A Beginner's Guide to Machine Learning

Machine Learning (ML) is the new buzzword everywhere. In this blog, we'll discuss the benefits of using machine learning, the different machine learning algorithms, and how you can start your projects in ML!

What is Machine Learning?

Machine learning is the mathematical simulation of the human brain in a computer. It is a subfield of artificial intelligence based on the concept that machines can learn from data, recognise patterns, and make predictions with little to no human involvement. Applications for machine learning algorithms include email filtering, Computer Vision (CV), data analytics, and much more.

Types of Machine Learning Algorithms

There are four main types of machine learning algorithms:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

Supervised Learning

Supervised learning is when the algorithm is given a training data set and can then learn patterns and generalise information. The most common type of supervised learning is

• Regression - It is a type of supervised learning algorithm in which the algorithm predicts a continuous value.
• Classification - It is another type of supervised learning in which the algorithm predicts a class label for each example in the training data.

Unsupervised Learning 

Unsupervised learning is when the algorithm is not given any training data and must find structure and patterns in the data itself. The most common type of unsupervised learning is clustering when the algorithm groups similar examples together. Other types of unsupervised learning include dimensionality reduction and feature selection.

Semi-supervised Learning

Semi-supervised learning combines supervised and unsupervised learning, where the algorithm is given some training data but not all of it. This can be useful when there is not enough labelled data to train a supervised model but enough unlabeled data to learn from.

Reinforcement Learning

Reinforcement learning is when the algorithm learns by taking actions in an environment and receiving rewards for those actions. Reinforcement learning aims to maximise the total reward received by taking the best possible action at each step.

Common Applications of Machine Learning

Many different applications employ machine learning, including

• Email Filtering
• Detection of Network Intrusions
• Computer Vision (CV)
• Self-Driving Cars
• Product Recommendations
• Speech Recognition

Steps to Get Started with Machine Learning:

Below are the steps to get started with ML:

Familiarise yourself with the basics!

Before you jump into anything too complicated, it's important to familiarise yourself with the basics of machine learning. This means understanding things like what algorithms are and how they work.

Plenty of resources, like Skill-Lync, help you with the basics, so do some research and ensure you understand the basics before moving on.

Figure out what you want to achieve!

What do you want to use machine learning for? Do you want to build a model that can predict something specific? Or do you just want to explore and see what insights you can glean from the data?

Once you know your goal, it will be easier to figure out which algorithm or approach best suits your needs.

Choose a platform or tool!

Many different platforms and tools are available for machine learning, so choosing one can be daunting. But don't worry, there is no need to choose the perfect tool from the get-go. Just pick one that looks promising and give it a try. As you become more comfortable with machine learning, you can experiment with other tools and platforms.

Find resources to learn from!

If you're interested in learning more about machine learning, plenty of resources are available to help you get started. Here are a few of our favourites:

• The 100-Page Machine Learning Book:

This book is a great resource for those who want to learn more about the basics of machine learning. It covers supervised and unsupervised learning, data preprocessing, and model evaluation.

• Machine Learning for Hackers:

This book is perfect for those who want to learn more about how to apply machine-learning techniques to real-world problems. It covers data wrangling, feature engineering, and model selection.

• Introduction to Machine Learning with Python:

This book is a great option for those who want to learn more about machine learning with Python. It covers topics such as data preprocessing, feature extraction, model training and tuning, and deployment.

• Skill-Lync

Skill-Lync is an online platform that offers a variety of courses in this domain to help you get started, the Data Structures and Algorithms using Python, Machine Learning Fundamentals In Depth, Data Science and Machine Learning, and Math behind Machine Learning and Artificial Intelligence using Python.

Tools and Platforms for Working with Machine Learning

There are many different tools and platforms that you can use to work with machine learning. Some of the most popular ones include:

TensorFlow:

TensorFlow is an open-source platform for machine learning. It was developed by Google and released in 2015.

Keras:

Keras is a high-level neural network API written in Python. It was developed by Francois Chollet and released in 2015.

Scikit-learn:

Scikit-learn is a free machine-learning library for the Python programming language. It was released in 2007.

Microsoft Azure Machine Learning Studio:

Azure Machine Learning Studio is a cloud-based tool for building and deploying machine learning models. It was released in 2016.

Conclusion

With the right resources and guidance, anyone can learn machine learning. So now it’s time for you to take your first steps towards becoming an expert in machine learning. Whether you’re a beginner or an experienced programmer, Skill-Lync can help you get started today. We offer various courses on Artificial intelligence, data science, machine learning, etc. With our easy-to-use platform, you can learn about different machine-learning algorithms, techniques, and tools. Plus, you can get hands-on experience with our interactive code examples.