All You Need To Know About Machine Learning

Speakers that listen! Cars that drive! If you are reading this blog, it’s probably because you are constantly coming across futuristic stuff like this and realizing how Artificial Intelligence is revolutionizing the world. You might also, undoubtedly, have heard of machine learning, the technology powering the AI revolution. Of course, technologists are going crazy nowadays, implementing AI in everything possible. From online shopping recommendations, that makes you go wow, to self-driving cars, machine learning is changing the perspective of everything around us at warp speed.

With the future flying by at such pace, the onus is on us to keep tabs on latest technology and the stuff powering it. That in mind, here is a handy guide on Machine Learning that includes everything you need to know. So put your learning cap on!

What is machine learning? Why is it so important today?

Machine learning is a subset of AI that focuses on machines learning on their own instead of being completely dependent on code.

Machine learning uses models running on neural networks (more on that later). These models can be trained to learn from data instead of being programmed for tasks like recognizing text, images and video. Learning is an important aspect of human behavior. So, if an AI has to impart human like intelligence, we need to make it learn, either from past data or from its mistakes. That is why, huge volumes of data are fed to these models whose algorithms teach them to operate on data.

Example: for an AI to learn translation of Russian to English, large sample data sets are uploaded until the model’s probability of accuracy in translating a sentence is high enough.

This technique of making machines learn from past experiences is different from the classical approach to AI, where programmers code step by step instructions. Machine learning combines the classical with a knowledge to learn and grow, which makes it the most preferred subset of AI.

Machine Learning in action:

Let us look at a real world scenario where Machine Learning is utilized for a simple but very useful application. The spam filter in your email inbox that does the seemingly simple task of filtering out junk or spurious emails.

Who would have imagined that Machine Learning is important in what looks like a tool with a few lines of code and rules that together judge if an email is worth your while. Anything with lots of images or specific keywords is a spam, done. Well, the problem is, these rules are clearly subjective. They can easily be predicted and manipulated by spammers.

Machine Learning enables the tool to adapt to a user based on his/her requirements. It takes note of the user’s response to flagged emails (either reading or deleting them) and helps train the AI agent to better deal with this kind of emails in the future. The more information the algorithm gathers, the more improved is the performance.

Machine Learning learns from its mistakes:

Neural networks are trained to recognize inputs. When it misinterprets an input, it means that the neural networks processing elements or variables have improper values. All the incorrect processing element conditions are taken and converted as an error function, which is then used to correct the algorithm.

Machine learning learns from its mistakes

Neural networks are trained to recognize inputs. When it misinterprets an input, it means that the neural networks processing elements or variables have improper values. All the incorrect processing element conditions are taken and converted as an error function, which is then used to correct the algorithm.

Is machine learning similar to data mining?

The main reason for an ambiguity in this distinction is the fact that there is a crossover between the two fields.

“Data mining is finding a list of dance centers in Portland; machine learning is learning how to dance,” is how experts popularly explain them.

Data mining is the art of mining deep into a data set to extract or find more information. Whereas Machine Learning is only about using this data. It absorbs the data, learns from it and uses it to perform tasks or predictions.

Deep learning:

“A neural network is a system of programs and data patterned on the operation of the human brain that learns from and adapts to initial rules and experience.”

Just to make stuff complicated to the layman, deep learning involves layers of neural networks, usually compared to the design of the brain. The layers of neural networks are essentially multiple layers of processing elements. The learning process can be either supervised (using categorized data) or unsupervised (without human categorization and labeling).  

deep learning

Types of Machine Learning:

It is usually identified by the way neural networks learn. There are four main methods: 

Languages used in Machine Learning:

Owing to its famous simplicity, and the extensive availability of deep learning libraries such as TensorFlow and PyTorch, Python is now the most popular language.

Java is also a good option and comes with a great community of its own. C++ and R are also worth giving a shot if you are into C.

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