2021 Roadmap to Machine Learning
Let’s dive in and see what is the road map to learn machine learning in this article. In 2021 use of Machine learning will increase more because of these pandemic situations everything is becoming virtual.
I think every data science enthusiast must have come across this kind of question or you just want to know the answer to these questions. The questions are ‘How to learn machine learning and what are the prerequisites to Machine learning and what is the need to learn it?’. There might be too many results for your search which may confuse you from where to start. Don’t worry I am here to help you out and show you guys an easy path to follow.
Data Science is the most happening technology in the entire world. Nowadays in every corner of the world, we can see the applications of Data Science. But Data Science is vast to learn at once so we have to go step by step.
Why roadmap is important?
Before learning anything, we should have an idea that how to start it and what are the skills it requires and a perfect source that explains you clearly. The roadmap here is quite similar to the real-life road map which leads us to the destination. The road map is something that helps our learning journey easy and simple.
Let’s see the flow diagram which describes the road map of Machine learning in 2021.
Pre-requisite to Machine Learning
Before learning Machine learning we should have an idea about a few topics like maths, programming, etc.
- Mathematics Skills – Mathematics is very important in developing any part of technology. You can refer to this article to know what is the maths behind machine learning Mathematics for ML. The few most important topics to learn in mathematics are Algebra, Calculus, Probability, and Statistics.
We have to learn Mathematics because to understand the mechanism behind the algorithms we use in Machine learning. These topics help us realize how these libraries work and all.
- Programming Skills: To develop a model or to create a technology programming language is a must. Because programming is crucial when it comes to create solutions to global problems and to speed up them. It helps us to write the most efficient code and solves many real-life problems.
Python is very simple and easily understandable. And it is very easy to learn, which makes it easy for developers to build machine learning models. Python is a platform-independent language which means we can implement our program in one machine and use them in another machine. And Linux and macOS also support python language. Let’s see some collection of python libraries which we use frequently.
- Scikit-Learn, Kera’s, TensorFlow for Machine learning
- NumPy for data analysis and high-performance computing
- SciPy for advanced computing
- Pandas for general data analysis
I highly recommend you to learn python and the next best-fit language will be ‘R’. To implement in python, you should use the software ‘Anaconda’ to write our machine learning programs. Anaconda includes all inbuilt libraries we use. In 2021 many students will be able to learn python because every college is trying to embed it in the curriculum.
- Data Engineering Skills: We use Data engineering skills to organize our data. It transforms our data set into a useful format. Raw data consists of a lot of noise which leads to more error percentages. So, to increase the accuracy, we have to remove the noise present within the data. If our data is in the form of images then the background may be noise. There will be umpteen features in a dataset but we might not use all those so, here we have to use feature reduction techniques. You can refer to this article for feature reduction technique Principal component analysis.
To become an expert in Machine learning the first thing we have to know is what is ML? Machine learning is a subset of Artificial intelligence. It studies algorithms that improve automatically through experience.
- Machine Learning Algorithms: Algorithm is a technique we use to predict the results by training a set of data. In Machine learning, there are numerous algorithms that we use frequently based on which type of output we need. There are three types of algorithms in ML are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. This article helps you to understand these algorithms in clear Machine learning models.
- Machine Learning Frameworks: A Machine learning framework is an interface that enables the developers to build the models much faster and easier. It simplifies the machine learning algorithms by building the framework which trains itself on datasets and makes them fastest while classification, prediction, etc. The famous frameworks we use in machine learning are:
- TensorFlow and PyTorch: These both frameworks provide a set of algebraic tools and can run regression analysis perfectly.
- Scikit Learn: This framework might be most popular among R programmers but we will use this frequently in python also. But we cannot run clustering algorithms using this.
- Spark ML: This framework is designed to run clustering algorithms.
How to use Machine Learning
Let’s see a generalized pseudo algorithm which is quite similar for all machine learning models.
- Gather the data (To get the data you can use the Kaggle website).
- Remove the noise and clean the data. Then extract the features that we use in our prediction.
- Choosing an algorithm that best suits our model and data
- Training the data with the help of the algorithm.
- Predicting the output for required inputs.
So, to learn Machine learning these are the few steps we should follow, and following this path will make your journey much easier and understandable.
Thanks for reading!
Happy New Year! Have a great year ahead.