As the demand for Machine Learning is getting bigger every year, you probably want to learn Machine Learning. Let’s start with the basics of how to learn Machine Learning from scratch.
In 2020, the growth of Machine Learning jobs has spiked. Despite the spike, there is still a lack of people with the right skill set.
Brief History of Machine Learning
The term Machine Learning was first coined by Arther Samuel in 1959. He defined it as a Field of study that gives computers the capability to learn without explicitly programmed.
The average base salary of Machine Learning expert is around $140K per year. Sounds exciting right? But this depends on the experience, organization, and position.
But there’s a lot of chaos on how to learn Machine Learning in 2020. Don’t worry we’ll explain the detailed steps to learn Machine Learning from scratch.
Often AI and Machine learning are used interchangeably, but they are both different topics.
What is Machine Learning?
Machine Learning is a subset of AI. In short, ML is the process where the machines learn automatically without human intervention.
Machine Learning involves the use of AI to allow the machines to learn a task by themselves without programming machines about the specifics of the task.
Using ML, computers can learn and recognize the patterns on their own and make accurate predictions of the particular task.
ML process involves that the machine is provided with the data and then a set of algorithms are supplied to machines. The choice of algorithms depends on the type of data we have supplied and the type of task we are trying to automate.
There are 3 types of Machine Learning, let’s take a look at it:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
1. What is Supervised Learning in Machine Learning?
Supervised Learning is similar to your school/college, where the learning is guided by a teacher. The dataset that is supplied will act as a teacher and its role is to train the machine.
Once the model is trained, it can make decisions or predictions when new data is provided to it.
2. What is Unsupervised Learning in Machine Learning?
In Unsupervised Learning, the machine learns through observation and finds structures in the data. Once the machine is provided with the dataset, the model automatically finds the patterns and relationships in the dataset by creating clusters in it.
The unsupervised model would be that it would separate two different fruits but, it cannot add labels to the cluster. Like it cannot tell the difference between the group of fruits.
An example would be, let’s say we provide images of apples, oranges, and mangoes to the model. Based on the patterns and relationship it creates the cluster and divides the dataset into those clusters.
3. What is Reinforcement Learning?
Reinforcement Learning involves an agent to interact with the environment to find the best outcome. Hit and trial method is the concept followed by Reinforcement Learning.
The agent is trained by awarding positive or negative points based on the provided correct or wrong answer.
So how to learn Machine Learning from scratch?
A step by step guide to learn Machine Learning is explained in this article. There are 3 steps to learn ML from scratch :
- Know why you want to learn Machine Learning?
- What is the prerequisite of Machine Learning?
- What is the best resource to learn Machine Learning?
Let’s get started with the basics of Machine Learning. Your first step is the learn the difference between AI and Machine Learning.
Step 1: Why learn Machine Learning?
Machine Learning is a subset of Artificial Intelligence. But what does the machine learning do? By leveraging Machine Learning, software applications will become accurate in predicting outcomes.
What does Google have to say about Machine Learning? “Machine Learning is the Future”. Meaning the future of ML will be bright and open up many career opportunities.
Due to Machine Learning’s capability, more companies are integrating into their daily operations. This gives a glimpse of career opportunities in Machine Learning.
7 reasons why you should learn Machine Learning in 2020.
- Learning ML brings in better career opportunities
- ML engineers get a better salary
- Jobs in Machine Learning are the rise
- ML is linked directly to Data Science
- Machine learning helps increase your efficiency.
- ML recommends products to your customers.
- ML helps to detect fraud.
Now that we know the reason for learning Machine Learning, let’s explore the prerequisite of learning ML.
Step 2: What is the prerequisite of Machine Learning?
In step 1, let us understand the basic element of start learning Machine Learning. That is understanding the prerequisite of ML.
There are 3 prerequisite skill sets that one should have to learn to go ahead with Machine Learning.
- Linear Algebra and Multivariate Calculus
- Python Programming Langauge
1. Learn Linear Algebra and Multivariate Calculus
Learning linear algebra and multivariate calculus is crucial to learn Machine Learning. You might have studied them in either high school or college or universities.
Why are they important? Because both linear algebra and multivariate calculus are important as they are implemented on ML algorithms from scratch.
Machine Learning is heavily focused on Maths. Unless you are very good at maths avoid taking the Machine Learning course.
But if you still want to continue, then your first goal is to learn the basics of linear algebra and calculus.
2. Learn Statistics
Data is an important factor in Machine Learning. You will spend about 80% of your time in collecting data and cleaning data. Statistics is exactly what you need for this.
Statistics is used to handle the collection of data, analysis of data, and presentation of data. Hence this is an important subject to go ahead with Machine Learning.
The core concepts that you should be aware in Statistics are:
- Statistical Significance
- Probability Distributions
- Hypothesis Testing
- Bayesian Thinking, etc.
3. Learn Python
Although other programming languages like R and Scala are used in Machine Learning, Python remains the best. Check out the Coursera’s best Python courses to learn online.
Python is a popular programming language for Machine Learning. Please refer here if you have any questions about learning Python.
Python supports popular libraries for AI and Machine Learning, thus making it an ideal programming language. One cannot skip learning Python when you’re on the path to become a Machine Learning expert.
Python’s popular libraries for AI and Machine Learning are:
- TensorFlow, etc.
So to become a Machine Learning expert, you cannot skip learning Python. Also, read our guide on why you should learn Python.
Note: You don’t have to know in-depth knowledge or a degree in Python, Statistics, Linear Algebra, and Multivariate Calculus. Understanding the basics and core concepts of the topics is enough to get started with Machine Learning.
Step 3: What is the best resource to learn Machine Learning?
Once you’ve passed the prerequisite skill set, you can start learning Machine Learning. But where to find the best resource for learning Machine Learning?
Let’s look at the best resource to start learning Machine Learning and why choose them.
Online Training: Let’s look at the two methods of online training on Machine Learning.
1. Instructor–led Learning
This training offered by Edureka has a course on Machine Learning Certification Training using Python. This learning model has over 7000 satisfied learners.
The advantage of Instructor-led training is that you can ask the questions to the instructor at any time during the lecture. It is quite common to have a question during learning something new and getting answers immediately is a benefiting factor. Because you learn fast.
Instructor-led training offers both weekday and weekend classes. This offers the flexibility for you to choose between any of the fixed schedules.
At the end of the course, you will be awarded the completion certificate ‘Machine Learning Certification’.
Sign up here for learning Machine Learning in Edureka.
2. Self-paced Learning
The advantage of self-paced learning is that you can start learning at any time during your day time. There are over 1 Million learners from DataCamp.
Self-paced learnings offered by DataCamp has many training videos. DataCamp offers a Machine Learning course using different models.
They provide three membership offers: Free, Basic, and Premium. But, we recommend subscribing to Premium membership. Because several challenges and exercises are offered in this segment.
Sign up here for learning Machine Learning in DataCamp.
Bonus Step: Participate in Machine Learning Competitions
The benefit of taking part in Machine Learning Competitions is that you get more exposure in ML. This makes you more proficient in ML.
Entering competitions will enhance both theoretical knowledge and practical implementation.
You can build confidence by using Kaggle’s basic Machine Learning competitions for beginners:
Digit Recognizer: Once you have gained the knowledge of Python and Machine Learning basics, you can get started with this project. This introduces you to the world of neural networks.
Titanic: Machine Learning from Disaster: This is a popular project for beginners in Machine Learning. There are many tutorials available. And supports newbies to introduction to Machine Learning concepts.
Once you complete these competitions, you will become more confident in Machine Learning. You can also sign up on additional guided-projects on Machine Learning from Coursera.
So in this article, we answered the popular questions to Machine Learning. Such as why learn Machine Learning, a prerequisite to ML, resources to learn ML, and how to enhance and build confidence in ML.
Also, we’ve made some detailed research and listed the best online course to learn Machine Learning, make sure you check it out. If you loved this piece of content we share the article with your friends and colleagues.