Machine learning is omnipresent in the industry these days. Companies worldwide are scrambling to integrate machine learning into their functions, and new opportunities for aspiring data scientists are growing multi-fold.
What is Machine Learning?
Machine learning helps systems learn and improve from experiences fed by users without having to be programmed for it explicitly.
For example, a classification algorithm puts data into different groups. The same algorithm can be used to classify handwritten numbers and to classify spam for non-spam emails without changing a line of code. The only variation will be the change of training data that makes a different classification logic.
We can classify ML (Machine Learning) into two types:
1. Supervised Learning: In Supervised Learning, we have an input variable (x) and an output variable (Y), and we use an algorithm to learn the mapping function from the input to the output. The algorithms try to form a function, Y = f(X). The mapping function is approximated in such a way that when new data is received, it predicts a correct output.
It can further be classified into two types: Regression and Classification.
In a Regression problem, the output is a real value. Ex: Predicting house prices.
In a classification problem, the output is a variable from a category. Ex: Classifying an image
2. Unsupervised Learning Unsupervised learning is where we only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the information. It can be classified into two types: Clustering and Association.
In clustering, data that have similar properties are grouped together. Ex: k-means clustering.
In association, we want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
Before we get an output from the ML algorithm, there are multiple steps that are taken. From collecting good data to hyperparameter tuning, every step contributes towards a good ML algorithm. The steps taken when approaching an ML problem are:
Data Collection: The quantity & quality of your data dictate how accurate your model is.
Data Preparation: Various things like cleaning the data, normalization, splitting in training, and test set are done in this step.
Choosing a Model: There are multiple ML algorithms. We chose the one which best fits our purpose.
Training the Model: The data is used to train the model, which helps the algorithm in making predictions.
Evaluating the Model: We use different metrics to check how well the algorithm is working.
Parameter Tuning: We make certain changes in the algorithms to make the model a little better performance-wise.
Making Predictions: We check the model on the test set to check how well the model will perform in real-world problems.
In the part 2 of this blog, we will be discussing more on how to get started with ML, and we'll be explaining some technologies used in Machine Learning today.
Machine Learning - what and what's next, 30th September 2017 Link:(https://byte7.github.io/blog/Machine-Learning-what-and-what's-next/ )
Adam Geitgey, Machine Learning is Fun!, 5th May 2014 Link: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
Jason Browniee, Supervised and Unsupervised Machine Learning Algorithms, 20th August, 2020 Link: (https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/)
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