These methods can help us understand what are the significant relationships and why has the machine taken a particular decision.
On the other hand, there are certain algorithms that are difficult to interpret. With these methods, even if we achieve a very high accuracy, we may struggle with explanations.
The good thing is that depending on the application or the problem we are trying to solve – we can choose the right method. This is also a very active field of research and development.
Now you are asking the perfect questions! Given the shortage of talent in this domain, it definitely makes sense to look at building a career in data science and machine learning for analytics. But before you decide, you should keep the following things in mind:
If you are ready to build a career in data science after reading the tips above – we have a plan for you. You can check out the FREE learning path to become a data scientist by Analytics Vidhya. If you need guidance and mentorship – check out our AI & ML Blackbelt program.
In addition, you can do the following:
Check out our awesome course “Ace Data Science Interviews” for a detailed and Structured preparation module. Here is a comprehensive guide you might want to look at as well:
As a beginner with no background in machine learning, you can read the following books (the links are affiliate links):
On the other hand, if you already have the required background and want to learn machine learning – these are the books you should read:
As mentioned multiple times – Machine Learning is a very active field of research. From Andrew Ng to Peter Norvig, the contributions of top experts and researchers cannot be spoken about enough.
You can read this article to get a list of top researchers in the field of machine learning:
You’ve chosen the right career at exactly the right time. Happy learning!
