"Careers in Information Technology: Machine Learning Engineer": GoodMan, #1

By Patrick Mukosha

"Careers in Information Technology: Machine Learning Engineer" offers a comprehensive and insightful exploration into the dynamic and evolving field of machine learning within the realm of Information Technology. Authored by an esteemed IT expert, this book serves as a valuable guide for aspiring professionals, career changers, and anyone intrigued by the intersection of technology and artificial intelligence.

 

The book begins by providing a solid foundation in understanding machine learning, breaking down complex concepts into accessible language. It covers the fundamental principles, algorithms, and methodologies that form the backbone of machine learning, ensuring readers grasp the core concepts before delving into the specific role of a Machine Learning Engineer.

 

With a keen focus on career development, the author meticulously details the skills and qualifications required to thrive in this exciting field. From programming languages and statistical analysis to domain-specific expertise, readers gain a clear understanding of the multifaceted skill set demanded by employers in the machine learning domain.

 

The book not only explores the technical aspects of machine learning but also delves into the ethical considerations and societal implications of deploying AI systems.

 

A significant portion of the book is dedicated to providing a roadmap for career progression in the field of machine learning. The author shares experiences and industry anecdotes, shedding light on the day-to-day responsibilities of a Machine Learning Engineer and the various career paths available within this domain. Practical advice on building a strong professional network, staying updated on industry trends, and adapting to technological advancements adds immense value to the reader's career journey.

 

Whether the reader is a student considering a degree in machine learning, a seasoned professional contemplating a career shift, or an IT enthusiast fascinated by the future of technology, "Careers in Information Technology: Machine Learning Engineer"   serves as an indispensable guide to understanding, entering, and thriving in the captivating world of machine learning within the IT landscape.

• Language: English
• Publisher: Patrick Mukosha
• Release date: Jan 3, 2024
• ISBN: 9798224391813

Patrick Mukosha

Patrick Mukosha is an ICT & Management Consultant. With 15+ years of IT experience, he's passionate about all things ICT. He also loves to bring ICT down to a level that everyone can understand. His works have been quoted on Academia by Researchers and ICT Practitioners (www.academia.edu). He has a PHD and MBA from AIU, USA, BSc(Hons) ICT, UEA, UK, Dipl, CCT, UK. He's a founder of PatWest Technologies.

Book preview

Chapter 1: Introduction to Machine Learning

1.1.  What is Machine Learning?

Within the subject of computer science, machine learning focuses especially on artificial intelligence. It replicates how humans learn by using algorithms to understand data. The machine's objective is to increase the accuracy of its learning and give the user data based on that learning. Machine learning is the branch that focuses on creating models and algorithms that let computers learn and make judgments without needing to be explicitly programmed for a specific task. Enabling systems to learn from data and gradually improve their performance is the fundamental goal of machine learning.

The main goal of machine learning is to give computers the ability to recognize patterns, interpret data, and modify their behaviour in response to past experiences. Machine learning systems use examples and data to generalize and make predictions or conclusions in new, unforeseen scenarios, as opposed to depending solely on explicit programming for every task. Social media sites such as Meta employ machine learning techniques to deliver ads to users according to their likes, posts, and preferences. Similar to this, retail portals like Amazon employ algorithms to recommend products to customers based on their past purchases and viewing choices.

To identify patterns in vast volumes of data, including words, numbers, photos, clicks, and anything else that can be digitally stored, machine learning leverages statistics. Many of the services we use every day, such as recommendation engines like those on Netflix, Disney+, YouTube, and Spotify, are powered by machine learning technology. Search engines, social media feeds like Facebook and Twitter, and voice assistants like Siri and Alexa all depend on machine learning to work.

In each of these cases, these platforms leverage machine learning to gather as much information as they can about you, including your favourite directors, the links you click, and the status updates that make you laugh or cry. Based on this information, they create highly educated guesses about what you might want to purchase, watch, or click on next. Conversely, voice assistants utilize machine learning to infer which words best fit the noises that are coming out of your mouth.

Actually, the procedure is quite simple: identify the pattern, then apply it. But it controls the globe almost entirely. This is largely attributable to a creation made in 1986 by Geoffrey Hinton, who is regarded as the father of deep learning.

1.1.1.  Machine Learning Models

There are three main types of machine learning models.

1.1.1.1.  Supervised Machine Learning: Supervised machine learning, or supervised learning, is characterized by the use of labelled datasets to train algorithms for precise outcome prediction or data classification. The model modifies its weights when input data is entered until a satisfactory fit is achieved. This happens during the cross-validation phase, which makes sure the model doesn't overfit or underfit. Sorting spam into a different folder from your email is just one example of the many real-world problems that supervised learning helps enterprises solve at scale. Neural networks, naïve bayes, logistic regression, random forest, linear regression, and support vector machines (SVM) are a few techniques used in supervised learning.

1.1.1.2.  Unsupervised Machine Learning: Unsupervised learning, sometimes referred to as unsupervised machine learning, is the process of analysing and grouping unlabelled datasets using machine learning algorithms. These algorithms find hidden relationships or patterns in the data without requiring human assistance. This approach is perfect for consumer segmentation, cross-selling tactics, exploratory data analysis, and pattern and image recognition since it can identify patterns and similarities in data. It can also be applied to dimensionality reduction, which lowers the amount of features in a model. Two popular methods for this are singular value decomposition (SVD) and principal component analysis (PCA). Neural networks, probabilistic clustering techniques, and k-means clustering are among more algorithms utilized in unsupervised learning.

1.1.1.3.  Semi-Supervision Learning: A satisfying middle ground between supervised and unsupervised learning is provided by semi-supervised learning. It guides categorization and feature extraction from a larger, unlabelled data set during training by using a smaller, labelled data set. The issue of insufficient labelled data for a supervised learning system can be resolved through semi-supervised learning. It's also beneficial if labelling sufficient data would be too expensive.

However, broadly speaking, there are various kinds of machine learning techniques, such as:

1.1.1.4.  Supervised Learning: A labelled dataset, consisting of matched input and output labels, is used to train the algorithm. In order to generate predictions on fresh, unseen data, the model must understand the mapping between inputs and outputs. A mathematical model of a set of data that includes the inputs and the intended outputs is created by supervised learning techniques. A collection of training examples make up the data, which is referred to as training data. Every training example contains one or more inputs as well as the supervisory signal—the desired result. The training data is represented by a matrix in the mathematical model, and each training sample is represented by an array or vector, also referred to as a feature vector.

Supervised learning techniques develop a function that may be used to predict the output associated with fresh inputs by iteratively optimizing an objective function. When inputs were not included in the algorithm, an optimal function enables the algorithm to accurately identify the output. An algorithm is said to have learned to do a task when it gradually increases the accuracy of its predictions or outputs. Algorithms for supervised learning encompass active learning, regression, and classification. Regression techniques are used when the outputs can have any numerical value within a range, and classification methods are used when the outputs are constrained to a certain set of values. An incoming email would be the input of a classification system, for instance, which filters emails; the output would be the name of the folder in which the email should be filed.

In supervised machine learning, similarity learning is closely associated with regression and classification. Its objective is to learn from examples by utilizing a similarity function that quantifies the degree of similarity or relatedness between two objects. It can be used for face verification, speaker verification, visual identity tracking, rating, and recommendation systems.

1.1.1.5.  Unsupervised Learning: In this method, the algorithm is given unlabelled data and is left to its own devices to identify patterns or relationships in the data. Common tasks in unsupervised learning are dimensionality reduction and clustering. Algorithms for unsupervised learning discover structures in unlabelled, uncategorized data.

Unsupervised learning algorithms find patterns in the data and make decisions based on whether or not these patterns are present in each new piece of data, rather than reacting to feedback. Density estimation, dimensionality reduction, and clustering are three important uses of unsupervised machine learning. The method of discovering big indel based haplotypes of a gene of interest from a pan-genome has also been expedited by unsupervised learning algorithms. The alignment image is transformed into a learning regression problem using Clustering via Large Indel Permuted Slopes, or CLIPS. Segments that share the same set of indels can be identified thanks to the different slope (b) estimates between