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What Machine Learning (ML) Entails | Its Key Algorithm Types

Did you know that some of the recommendation engines are a common use case for Machine Learning (ML) today? Other popular uses include fraud detection, spam filtering, malware threat detection, Business Process Automation (BPA), and predictive maintenance. But, you may ask this question too: why is machine learning important?

Well, machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns. As well as its support role in the development of new products. Many of today’s leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations.

Thus, nowadays, machine learning has become a very significant competitive differentiator for many companies. Most Machine Learning (ML) Software Toolkits and Algorithms use historical data as input to predict new output values. That said, let’s learn more about what it’s all about in this free guide in detail.

What Machine Learning (ML) Is All About

Machine Learning (ML) is a type of Artificial Intelligence (AI) that allows software applications to become more accurate at predicting outcomes. More so, without being explicitly programmed to do so. Machine Learning requires a great deal of dedication and practice to learn. Partially, due to the many subtle complexities involved in the wholesome process.

Whilst, ensuring your machine learns the right thing and not the wrong thing. An excellent online course for Machine Learning is Andrew Ng’s Coursera Course for beginners. Below is a tutorial video of what machine learning is for newbies in more detail:

What is Machine Learning (ML)?  A Basic Introduction

Today, Classical Machine Learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learningunsupervised learning, semi-supervised learning, reinforcement learning, etc. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

The Main Machine Learning (ML) Algorithm Types:
  • Supervised Learning: In this type, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm are specified.
  • Unsupervised Learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
  • Semi-Supervised Learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
  • Reinforcement Learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

On one side, Supervised Learning Algorithms require the data scientist to train the algorithm with both labeled inputs and desired outputs. Basically, supervised learning algorithms are good for various task accomplishments.

Consider the following:
  • Binary Classification: Dividing data into two categories.
  • Multiple Classification: Choosing between more than two types of answers.
  • Regression Modeling: Predicting continuous values.
  • Data Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

On the other side, Unsupervised Learning Algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for various tasks as well.

Consider the following:
  • Data Clustering: Splitting the dataset into groups based on similarity.
  • Anomaly Detection: Identifying unusual data points in a data set.
  • Association Mining: Identifying sets of items in a data set that frequently occur together.
  • Dimensionality Reduction: Reducing the number of variables in a data set.

Below is a tutorial video discussing Supervised Vs Unsupervised Machine Learning for beginners to learn more in detail:

Supervised vs. Unsupervised Machine Learning: What's the Difference?

On the other hand, Semi-Supervised Learning Algorithms work by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets.

But, labeling data can be time-consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning.

Some areas where semi-supervised learning is used include:
  • Machine Translation: Teaching algorithms to translate language based on less than a full dictionary of words.
  • Fraud Detection: Identifying cases of fraud when you only have a few positive examples.
  • Labeling Data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.

Last but not least, Reinforcement Learning Algorithms work by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards.

After all, something which it receives when it performs an action that is beneficial toward the ultimate goal. And, by so doing, it’s able to avoid punishments — which it receives when it performs an action that gets it farther away from its goal.

Reinforcement learning is often used in areas such as:
  • Robotics: Robots can learn to perform tasks in the physical world using this technique.
  • Gameplay: Reinforcement learning has been used to teach bots to play a number of video games.
  • Management: Given finite resources and a defined goal, reinforcement learning help enterprises plan budget allocation.

So, who is using Machine Learning and what is it used for in general? Curious about Machine Learning and its many applications? Well, before we discuss a few examples down below, make sure that you also learn all the ins and outs of supervised and unsupervised machine learning. Follow this machine learning tutorial with examples by Toptal to gather more.

Some Examples Of Machine Learning (ML) In Action

As of today, Machine Learning (ML) is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook’s news feed.

Notably, Facebook uses machine learning to personalize how each member’s feed is delivered. Whereas, if a member frequently stops to read a particular group’s posts, the recommendation engine will start to show more of that group’s activity earlier in the feed. Behind the scenes, the engine is attempting to reinforce known patterns in the member’s online behavior.

What if the member change patterns and fail to read posts from that group in the coming weeks? Well, the news feed will adjust accordingly. In addition to recommendation engines, there are other key machine learning uses.

They include the following:
  • Customer relationship management.CRM Software System can use machine learning models to analyze emails and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
  • Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points, and anomalies.
  • Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
  • Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
  • Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

Forthwith, what are the main advantages and notable disadvantages of machine learning? Well, let’s try to find out more!

The Overall ML Advantages And Disadvantages

Generally speaking, Machine Learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars. When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. It also helps in collecting customer data and correlating it with behaviors over time.

And by doing so, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand. Some companies use ML as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses ML to surface ride advertisements in searches.

But, machine learning comes with its very own disadvantages as well. First and foremost, it can be expensive to implement, integrate and adopt. Typically, most machine learning projects are driven by data scientists, who command high salaries. In addition, these projects also require a unique software infrastructure that can be quite expensive to deploy.

Likewise, there’s also the problem of machine learning being biased. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.

Steps To Choose The Right Machine Learning Model

Take, for example, the main role of a Support Vector Machine (SVM) in this case. To enumerate, this is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model set of labeled training data for each category, they’re able to categorize new text.

Markedly, the Decision Tree Algorithm in machine learning is one of the most popular algorithms in use today. Whereby, this is a supervised learning algorithm that is used for classifying problems. But, realistically, the process of choosing the right machine learning model to solve a problem can be time-consuming if not approached strategically.

See the simple steps that you can follow:

Step #1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

Step #2: Collect data, format it, and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

Step #3: Choose which algorithm(s) to use and test to see how well they perform. This step is usually carried out by data scientists.

Step #4: Continue to fine-tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

That said, explaining how a specific ML model works can be challenging when the model is complex. In some vertical industries, data scientists have to use simple machine learning models because it’s important for the business to explain how every decision was made. This is, especially, true in industries with heavy compliance burdens like banking and insurance.

It’s also, important to realize, that there are two main types of Machine Learning Tasks to note: Synthesis & Sampling. Synthesis and sampling are essential tasks in deep learning and machine learning. They are used to generate new data from existing data or to select a representative subset of data for further analysis. There are also three ML pillars to note;

The pillars are: 
  • intention pillars,
  • invention pillars,
  • adaptation pillars.

On one hand, the Intention Pillar emphasizes advancements in human-to-computer and computer-to-machine-learning interfaces. While, on the other hand, the Invention Pillar emphasizes the creation or refinement of algorithms or core hardware and software building blocks through Machine Learning.

Of course, it’s true that complex models can produce accurate predictions, yes, but explaining to a layperson how the output was determined can be quite difficult. This is where a great machine-learning model comes in handy. Obviously, since this is a unique application program that can find patterns or make decisions from a previously unseen dataset.

For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. Amazon ML supports three types of ML models.

They include:
  • binary classification,
  • multiclass classification,
  • and regression.

Be that as it may, the type of model you should choose depends on the type of target that you want to predict.

Takeaway Thoughts:

In a nutshell, while Machine Learning Algorithms have been around for decades, they’ve attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today’s most advanced AI applications. Thus, machine learning platforms are among enterprise technology’s most competitive realms.

Suffice it to say, with most major vendors, including Amazon, Google, Microsoft, IBM, and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training, application deployment, and the like.

Other Related Useful Resource Reference:
  1. Python Machine Learning – W3Schools
  2. See The Three Pillars of Machine Programming – Intel
  3. Supervised Machine Learning: Regression & Classification
  4. The Most Commonly Used Machine Learning Algorithms
  5. Machine Learning Steps: A Complete Guide | Simplilearn
  6. The 3 Stages Of Machine Learning: From BI To ML Stage 1 
  7. Machine Learning Specialization – Coursera

As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify. Continued research into deep learning and AI is increasingly focused on developing more general applications. Today, most AI models require extensive user training.

Specifically, in order to produce Machine Learning Algorithms that are highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks. For more information, please Consult Us and let us know right away.


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