A Guide to Machine Learning 2022

Machine Learning has changed our lives for the past few decades – taking pictures with a blurry background and focused face to virtual assistants answering our queries; we are drastically dependent on applications that execute ML at their core.

A data scientist will scrutinize the received data and extract actionable insights. And an ML engineer will create the self-running software that uses the extracted data and automates predictive models.

These engineers are experienced in fundamental data science skills such as quantitative analysis methods, statistics, data structures & modeling, and foundational software engineering skills.

With innovation around Machine Learning, it's no surprise that any enthusiast looking to advance their career in software technology would choose Machine Learning as a base to set their job.

As Machine Learning is a central factor of data science, algorithms are trained to make predictions through statistical methods, revealing crucial information within data mining projects. 

Significance of Machine Learning

The main goal of ML technology is to help organizations enhance their overall productivity, decision-making process, and process flow. In addition, as machines start learning via algorithms, it will help ventures to resolve data patterns, helping the organization make better decisions without human support.

Some of the Machine Learning benefits are:

Business Transformation

Machine Learning has been changing businesses with the potential to offer valuable insights. For example, the insurance and finance sectors use the technology to create meaningful patterns within big data, prevent fraud, and provide personalized plans to several customers. 

When considering the healthcare sector, fitness and wearable sensors powered by technology allow us to take charge of their health, minimizing the pressure on healthcare experts.

ML is also leveraged in the oil and gas industry to determine new energy sources, system failure predictions, analysis of ground minerals, and more.

As technologies evolve to new heights daily, Machine Learning has been augmenting business or organizational growth. This trend highlights how ML plays a vital role in business transformation.

Instantaneous Predictions

A feature that attracts the ML enthusiast is the swift processing of insightful data from myriad sources – helping to make spontaneous predictions that can be valuable for companies.

ML algorithms provide meaningful data on customers' buying and spending patterns, allowing businesses to devise procedures to minimize loss and maximize profits.

Moreover, it helps determine the backlogs of marketing campaigns and customer acquisition policies.

Types of Machine Learning

Machine learning is classified into three fields:

Supervised Learning

Here, labeled data is used for training the data. Then, the input goes through the Machine Learning algorithm and is used to train the model. 

Once done, we can feed unknown data into the trained ML model and obtain a new desired response.

Prime algorithms that are used for supervised learning are:

• Naive Bayes
• Polynomial Regression
• Decision Trees, and more.

Unsupervised Learning

In this type of ML, the training data is unlabelled and unknown. However, labeled data is necessary for the input to be guided to the ML algorithm, where unsupervised learning occurs. 

This data is used in the algorithm for training the model. Finally, the trained model searches for a pattern and generates the desired outcome. This case is similar to the Enigma machine trying to break code without human intervention.

Prime algorithms that are used for unsupervised learning are:

• Principal Component Analysis
• Fuzzy Means
• Apriori
• Partial Least Squares, and more.

Reinforcement Learning

The ML algorithm identifies data through a trial-and-error process in reinforcement learning and then decides what action yields higher benefits. Three significant components of this ML type are - the agent, the environment, and the steps.

The agent is the decision-maker, the environment consists of everything the agent interacts with, and the actions are what the agent does. This type of ML occurs when the decision-maker chooses activities that increase the expected profit over a given period.

Technical Skills of Machine Learning Engineers

We have learned how ML application operates, followed by various job offers in the IT domain for software engineers and data scientists. To be a part of Machine Learning technology, you need specific technical and soft skills. 

Neural Network Architecture

Neural networks, called Simulated Neural Networks (SNN), are the predefined algorithm sets used for Machine Learning task implementation.

They offer models and play a vital role in this futuristic technology. Now, ML seekers must be skilled in neural networks because it provides an understanding of how our brain works and assist in model & simulating an artificial one. 

Some of the neural network areas that are essential for ML are:

• Boltzmann machine network
• Convolutional neural networks
• Deep auto-encoders

Natural Language Processing (NLP)

It is a branch of linguistics, AI & computer science that, combined with Machine Learning, Deep Learning (DL), and statistical models, enables computers to process human language in the form of spoken words and text and understand its whole meaning with the writer's intent.

Some techniques and libraries of NLP technology used in ML are:

• Word2vec
• Summarization
• Genism & NLTK
• Sentiment analysis

Applied Mathematics

Machine Learning is all about developing algorithms that can learn data to predict. Hence, mathematics is significant for solving data science projects' DL use cases. Therefore, to be an ML engineer, you must be an expert in the following math specializations.

For instance, choosing appropriate algorithms to suit the outcomes, understanding & working with parameters, deciding validation approaches, and estimating the confidence intervals.

If you are wondering about the math proficiency level one must hold to be an ML engineer, it depends on the engineer's station. 

Data Modeling & Evaluation

When Machine Learning has to work with extensive data and employ them in predictive analytics, data modeling & evaluation become essential to deal with these bulks and estimate the final model's good.

Hence, the following concepts are must-learn skills for an ML engineer:

• F1 Score
• Log loss
• Mean absolute error
• Confusion matrix
• Classification accuracy
• Area under curve
• Mean squared error

Soft Skills for Machine Learning Engineers

While ML engineering is a technical job, soft skills such as problem-solving, collaboration with others, communication, time management, etc., lead to the successful completion and delivery of the project.

Team Work

Machine Learning Engineers are often at the center of AI initiatives within a firm, so they naturally work with software engineers, product managers, data scientists, marketers, and testers. The potential to work closely with others and contribute to a supportive working environment is a skill many recruiters seek in ML engineers.

Problem-solving 

The potential to solve an issue is a vital skill required for both software & Machine Learning engineers and data scientists. ML focuses on solving challenges in real time, so the potential to think creatively and critically about the problem and develop solutions accordingly is a fundamental skill.

Communication 

Machine Learning Engineers must possess top-notch communication skills when communicating with shareholders regarding the project objectives, timeline, and expected delivery. In addition, we know that ML engineers collaborate with data scientists, marketing & product teams, research scientists, and more; hence, communication skill is crucial.

Domain Knowledge 

To create self-running software and optimize solutions leveraged by end-users and businesses, Machine Learning Engineers should have an insight into the requirements of business demands and the type of issues the software is solving. 

Without domain knowledge, an ML engineer's recommendation may lack accuracy, their task may overlook compelling aspects, and it might be strenuous to evaluate a model.

Programming Skills of Machine Learning Engineers

Machine learning is about coding and feeding the machines to carry out tasks. Therefore, ML engineers must have hands-on experience in software programming and related subjects to provide the code.

ML Algorithms & Libraries 

Machine Learning engineers are expected to work with myriads algorithms, packages, and libraries as part of daily tasks. 

ML engineers must be skilled with the following ML algorithms and libraries:

• Knowledge in packages & APIs - TensorFlow, Spark MLlib, sci-kit-learn, and more.
• Decide and choosing of hyperparameters that impact the learning model & the result.
• Algorithm selection provides the best performance from support vector machines, Naive Bayes Classifiers, and more.

Unix

Machine Learning Engineers require most servers and clusters to operate are Linux (Unix) variants. Though they can be performed on Mac & Windows, more than half of the time, they are required to run on Unix systems only. Therefore, having good knowledge of Linux & Unix is vital to being an ML engineer.

Computer Science Fundamentals & Programming

Engineers must apply the concepts of computer science and programming accurately as per the situation. The following ideas play a significant role in ML and are a must on the skillset list:

• Algorithms: search, sort, optimize, dynamic programming
• Computer architecture: memory, bandwidth, cache, distributed processing, and more.
• Data structures: queues, trees, stacks, graphs, and multi-dimensional arrays
• Complexity & computability: big-O notation, P vs. NP, approximate algorithm, and more.

System Design and Software Engineering

Machine Learning Engineers must have strong knowledge of the following areas of software programming & system design, as all they do is code:

• Top-notch measures to circumvent bottlenecks & develop user-friendly outcomes.
• Algorithm scaling with data size.
• Interacting with different working components and modules using library calls, REST APIs, and database queries.
• Fundamental software design methodologies and codings like testing, requirement analysis, and version management.