How to Start Learning Machine Learning?

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

And that was the beginning of Machine Learning! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

But there is still a lot of doubt about what exactly is Machine Learning and how to start learning it? So this article deals with the Basics of Machine Learning and also the path you can follow to eventually become a full-fledged Machine Learning Engineer. Now let’s get started!!!

What is Machine Learning?

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

How to start learning ML?

This is a rough roadmap you can follow on your way to becoming an insanely talented Machine Learning Engineer. Of course, you can always modify the steps according to your needs to reach your desired end-goal!

Step 1 – Understand the Prerequisites

In case you are a genius, you could start ML directly but normally, there are some prerequisites that you need to know which include Linear Algebra, Multivariate Calculus, Statistics, and Python. And if you don’t know these, never fear! You don’t need a Ph.D. degree in these topics to get started but you do need a basic understanding.

(a) Learn Linear Algebra and Multivariate Calculus

Both Linear Algebra and Multivariate Calculus are important in Machine Learning. However, the extent to which you need them depends on your role as a data scientist. If you are more focused on application heavy machine learning, then 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

Data plays a huge role in Machine Learning. In fact, around 80% of your time as an ML expert will be spent collecting and cleaning data. And statistics is a field that handles the collection, analysis, and presentation of data. So it is no surprise that you need to learn it!!!
Some of the key 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 prefer to skip Linear Algebra, Multivariate Calculus and Statistics and learn them as they go along with trial and error. But the one thing that you absolutely 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. In fact, there are many Python libraries that are specifically useful for Artificial Intelligence and Machine Learning such as Keras, TensorFlow, Scikit-learn, etc.

So if you want to learn ML, it’s best if you learn Python! You can do that using various online resources and courses such as Fork Python available Free on GeeksforGeeks.

Step 2 – Learn Various ML Concepts

Now that you are done with the prerequisites, you can move on to actually learning ML (Which is the fun part!!!) It’s best to start with the basics and then move on to the more complicated stuff. Some of the basic concepts in ML are:

(a) Terminologies of Machine Learning

• Model – A model is a specific representation learned from data by applying some machine learning algorithm. A model is also called a hypothesis.
• Feature – A feature is an individual measurable property of the data. A set of numeric features can be conveniently described by a feature vector. Feature vectors are fed as input to the model. For example, in order to predict a fruit, there may be features like color, smell, taste, etc.
• Target (Label) – A target variable or label is the value to be predicted by our model. For the fruit example discussed in the feature section, the label with each set of input would be the name of the fruit like apple, orange, banana, etc.
• Training – The idea is to give a set of inputs(features) and it’s expected outputs(labels), so after training, we will have a model (hypothesis) that will then map new data to one of the categories trained on.
• 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 – This involves learning from a training dataset with labeled data using classification and regression models. This learning process continues until the required level of performance is achieved.
• Unsupervised Learning – This involves using unlabelled data and then finding the underlying structure in the data in order to learn more and more about the data itself using factor and cluster analysis models.
• Semi-supervised Learning – This involves using unlabelled data like Unsupervised Learning with a small amount of labeled data. Using labeled data vastly increases the learning accuracy and is also more cost-effective than Supervised Learning.
• Reinforcement Learning – This involves learning optimal actions through trial and error. So the next action is decided by learning behaviors that are based on the current state and that will maximize the reward in the future.

(c) How to Practise Machine Learning?

• The most time-consuming part in ML is actually data collection, integration, cleaning, and preprocessing. So make sure to practice with this because you need high-quality data but large amounts of data are often dirty. So this is where most of your time will go!!!
• 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. This is easier to do if you understand various tuning parameters and regularization methods applied on different models.

(d) Resources for Learning Machine Learning:

There are various 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 quite popular. It focuses on machine learning, data mining, and statistical pattern recognition with explanation videos are very helpful in clearing up the theory and core concepts behind ML.
• If you want a self-study guide to Machine Learning, then Machine Learning Crash Course from Google is good for you as it will provide you an introduction to machine learning with video lectures, real-world case studies, and hands-on practice exercises.
• In case you prefer an offline course, the Geeksforgeeks Machine Learning Foundation course will be ideal for you. This course will teach you about various concepts of Machine Learning and also practical experience in implementing them in a classroom environment.

Step 3 – Take part in Competitions

After you have understood the basics of Machine Learning, you can move on to the crazy part!!! Competitions! These will basically make you even more proficient in ML by combining your mostly theoretical knowledge with practical implementation. Some of the basic competitions 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 popular beginner project for ML as it has multiple tutorials available. So it is a great introduction to ML concepts like data exploration, feature engineering, and model tuning.
• Digit Recognizer: The Digit Recognizer is a project after you have some knowledge of Python and ML basics. It is a great introduction into the exciting world neural networks using a classic dataset which includes pre-extracted features.

After you have completed these competitions and other such simple challenges …Congratulations!!! You are well on your way to becoming a full-fledged Machine Learning Engineer and you can continue enhancing your skills by working on more and more challenges and eventually creating more and more creative and difficult Machine Learning projects.