How to Start with Machine Learning?
In today's world, every day, we are getting new software, applications, gadgets, and many more technologies that make our lives easier and fast. New technologies are evolving day by day and changing the way of doing a task done in the traditional way, and one of such technologies is Machine Learning. Although machine learning is not a new technology, it is coming with new surprising innovations every day, making it one of the most popular and demanding technology among each tech enthusiast.
Currently, machine learning has become one of the best career choices among beginners and professionals. It is one of the best careers with great growth chances and packages per various surveys. However, many of us have difficulties taking a start with machine learning. Lots of beginners and professionals want to make their career in this field. Still, they don't have sufficient information as to how they can start, what should be the best path for machine learning, what technologies and mathematics they should know, and many more. So, in this topic, we will discuss how you can start with machine learning, what the prerequisites should be, etc. Let's start with the basic understanding of What Machine Learning is?
What is Machine Learning?
Machine Learning is a subfield of Artificial Intelligence that teaches machines how to make predictions using past data and experiences. For true machine learning, it doesn't need any human intervention and explicit programming to learn from data and identify the pattern. It combines or overlaps with other fields, including statistics, Mathematics, Data Science, Big data, etc. For example, different machine learning algorithms are used in data science.
A typical machine learning process starts with feeding good quality and pre-processed data; algorithms learn from this data and build the model to make predictions. The use of algorithms depends on the type of task or problem.
Machine Learning is broadly classified into four types:
1. Supervised Machine Learning
Supervised Learning is a type of machine learning that teaches machines with the labeled dataset. With the labeled datasets, the model makes predictions and provides an accurate output. Supervised learning helps organizations to solve various real-world problems, such as classifying emails as Spam or Not-spam.
Supervised machine learning is mainly classified into two types of problems, which are:
Supervised learning Algorithms:
Some of the commonly used supervised learning algorithms are as follows:
2. Unsupervised Machine Learning
Unsupervised learning is a type of machine learning which uses an ML algorithm to analyze and group the unlabeled dataset. This machine learning technique aims to find the hidden pattern from the given dataset and group them together as per the similarities and differences between the data point. As it can find the hidden pattern from the data, it is suitable for complex tasks such as customer segmentation, image recognition, exploratory data analysis, etc.
Unsupervised learning technique is mainly classified into two types, which are:
Unsupervised Learning Algorithms
Some of the commonly used Unsupervised Learning algorithms are as follows:
3. Semi-supervised Machine Learning
Semi-supervised learning combines both supervised and unsupervised learning techniques as it involves training of algorithm with an unlabelled dataset similar to unsupervised learning with a small amount of labeled dataset similar to supervised learning. It improves or overcomes the limitations of supervised and unsupervised learning. It enhances the learning accuracy of the model and is also much more cost-effective than the supervised learning technique.
4. Reinforcement Machine Learning
Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or a penalty.
In Reinforcement Learning, the agent learns automatically using feedback without any labeled data, unlike supervised learning. RL solves a specific type of problem where decision-making is sequential and the goal is long-term, such as game-playing, robotics, etc.
How to start with Machine Learning: Learn ML by Self.
Now, let's directly jump to how one should start learning Machine Learning on their own. So below are the main four steps you should follow if you are a very beginner in this field and starting to learn on your own.
Now let's understand each step in detail.
Step-1: Understand the Prerequisites
Machine Learning might appear a scary field of study if you skip to understand and learn about its prerequisites, as these perquisites build a base for your learning, create interest in this field, and also boost your confidence for further. So, it is really important to first create your strong base by learning and understanding the prerequisites for Machine Learning. After completing these prerequisites, you will be able to understand the further concepts in a much easier way, irrespective of your educational background.
Below are some important prerequisites for Machine Learning:
Sometimes, lots of people skip learning Linear algebra & Multivariate Calculus, and statistics, but you cannot ignore Python. However, there are a few languages used in ML-like Scala, Ruby, etc., but currently, Python and R are the most popular among them.
Step-2: Understand the Essential theory behind Machine Learning
After getting an idea about important prerequisites for learning Machine learning, it's time to focus on the essential machine learning concepts. To make your career in the ML industry, you need to know all essential concepts from beginning to advanced level. So, we have compiled a few important ML concepts and theories behind machine learning to help those who have decided to make a career in this domain. These are as follows:
1. Techniques in Machine Learning:
Machine learning is categorized mainly into four types as follows:
2. Key terminologies used in ML:
Before landing in any domain, you must be aware of their key terms to understand their concepts very well. Similarly, in machine learning, there are so many important terminologies that you should have a look at, which are as follows:
3. Resources to Learn Machine Learning Concepts
To understand and learn about the essential theory and concepts of Machine Learning, there is a variety of courses and books available online. These courses are provided by well-known universities and platforms like Udemy, Coursera, etc.
The two most popular courses in Machine Learning are given Below:
This is one of the best courses on Machine Learning instructed and created by Andrew Ng, a pioneer in Machine Learning and Artificial Intelligence as well as one of Coursera's founders. The best thing about this course is that it is a freely available online course, and it provides a strong and clear explanation of the core concepts of Machine Learning.
After learning the basic concepts, if you want to spend some amount of money, then firstly go for the "Machine Learning A-Z course" on Udemy Platform. This will brush up on your theoretical concepts as well as give you practical exposure to implementing various ML algorithms, including simple linear Regression, Logistic Regression, Confusion Matrix, etc.
Apart from these courses, there are some popular books for Learn Machine Learning Concepts:
Step-3: Targeted Practice- Practice on Datasets
After learning the basic and required concepts of Machine Learning, the next step is to do practice on various core concepts and datasets. The targeted practice involves the below points:
Step-4: Build Machine Learning Projects- Take Part in Competitions
After completing the above steps, it's time to move on to evaluate your ML skills by building different ML projects and taking part in competitions.
This step will make you proficient in machine learning as you will be able to use your theoretical concepts with practical implementation. There are some popular projects available online that you can complete, which are given below:
In this way, if you follow the above-given steps, then you will have great ML skills in comparison to others. Through this learning path, you will become a full-fledged Machine Learning Engineer, and you can continue practicing to enhance your skills for working in a more challenging environment.