Machine Learning has transcended all aspects of human life, and it continues to revolutionize our work-life. It has penetrated deep into the way that even humans process things.
Practical Machine Learning has become increasingly important, and many believe it’s more important than theoretical machine learning, but the two go hand in hand. While theoretical machine learning is great for researching what machine learning can achieve and how to alter it, practical machine learning will help you understand how to implement several machine learning models, determine their quality, and implement these models successfully.
More than ever before, any programmer or software engineer needs practical Machine Learning to explore data-based solutions.
To fully understand machine learning and its practicality, there are a few concepts one must grasp.
What is Artificial Intelligence?
Artificial Intelligence is the simulation of human intelligence processes by machines. The ultimate aim is for machines to replicate (as closely as possible) the actions of an objective, intelligent human.
A strong artificial intelligence can provide human-like answers or exercise a human-like approach to questions set by a programmer. Thus, the future of machine learning is one where it becomes increasingly difficult to tell a machine from a man and vice versa. On the other hand, a weak AI can answer specifically set questions without variables.
Therefore, programmers use machine learning to create, interact, process and understand strong AI systems.
Next, we have to fully understand machine learning.
What is Machine Learning?
Machine learning has a dynamic relationship with our world today.
Machine learning is the scientific study of algorithms — models computers adopt to learn human behavior from observations. It is a subset of artificial intelligence which seeks to build systems that learn how AI systems perform and improve performance.
It is a subcategory of AI that uses a range of algorithms that recognize data patterns to gain insight. This is then used to help AI make more human-like (or beyond human-like) decisions.
Practical machine learning evolves when programmers and software engineers study machine learning and use collected data to improve, analyze and increase a computer’s perception and cognition.
With practical machine learning techniques, programmers can study data and algorithms to improve and experiment with AI across various human sectors.
Some technical aspects of machine learning are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Online learning
- Ensemble learning
- Transfer learning
How Does Practical Machine Learning Help?
Practical machine learning allows us to use machine learning to accurately solve a real-world problem.
Real data is hardly ever isolated and identically distributed. So practical machine learning allows programmers to detect concept, covariate, and label shifts. But more importantly, it helps programmers to train and combine deployment models for each given dataset.
The core purpose of practical machine learning is analyzing algorithms to understand models and problems, employing fairness, and deploying the most effective model or model combinations.
You should also remember that machine learning is useful in basically all fields where data has been collected. In the 21st century, this means practically every industry – and will only continue to grow. For example, it’s already a core part of bioinformatics, weather forecasting, medical diagnosis, computer vision, and outlier detection.
Practical Machines Learning vs Theoretical Machine Learning
Theoretical machine learning serves as a driving force for algorithm decisions. The aim is to understand and create theories on how these decisions are made. In addition, it involves trying to increase the precision of models, which will be applied to AI improvement in certain industries. But theoretical students don’t care much about the application – they pay attention to analysis and improvement.
Practical machine learning seeks to approach machine learning and AI from a problem angle. It involves identifying a problem and modeling the problem.
Problem modeling involves trying to understand a problem, especially breaking it down and making it fit into a context already theoretically proven. This is a defining factor for practical machine learning, as theory often relies on available datasets that may not require much urgency.
Next, there are three phases in a typical practical machine learning process:
This is almost always unique to industrial or practical machine learning. Feature engineering may not be necessary for academia, except your research question is on the topic. But for field work, you need the unique data set of your business that will help you train the model.
The model for the project needs to be appropriate for the project. In theory, the model is central to research, and you may design a dataset around it. For practical or industrial machine learning methods, the problem is central, and you will spend time testing models.
The theory equips you with your knowledge of models and the outcome you can expect per feature, but it’s your practical knowledge that helps you decide what works in your industry.
Another thing that features almost exclusively in practical sessions is model assembly. Because datasets in the field are often unpredictable, you may have to assemble multiple models to finally find latent patterns from the features that give the right outcome.
Model assembly may require either stacking, bagging, weighing, or voting.
In essence, practical and academic uses often merge, and it’s difficult to pick which one is more important to a programmer. The theoretical machine learning aspects help you understand models, enhance and develop them for the best possible outcome and make advances in machine learning. While practical machine learning requires a hands-on approach and a flexible process that competes highly with academia’s singular and linear method.
Tips to Help You Study Machine Learning in a Practical Way
If you are transitioning from academia, you may be immersed in theoretical machine learning, or you may be a beginner who wants to learn things practically. In either case, keeping a healthy balance is the key to being a great programmer.
Immerse Yourself in Machine Learning
You probably already have a lot of interest in machine learning, but fully immersing yourself in it allows you to reassess the practical problems it’s solving. You should ask yourself several questions about the possible uses of machine learning and how you specifically want to apply it. This approach will help you see machine learning as a tool for everyday solutions.
Here are some tips for the course of your studies:
- Decide on a machine learning project you want to work on
- Learn the programming language required to work on this project
- Create a timeline for your project
- Decide on a performance metric
- Identify the challenges in executing that project and learn more about it
- Rinse and repeat
Books You Can Read on Practical Machine Learning
If you need literature on machine learning to develop your practical skills, here are a few you can check out.
Hands-on ML with Scikit-Learn, Keras & TensorFlow
Many people have tagged this the best practical introduction to machine learning. This work mixes theories with practice to make otherwise abstract concepts unforgettable. It’s easy for an average machine learning student to comprehend, and the flow in this book is interesting.
The second edition offers many practical examples and includes new topics like Neural Networks, the K-Mix, and Gaussian Mixes. This book may be especially helpful if you are preparing for the Tensorflow Developer Certification.
Machine Learning for Absolute Beginners by Olliver Theobald
This is a great book to learn machine learning core concepts, and it’s particularly useful for people attempting machine learning with internet courses. Concepts like models, AI, algorithms, latent patterns, etc., will become much clearer.
Some important topics covered in the third edition are Regression Analysis, Machine Learning tools and Libraries, Bias/ Variance, Decision Trees, Machine Learning models, Logistic Regression, and k-Means Clustering to find new relationships.
The Hundred Page Machine Learning Book by Andriy Burkov
This book is written in simple language, and it is a great attempt at discussing the basics in just a hundred pages. This is a great fit if you find it difficult to run through so many words.
However, this is not a book for complete beginners. Since it attempts to cram many things into a hundred pages, it tends to oversimplify concepts. Still, it’s a great one endorsed by many top industry members like Sujeeth Varakhedi, eBay’s Head of Engineering.
Deep Reinforcement Learning Hands-on by Maxim Lapan
This book has a fluid and enjoyable writing style that will have you breezing through. It also gives you the practical touch that only a few books do.
The book offers insight on accurate coding, behind-the-scenes machine learning, deep learning model, and many practical insights to balance things.
Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard and Sylvain Gugger
This is a book that helps you understand Deep Learning methods without a Ph.D. Most people view deep learning as an advanced form of machine learning, and it is. However, that’s not to say you can’t start grabbing the rudiments early.
This book intends to provide readers with the maths behind the library’s functions. It provides a step-by-step guide on how to train models to accomplish several tasks, including computer vision, hierarchical clustering, regression models, tabular data, and generative works.
Natural Language Processing with Python
Natural Language is one of the critical concepts in machine learning. This book is an in-depth guide to understanding Python codes and shares NLTK resources on statistical and symbolic Natural Language Processing.
This book contains things like well-structured datasets, linguistic structure in sets, and more. It shows a great interaction between human language, artificial intelligence, and linguistics. Other topics in the book are Parsing and Semantic Analysis and Popular Linguistic Databases.
Practical machine learning is basically about applying machine learning to build everyday solutions. This is important, especially with the ever-growing reception of machine learning in every walk of life.
Practical machine learning is as important as theory, but it requires a hands-on approach and a dexterous shift from model-centered machine learning to problem-centered machine learning.
If you are interested in practical machine learning, you must equip yourself with the right resources. There are several books and various online learning resources you can benefit from. Just ensure you have a healthy mix of academia and practice.
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