Machine Learning Tutorial: Learn ML from Scratch

The deployment of artificial intelligence (AI) and machine learning (ML) solutions continues to advance various business processes, customer experience improvement being the top use case. 

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Today, machine learning has a wide range of applications, and most of them are technologies that we encounter daily. For instance, Netflix or similar OTT platforms use machine learning to personalise suggestions for each user. So if a user frequently watches crime thrillers or searches for the same, the platform’s ML-powered recommendation system will start suggesting more movies of a similar genre. Likewise, Facebook and Instagram personalise a user’s feed based on posts they frequently interact with. 

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In this Python machine learning tutorial, we’ll dive into the basics of machine learning. We’ve also included a brief deep learning tutorial to introduce the concept to beginners.

What is Machine Learning?

The term ‘machine learning’ was coined in 1959 by Arthur Samuel, a trailblazer in computer gaming and artificial intelligence. 

Machine learning is a subset of artificial intelligence. It is based on the concept that software (programs) can learn from data, decipher patterns, and make decisions with minimal human interference. In other words, ML is an area of computational science that enables a user to feed an enormous amount of data to an algorithm and have the system analyse and make data-driven decisions based on the input data. Therefore, ML algorithms do not rely on a predetermined model and instead directly “learn” information from the fed data. 

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Here’s a simplified example – 

How do we write a program that identifies flowers based on colour, petal shape, or other properties? While the most obvious way would be to make hardcore identification rules, such an approach will not make ideal rules applicable in all cases. However, machine learning takes a more practical and robust strategy and, instead of making predetermined rules, trains the system by feeding it data (images) of different flowers. So, the next time the system is shown a rose and sunflower, it can classify the two based on prior experience.

Read How to Learn Machine Learning – Step by Step

Types of Machine Learning 

Machine learning classification is based on how an algorithm learns to become more accurate at predicting outcomes. Thus, there are three basic approaches to machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

In supervised machine learning, the algorithms are supplied with labelled training data. Plus, the user defines the variables they want the algorithm to assess; the target variables are the variables we want to predict, and features are the variables that help us predict the target. So, it’s more like we show the algorithm a fish’s image and say, “it’s a fish,” and then we show a frog and point it out to be a frog. Then, when the algorithm has been trained on enough fish and frog data, it will learn to differentiate between them.

Unsupervised Learning

Unsupervised machine learning involves algorithms that learn from unlabelled training data. So, there are only the features (input variables) and no target variables. Unsupervised learning problems include clustering, where input variables with the same characteristics are grouped and associated to decipher meaningful relationships within the data set. An example of clustering is grouping people into smokers and non-smokers. On the contrary, discovering that customers using smartphones will also buy phone covers is association.

Reinforcement Learning

Reinforcement learning is a feed-based technique in which the machine learning models learn to make a series of decisions based on the feedback they receive for their actions. For each good action, the machine gets positive feedback, and for each bad one, it gets a penalty or negative feedback. So, unlike supervised machine learning, a reinforced model automatically learns using feedback instead of any labelled data.

Also Read, What is Machine Learning and Why it matters

Why use Python for Machine Learning?

Machine learning projects differ from traditional software projects in that the former involves distinct skill sets, technology stacks, and deep research. Therefore, implementing a successful machine learning project requires a programming language that’s stable, flexible, and offers robust tools. Python offers its all, so we mostly see Python-based machine learning projects. 

Platform Independence

Python’s popularity is largely due to the fact that it is a platform-independent language and is supported by most platforms, including Windows, macOS, and Linux. Thus, developers can create standalone executable programs on one platform and distribute them to other operating systems without requiring a Python interpreter. Therefore, training machine learning models become more manageable and cheaper.

Simplicity and Flexibility

Behind every machine learning model are complex algorithms and workflows that can be intimidating and overwhelming for users. But, Python’s concise and readable code allows developers to focus on the machine learning model instead of worrying about the technicalities of the language. Moreover, Python is easy to learn and can handle complicated machine learning tasks, resulting in rapid prototype building and testing.

A broad selection of frameworks and libraries

Python offers an extensive selection of frameworks and libraries that significantly reduce the development time. Such libraries have pre-written codes that developers use to accomplish general programming tasks. Python’s repertoire of software tools includes Scikit-learn, TensorFlow, and Keras for machine learning, Pandas for general-purpose data analysis, NumPy and SciPy for data analysis, and scientific computing, Seaborn for data visualisation, and more.

Also Learn Data Preprocessing in Machine Learning: 7 Easy Steps To Follow

Steps to Implement a Python Machine Learning Project

If you are new to machine learning, the best way to come to terms with a project is to list down the key steps you need to cover. Once you have the steps, you can use them as a template for subsequent data sets, filling gaps and modifying your workflow as you proceed into advanced stages.  

Here’s an overview of how to implement a machine learning project with Python:

1. Define the problem.
2. Install Python and SciPy.
3. Load the data set.
4. Summarise the dataset.
5. Visualise the dataset.
6. Evaluate algorithms.
7. Make predictions.
8. Present results.

What is a Deep Learning Network?

Deep learning networks or deep neural networks (DNNs) are a branch of machine learning based on the imitation of the human brain. DNNs comprise units that combine multiple inputs to produce a single output. They are analogous to the biological neurons that receive multiple signals through synapses and send a single stream of an action potential down its neuron. 

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In a neural network, the brain-like functionality is achieved through node layers consisting of an input layer, one or multiple hidden layers, and an output layer. Each artificial neuron or node has an associated threshold and weight and connects to another. When the output of one node is above the defined threshold value, it is activated and sends data to the next layer in the network. 

DNNs depend on training data to learn and fine-tune their accuracy over time. They constitute robust artificial intelligence tools, enabling data classification and clustering at high velocities. Two of the most common application domains of deep neural networks are image recognition and speech recognition.

Way Forward

Be it unlocking a smartphone with face ID, browsing movies, or searching a random topic on Google, modern, digitally-driven consumers demand smatter recommendations and better personalisation. Regardless of the industry or domain, AI has and continues to play a significant role in enhancing user experience. Add to that, the simplicity and versatility of Python have made the development, deployment, and maintenance of AI projects convenient and efficient across platforms.

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