Machine Learning Simplified!

Did your mind just pop images from the famous Terminator movie where machines are destroying the entire world with all the news you hear around the words “Artificial Intelligence” and “Machine Learning”? Who knows..there also might be a hulk generated anywhere now and a Thor hanging around to save our lives…But wait right there. Hold down your horses there because this isn’t a fictional movie but sadly reality! Artificial Intelligence and Machine Learning as opposed to what you think currently are NOT anywhere related to such scenes in our near future.

Machine Learning and AI are here to stay only for the best of humankind. To know how exactly that might be true, let’s clarify your ideas about Machine Learning here…

This blog is the result of many of my friends from the non-Computer Science background wondering and asking me what is Machine Learning. As the buzz around Machine Learning keeps escalating day by day, everyone wants to know what Machine Learning exactly is.

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I hope by the end of this blog this now-alien-looking term would turn into an interesting field and intrigue you. So let’s get started!

Whats and Hows of Machine Learning…

Examples of Machine Learning in real life today are- email auto filtering of spam and non-spam ones, online recommendations you get for online shopping, prediction of stock prices and much more.

Remember how we always ask for examples to understand a concept clearly? That pretty much is what Machine Learning is all about. Yes. It simply means understanding and learning by the means of different examples. For example- to make a machine know what is the difference between a cat and a dog, we provide it with various images of cats and dogs stating the correct answers and then testing the same after it was trained.

That is the gist of what essentially happens in a Machine Learning algorithm.

SO HOW IS MACHINE LEARNING DIFFERENT FROM OTHER TRADITIONAL ALGORITHMS?

To answer, I would say just in the same way we distinguish theoretical direct learning and the indirect way of learning through examples. Machines figure out on their own, the rules and patterns in data.

ML(Machine Learning) is a sub field of Artificial Intelligence and Data Science.

So after having a clear idea of what ML is all about conceptually, let’s get started with basics and types followed by implementation of our first ML program.

TYPES OF MACHINE LEARNING:

Types of learning(Credits-https://www.slideshare.net/meydman/machine-learning-ai)

The image above gives a clear idea of types of machine learning categorised as:

1.Supervised Learning- In simple terms this type is similar to the way a teacher teaches his students. For eg. if we want to learn what an apple is, teacher simply supervised us with a label called “apple”. In similar fashion, we provide a label for certain inputs to get the desired output.

2.Unsupervised Learning- Opposite of Supervised Learning, the algorithms themselves cluster output into various groups based on some input features. No labels are provided exclusively.

3.Reinforcement Learning-In this type we do not provide labels as seen in unsupervised but the difference is we provide positive or negative feedback to the output similar to supervised type. The feedback can be termed as hints for algorithm to generally understand what is the correct output required for a particular type of input.

For more details on types and their respective models, please visit official documentation here:

An introduction to machine learning with scikit-learn - scikit-learn 0.19.1 documentation

IMPLEMENTATION PROCESS:

Any machine learning model implementation is divided into following steps:

1. Importing libraries
2. Loading dataset
3. Splitting data into independent and dependent variables
4. Splitting data into training and testing datasets
5. Selecting the model type to use
6. Fitting our model
7. Predicting the values
8. Plotting the graph for our model

5th step is generally variable for choosing the model to use. Choosing a model depends on various factors which we can discuss in the next implementation post. We are implementing the relation of temperature and relative humidity in the month of June 2018 for Mumbai.

We will use Python language for implementation so knowledge of Python language, basic linear algebra is a must for implementation now.

FIRST ML MODEL:

ditsme/MachineLearning

Above is the link to the entire code for the first model that we are going to implement here.

We implement Simple Regression model here. Simple Regression Model algorithm comes under supervised learning type and is one of the easiest of all.

Check out my next blog here to implement this first model with detailed description of each step explained.

Please let me know your views about this blog in comments and do share if you find it worth-reading. Thankyou :)

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