Machine Learning is getting computers to learn like humans and improve their learning over time with autonomous fashion by feeding them information and data in the form of observations. Machine Learning is based on algorithms which can learn from data without being explicitly programmed.
When it comes to Machine Learning or any Programming field there are lot of options and programming languages to choose and one can get lost and confused for choosing the right option so this article on Complete machine learning roadmap 2021 it will valid in the future also will clear all your doubts and confusion and you will have :
- A clear Machine Learing roadmap
- A clear way to start your Machine Learning journey
- And write code and create your ML algorithms
Step By Step By Machine Learning Roadmap
I will show you step by step machine learning roadmap this is a complete guide on machine learning so read till the end. If you want to get started in python than read this guide – How to start programming in python I have shown here exact way how to start programming in python.
Step 1 : Pick A Programming Language
The first step in any programming field is to know a programming language and if you don’t know any programming language and this is your first time don’t worry we will tell you the best programming language to learn.
If you are a beginner and don’t know which language to start Python is the best language to for machine learning and other things also.
It can also be used in web development, Artificial intelligence and data analysis. Python is also used in Machine Learning it has many useful libraries like pandas and numpy. at beginner level don’t learn python deeply learn some basics and move to next step.
Step 2 : Learn Sklearn And Tensorflow
The second step is to learn Sklearn and Tensorflow which are popular libraries used for Machine Learning and Artificial Intelligence.
Sklearn – Scikit-learn (Sklearn) is a general purpose free Machine Learning library. It is the most useful library for Machine Learning in python. It has many efficient tools and statiscal modeling including classification, clustering, dimensionality reduction and regression. Sklearn should be used for building Machine Learning models not to read the data, manipulate and summarize it. There are better libraries that we will talk later.
Tensorflow – It is used as a deep learning library created by google. Earlier the coding mechanism for Machine Learning and Deep Learning was complicated. But Tensorflow made it easier because it provides a high level API and complex coding isn’t required.
If you want to learn more about tensorflow than read this docs – Tensorflow docs
You Learn Sklearn and Tensorflow and you will be set to create your own basic Machine Learning Models.
Step 3 : Learn Linear Algebra
If you have heard that you require maths for Machine Learning than it is true. Linear Alebra is a maths topic to learn in Machine Learning if you want to master and become expert of Machine Learning. If you know linear algebra from 11th and 12th you can brush up your knowledge and if you are not from a maths background you can learn it from youtube for free.
Linear Algebra is required because if you want to tune your ML models with maximum flexibility you need to know how they work and knowing linear algebra is must for it. You can start learning linear algebra while doing step 1 and 2 this will help you speed up your journey to become Machine Learning expert.
Step 4 : Learn Statistics
Another topic of maths you need to know in Machine Learning which is statistics which is little boring but not that hard to study so you have to have the basic understanding of probability and statistics is very important when it comes to Machine Learning.
You can also these both Maths topic Linear Algebra and Statistics while doing step one and two. If you are not from maths background you can learn it for free from youtube.
Step 5 : Learn Core Machine Learning Algorithms
After learning Python and Sklearn you should start learning how these Machine Learning Alogrithms work these algorithms are created by Sklearn developers so if you want to become a expert in Machine Learning you need to learn the core Machine Learning Algorithms.
To know how these Machine Learning algorithms work and created you can take a look into :
– Gradient Descent
– Supervised Learning
– Unsupervised Learning
– Basic Linear Regression
– Reinforcement Learning
Step 6 : Learn Python Libraries
Step 6 is to learn these python libraries Numpy and pandas which are very useful in machine learning and are essential to learn these libraries.
Pandas – It is used for data cleaning and analysis. It has many features which are used for exploring, cleaning, transforming and visualizing the data. It is a free open source library. It can be installed through PIP in your project.
Numpy – It is a library for python programming it provides large, multi-dimensional arrays, matrices and a large collection of high-level mathematical functions to operate on these arrays. It has a great use in machine learning so it is important to learn these.
Step 7 : Deployment
Once you have learn’t everything from step one to Six then you can start working on creating Machine Learning projects.
After creating them you need to deploy your projects so that people can use it. So you need to host your Machine Learning model on a powerful backend so to create that you have to learn python frameworks like Django or flask.
If you don’t want to learn these you can hire someone or use third party platform.
So if you have read this from the end you must have gained a lot of knowledge about Machine Learning. So in simple words you have to :
– learn Python programming language
– Learn Sklearn and Tensorflow
– Learn Linear Algebra
– Learn Statistics
– Learn Core Machine Learning Models
– Learn Python Libraries
– Learn Deployment
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