How can you learn machine learning? ( Easy Guide )

How can you learn machine learning?

learn machine learning

Humans learn from their past experiences and machines follow instructions given by humans but what if humans can turn the machines to learn from the past data and to what humans can do act much faster that’s called machine learning. it’s a lot more than just learning it’s also about understanding and reasoning. Machine learning is an ever-growing field of importance in today’s world and it has firmly secured its place in our future as well.

The term “Machine Learning” was coined by Arthur Samuel in 1959 and defined as a “Field of study that gives computers the capability to learn without being explicitly programmed”.

In modern times, Machine Learning is the most popular career option. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% high and an average base salary of $146,085 a year.

What is Machine Learning?

Machine Learning enables machines to learn a task from experiences without programming which involves artificial intelligence. This begins by providing them with good quality data and then teaching the machines by building various machine learning models using various data and algorithms. The algorithms depend on what type of data we contain and what kind of work that we are trying to automate.

How to start learning Machine Learning?

This is a not smooth roadmap you can follow on your way to becoming a wild talented Machine Learning Engineer. Of course, you can change the steps according to your needs to reach your desired end goal!

Step 1 – Understand the Prerequisites

In case you are good enough, you could begin ML directly but normally, there are some prerequisites t are important to know which include Linear Algebra, Multivariate Calculus, Statistics, and Python. And if you don’t know these, not a big deal, You don’t need a Ph.D. degree in these topics to get started but you do need a piece of basic knowledge.

(a) Learn Linear Algebra and Multivariate Calculus

In Machine LearningLinear Algebra and Multivariate Calculus are the two important things. However, the extent to which you need them depends on your role as a data scientist. If you are more interested in application-heavy machine learning, you will not be that heavily focused on maths as there are many common libraries available. But if you want to focus on R&D in Machine Learning, then mastery of Linear Algebra and Multivariate Calculus is very important as you will have to implement many ML algorithms from scratch.

(b) Learn Statistics

Some of the important concepts in statistics that are important are Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, etc. Also, Bayesian Thinking is also a very important part of ML which deals with various concepts like Conditional Probability, Priors, and Posteriors, Maximum Likelihood, etc.

(c) Learn Python

Some people like to skip Linear Algebra, Multivariate Calculus, and Statistics and learn them as they go along with trial and error. But we cannot skip is Python! While there are other languages you can use for Machine Learning like R, Scala, etc. Python is currently the most popular language for ML.

Step 2 – Learn Various ML Concepts

It’s always good to start with the basics and then move on to the more rough stuff. Some of the basic concepts in ML are:

a) Terminologies of Machine Learning

● Model. A model is also called a hypothesis.

● Feature – A feature is an individual countable property of the data. Target – A target variable or label is the value to be estimated by our model.

● Training – The idea is to give a set of inputs(features) and it’s expected outputs(labels)

● Prediction – Once our model is ready, it can be fed a set of inputs to which it will provide a predicted output(label).

(b) Types of Machine Learning Supervised Learning

It involves learning from a training dataset with labeled data using classification and regression models. Unsupervised. Learning – involves using unlabelled data and then finding the underlying structure in the data.

● Semi-supervised Learning – involves using unlabelled data like Unsupervised Learning with a small amount of labeled data

● Reinforcement Learning – This involves learning optimal actions through trial and error.

(c) How to Practise Machine Learning?

● The most time-taking part of ML is data collection, integration, cleaning, and preprocessing. So make sure to practice with this because you need high-quality data

● Learn various models and practice on real datasets. This will help you in creating your intuition around which types of models are appropriate in different situations.

● Along with these steps, it is equally important to understand how to interpret the results obtained by using different models.

(d) Resources for Learning Machine Learning:

There are many online and offline resources (both free and paid!) that can be used to learn Machine Learning. Some of these are provided here:

● For a broad introduction to Machine Learning, Stanford’s Machine. Learning Course by Andrew Ng is popular

● .If you want a self-learning guide to Machine Learning, then Machine. Learning Crash Course from Google● In case you like an offline course, the Geeksforgeeks Machine Learning. A Foundation course will be ideal for you.

Step 3 – Take part in Competitions

After you have understood the basics of Machine Learning, you can move on to Competitions! These will basically give practical knowledge make you even more proficient in ML that you can start with on Kaggle that will help you build confidence are given here:

● Titanic: Machine Learning from Disaster: The Titanic: Machine Learning from Disaster, challenge is a very famous beginner project

● Digit Recognizer: The Digit Recognizer is a project after you have some knowledge of Python and ML basics

Things have to be familiar with:

● The first thing is math.

● The next step takes a basic machine learning course that will teach you basic algorithms like linear regression.

● The third step involves learning python which is very necessary

● The next step is data preparation, which is fully automatic. doing data preparation gives higher accuracy.

● You have to familiarise yourself with the deep learning library Steps to learn

● Learn python, data science tools, and machine learning concepts.

● Learn data manipulation, analysis, and visualization with panda, NumPy

● Learn machine learning with scikit-learn data

● Learn deep learning and neural networks Andrew Ng’s deep learning specialization on Coursera.

Machine learning is a core sub-area of artificial intelligence; it enables computers to get into the mode of self-learning without being explicitly programmed. when exposed to new data, these programs are enabled to learn, grow, change, and develop by themselves.

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Thank you for reading!

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