Top 5 Home Machine Learning eBooks for Beginners
After everything is said and done, these unique periods of economic instability present unique chances for acquiring new skills and preparing for the upswing of new jobs.
Given that nearly all firms are transitioning to digital processes and service-based goods, machine learning (ML) will be very valuable.
To thrive in this new environment, the seamless flow of data operations will be critical, and ML is the greatest method to apply the power of automation to the infrastructure and procedures that keep people engaged and delighted with their digital services and connected devices.
While much has been written about how machine learning and the larger subject of artificial intelligence (AI) could render many current employment obsolete, this viewpoint ignores a more important fact:
Any jobs lost will not be to ML, but to individuals who understand how to use machine learning to become more productive.
A single IT expert may use ML to automate all of their rote, repetitive, and least productive daily chores, allowing them to focus on higher-order strategic challenges.
This will result in concrete advantages to the organization’s bottom line through improved performance, cheaper costs, the opening of new markets, the creation of new products, and a variety of other means.
These advantages are easily assessed, documented, and attributed to the responsible employee – the individual whose everyday responsibilities are performed by computers.
The problem, of course, is acquiring the skill sets required to efficiently exploit ML.
Is it possible to learn this with reasonable ease and at a minimal cost? Is it possible to do it at home in one’s spare time?
Both questions are answered with a qualified “yes.”
All that is required are the appropriate materials that clearly and concisely describe this technology.
The eBooks listed below are available, reasonably priced, and give a good basis for starting a new profession or advancing an existing one with machine learning.
Python is a popular machine learning language, so it’s an excellent place to start. This eBook teaches the fundamentals of creating clean, succinct code in Python 3.
It emphasises object-oriented programming (OOP), which may be used for beginning data research, visualisation, web apps, and other tasks. It also explains how to utilise Python to automate data-intensive jobs with predictive machine learning.
Instruction is delivered in the form of a workshop, using step-by-step learning tools that emphasise the practical aspect of Python programming rather than heavy, abstract theorising. It also allows students to study at their own speed, whether that means completing a single exercise each day or cramming a whole course in a single weekend.
Throughout, new abilities are added as the learner produces real-world code, and a certificate of accomplishment may be shared and confirmed.
Fundamentals of Artificial Intelligence and Machine Learning
To work with machine learning, you must first grasp how it works and, more crucially, how it differs from traditional software.
This book integrates the fundamentals of Python to machine learning in respect to real-world concepts and issues. You’ll learn how to use regression and classification techniques, as well as predictive analysis with decision trees and random forests, along the way.
As you develop, you will graduate from simple tasks to creating completely sophisticated programmes with complex capabilities.
beginning with k-means and mean shift algorithms and evolving to deep learning and artificial neural networks (ANN).
After finishing, you should be able to run your own ML apps in real-world circumstances.
Pandas is the most popular library in the Python universe, used by data analysts all over the world to handle and organise digital data.
This book delves into the many capabilities available in Pandas, such as multi-indexing, data structure change, and sampling. It also allows intermediate and expert practitioners to apply data insights to a variety of fields, such as Bayesian statistics, predictive analytics, and time-series analysis.
After finishing this book, readers will know how to utilise pandas on complicated data sets for more efficient analysis and more accurate findings.
It also teaches you how to create interactive business reports with the popular Jupyter notebook.
Python Machine Learning
Now that you’ve mastered the fundamentals of programming and data manipulation, it’s time to get to the heart of machine learning.
This book teaches you how to grasp the frameworks, models, and strategies that enable machines to “learn” from data. It includes instructions for using scikit-learn and TensorFlow 2.0 for ML and deep learning, as well as examples of how to apply these principles to image categorization, intelligent apps, and other tasks.
Readers will also discover the most recent methods for developing and training artificial neural networks, GANs, and other models, as well as the best approaches to analyse and improve their operations.
Additional courses address how to utilise regression analysis to forecast continuous goal outcomes, as well as how to analyse text and social media using sentiment analysis, a recently established subfield of Natural Language Processing (NLP).
The book covers the fundamentals of Python-based machine learning as well as practical applications through straightforward explanation, visualisation, and working examples, allowing students to create real-world models and applications on their own.
Cookbook for Python Feature Engineering
For those who have mastered the fundamentals of machine learning, this book gives the fine-tuning required to construct enhanced models, quicker procedures, and more elegant code.
Readers will master the ins and outs of dealing with continuous and discrete datasets by following how-to instructions for essential Python programmes such as pandas, scikit-learn, Featuretools, and Feature-engine in addition to converting characteristics from unstructured data
Key “recipes” also demonstrate how to automate feature engineering to simplify difficult processes using techniques like as the box-cox transform, power transform, and log transform, and then apply these procedures to machine learning, reinforcement learning, and natural language processing.
Readers will also learn how to impute missing values, encode categorical variables, rapidly and efficiently extract insights from text, and generate new features from transactional and time series data.
Experience is the finest instructor in any complicated learning effort. Nonetheless, we must all begin somewhere.
There are two options for anyone experiencing significant downtime at home amid the current crisis::Stare at the walls as the hours pass, or take the initiative to put yourself in the greatest position to succeed in the post-virus economy.