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As a programmer, you understand well how a program works: it runs based on certain commands and statements written by you. However, some smart people asked whether it would be possible for a program to learn things depending upon past experiences and improve its decision-making ability to enhance its overall performance.

This is the most fundamental and simplified version of the idea of Machine Learning.

## What is Machine Learning?

The term “Machine Learning” was coined by American Pioneer, Arthur Samuel. Arthur defined Machine Learning as “the field of study that gives computers the ability to learn without being programmed explicitly”.

In simple terms, Machine Learning is the science of getting things done using intelligent machines. It is a subset of Artificial Intelligence. It teaches a computer system to make precise predictions when some data is given as input. A Machine Learning model can make predictions by answering questions like whether a piece of fruit in a picture is an orange or mango, whether an email you received is spam or not, or recognizing speeches in a YouTube video for generating captions.

A Machine Learning algorithm is fed with data and information in the form of observations and real-world interactions. It studies the data available and improves its learning over time in its own way until the algorithm can make decisions and predictions.

The applications of Machine Learning are widely used in several sectors ranging from science, telecom, healthcare, production, and so on.

## How to learn and grow in Machine Learning?

If you want to become an expert in Machine Learning, you need to follow several steps which require you to invest a significant amount of time to learn about the principles behind it and acquire a firm grasp on it. The steps to learn Machine Learning in the most efficient way is described below.

### Understand the basics

Machine Learning is a deep domain technology and before you get started with ML, you need to spend a couple of weeks grasping the “general and basic knowledge” about the field of Machine Learning.

In the beginning phase, you should become well aware of the detailed and correct answers to the following questions:

• What is Machine Learning?
• What is the capability of Machine Learning?
• What are the merits of learning Machine Learning?
• What are the limitations of Machine Learning?
• What are the applications of Machine Learning?

After you have gathered the fundamentals, you can head on to the other related domains which are often associated with Machine Learning: Analytics, Data Science, Big Data, and Artificial Intelligence.

If you want to become an expert, you need to interpret the finer details of all the topics mentioned earlier. Try to understand the concepts in your own specific manner so that you can explain it in a simple way to just about anyone.

Recommended exercise

Write a blog about “The Basics of Machine Learning” on any blogging website. Your article must answer questions about Machine Learning considering that it is asked in an interview.

## Learn Statistics

Data plays a very important role in the field of Machine Learning. In your Machine Learning career, you will have to spend most of your time working with data. This is where statistics comes in picture.

Statistics is a field of mathematics that deals with the collection and analysis of data and also explains how you can present your data efficiently. It is a prerequisite for understanding Machine Learning deeply.

Though it is said that you can achieve to be a Machine Learning expert without any such expertise in statistics, it is also considered that you cannot completely avoid statistical concepts when the question is about Machine Learning and Data Science.

The concepts you need to learn in the domain of statistics are –

• Significance of Statistics
• Data Structures and Variables
• Basic principles of Probability
• Probability Distributions
• Hypothesis Testing
• Regression model

You can also gather information about the Bayesian model and its various concepts which tend to be an essential part of Machine Learning.

Recommended exercise
As an exercise in Statistics, you can create a list of references for each topic mentioned above which will explain them in the easiest manner and then put it out in a blog.

## Learn Python or R

If you want to become a master in any programming language, it could well take an eternity. However, in your quest of becoming a Machine Learning expert, you need to get familiar with learning a language. Experts say this it is not too difficult.

There are numerous languages like Java, C, C++, Scala, Python, R, etc. by which you can implement your Machine Learning algorithms. However, Python and R are the most popular languages, and learning one can certainly make it easy to learn the other.

Most of the experts prefer Python since it is easier to build Machine Learning models in this language than any other programming language. While Python is best for writing code related to Machine Learning, but when it comes to managing a huge amount of data for a Machine Learning project, experts suggest R.

Python also offers certain libraries that are specifically built for Machine Learning like Keras, TensorFlow, Scikit-learn, etc. Thus, it can be said learning both Python and R can be an upper hand in your journey of becoming a Machine Learning expert.

## Learn Machine Learning concepts and algorithms

Now that we have covered the prerequisites, let us reach out to the heart of Machine Learning. Algorithms are an important part in the world of programming. You need to learn about all the algorithms particularly designed for Machine Learning and the applications of these algorithms in your projects.

Machine Learning is a wide field of study and algorithms act as the bread and butter in your journey of learning it. Along with Machine Learning algorithms, you should also know about the types and building blocks of Machine Learning:

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning
• Data Preprocessing
• Ensemble Learning
• Model Evaluation
• Sampling & Splitting

Learn about all the concepts in detail such as what do they mean and why they are used in Machine Learning.

## Create Learning models

The most fundamental idea of any Machine Learning model is that the model is given a large amount of data as input and the corresponding output is also supplied to them. Here, we will take into consideration the two common Machine Learning models - Unsupervised learning model and Supervised learning model.

Unsupervised learning is a Machine Learning technique where the model works on its own to discover information. It uses unlabeled data and then finds the internal pattern in the data to learn more and more about the data itself. It can be used in a situation where you are given data about different countries as input and you need to find out the countries similar to each other based on a particular factor like population or health.

Some of the concepts you need to learn about Unsupervised learning algorithms are –

• What is Clustering?
• What are the types of Clustering?
• What are Association rules?

A supervised learning algorithm is a Machine Learning algorithm that takes place in the presence of a supervisor or a teacher. The training dataset is well labeled, and this learning process goes on until the required performance is obtained. It is useful in a situation where you need to identify if someone is likely to acquire a disease depending upon factors like lifestyle and habits.

Some of the concepts you need to learn about Supervised learning algorithms are –

• What are Regressions?
• What are Classification Trees?
• What are Vector Machines?

Recommended exercise
As an exercise on learning models, you can take a certain dataset and create models with the help of all the algorithms you have learned. Train and test each of the models to enhance their performance.

## Participate in competitions

Data Science competitions provide a certain platform to interact and compete in solving real-world problems since most data scientist’s work is theoretical and they lack the skill of working with real-world data.

Competitions are the best place to learn and augment your skills in Machine Learning and they also act as an opportunity to enhance boundaries and promote creativity among the brightest minds. The experience you gather from these competitions will help you to develop the most feasible solutions while working with big data.

Some of the most popular data competitions to practice Machine Learning algorithms are listed below:

• Kaggle
• International Data Analysis Olympiad (IDAHO)
• Topcoder
• DataHack and DSAT
• Machine Hack

## Learn about deep learning models

Deep Learning is a subfield of Machine Learning which is more powerful and flexible since its process of learning considers the world as a series of concepts where each concept is explained with some other simpler concepts.

The popularity of Deep Learning is because it is power-driven by a huge amount of data. Smartphone assistants like Google Assistant or Siri were created with the help of deep learning models. They also helped global companies to build self-driving cars.

Machines in this era can perform all the basic things that a human can perform like see, listen, read, write, and even speak to deep learning models. They are also a great influence on enhancing the skill set of people working on Artificial Intelligence.

Some of the topics you can cover to gather detailed insights about deep learning models are –

• What are Neural Networks?
• What is Natural Language Processing or NLP?
• What is TensorFlow?
• What is OpenCV?

Recommended exercise
Create a model that can identify a flower from a fruit.

## Learn about Big Data technologies

Big Data refers to the large volume of structured and unstructured data that business giants use for analyzing insights to make better decisions. A massive amount of data is used in day-to-day applications and managing such a huge amount of data is possible because of Big Data.

Big Data uses analytical techniques like Machine Learning, Statistics, Data Mining, etc. to perform multiple operations on a single platform. It allows storing, processing, analyzing, and visualizing data with the help of different tools.

Big Data technologies provide meaning to the machine learning models that have been around for decades. The models now have access to a sufficient quantity of data that can be given as input to the Machine Learning algorithms so that they can come up with outputs useful to organizations.

They have found applications in different sectors starting from Banking, Manufacturing, and to different Tech industries.

• What is Big Data and its ecosystem?
• What is Spark?

Recommended exercise

As an exercise, install a local version of Hadoop or Spark and upload data to run processes. Extract the results, study them, and find different ways to improve them.

## Work on a Machine Learning project

Finally, working on a Machine Learning project is very crucial as it helps to demonstrate your knowledge and skills on the subject. Since you are a beginner, start with a sample machine learning project like a social media sentiment analysis with the help of Facebook or Twitter.
Some of the topics you can cover under this section are:

• How to collect, clean, and prepare data?
• What is Exploratory Data Analysis?
• How to create and select a model?

The steps you need to follow while working on a Machine Learning project are:

• Deciding what problem, you want to solve.
• Deciding the required parameters.
• Choosing the correct training data.
• Deciding the right algorithms.
• Writing the code.
• Checking the results.

The Internet has a plethora of different sources and materials where you can start learning Machine Learning. Some of the most popular courses on Machine Learning along with Certifications are:

## Get started with the Foundations

Machine Learning is an expanding field and having a set of skills on Machine Learning is an investment for the future.

You can establish a firm foundation with the Machine Learning with Python course, where you will study machine learning techniques and algorithms, programming best practices, python coding, and more. This foundations course is intended to help developers of all skill levels get started with machine learning.

Machine Learning is an area where learning will never stop and if you plan your journey of becoming a Machine Learning expert in a well-rounded manner, you will indeed realize the next steps to rapidly propel your learning curve.

### KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com