An ultimate, beginner’s introduction to machine learning

For many people, their first experience and introduction to Machine Learning was the ‘Recommendations’ feature on Amazon. The system was able to predict future choices to a consumer based on what previous choices they had made. Many people conflate the concepts of Artificial Intelligence and Machine Learning, whereas, AI is the ability of a machine to perform intelligent tasks, while Machine Learning refers to the ability of a machine to find meaningful patterns and information from the data that it is given.

As long ago as 1959, the MIT engineer Arthur Samuels described machine learning as a “Field of study that gives computers an ability to learn without being explicitly programmed. The key change was the sudden availability of big data to inform machine-learning algorithms. “These algorithms provide a way to forecast future behavior and anticipate forthcoming problems,” according to Business Insider.

Machine Learning heralds a profound change in the technology around us, and it is already having present in many aspects of our lives.

Application of Machine Learning:

Let’s take a look at 3 areas where ML is having a profound impact:

1. Traffic Management: The problem of traffic jams and commuter rush hours is something that most of us have to live with in our daily lives. It seems impossibly complex to solve. There are multi-mode transportation models to take into account, shifting weather dynamics, seasons, construction, accidents and so many other variables to consider on an ongoing basis. Despite the complexity, applications such as Google Maps have found ways to use location data from smartphones to analyze traffic movements in real time. Maps use the fundamentals of ML to ingest vast amounts of data and recommend traffic routes to commuters that will minimize travel time.
2. Self-Driving Cars: Once the idea of driverless cars was just the stuff of science fiction. But no more. The future is rushing towards us at full-speed, and most car manufacturers expect to be in production of driverless cars in the next few years. Autonomous vehicles use an array of sensors, cameras, algorithms and masses of real-time data to create transport options that are predicted to dramatically reduce accidents, congestion, and pollution in the near future. Machine Learning is central to the way that self-driving cars will be operating in the near future.
3. Spam Filters: It’s not only in massively complex applications that ML is coming in handy. Consider the daily work and the hundreds of decisions that must be made by spam filters on your email inbox. Simple rules are not enough to combat effective spammers who are constantly changing the way they work.“Machine learning allows the software to adapt to each user based on his or her own requirements. When the system flags some emails as spam, the user’s response to these emails (either reading or deleting them) will help train the AI agent to better deal with this kind of email in the future,” explains DigitalTrends.com

Introduction to Machine Learning (ML) Process 

Determining which data to use is the first step in effective machine learning. Once that data is selected, then it usually has to be formatted to correctly fit the process. Experts in the field are best suited to help companies select and prepare their data. You should bear in mind that getting to the point where the data is ready to process is often the most time-consuming part of any project.

Once the data is ready, then the team will choose algorithms that are suitable to the project. Microsoft explains how “these algorithms typically apply some statistical analysis to the data. This includes relatively common things, such as a regression, along with more complex approaches, including algorithms with names such as two-class boosted decision tree and multiclass decision jungle.”

Data experts combine the machine learning algorithms with the prepared data in various ways until they achieve the result they want to get. Once that pattern is established, then ML allows you to generate code which can be used in any new sets of data to achieve the same results quickly and effectively.

Read also: Building Enterprise-Class Machine Learning Apps Using Microsoft Azure

Integrating Artificial Intelligence with Machine learning 

There are many common misconceptions around the use of artificial intelligence. People often imagine it to be a reference to robotics, but in fact there are many more subtle applications of AI which are already gaining traction. Others see AI as being something that will replace workers, but in fact it can be used as a tool to augment and improve the work that people do.

“In the age of the connected customer, the most effective method of closing the customer experience gap is for companies to invest in advanced predictive analytics and artificial intelligence (AI) powered customer relationship management (CRM) platforms,” says Vala Afshar, Chief Digital Evangelist at Salesforce.

CEOs and CTOs need to understand the capabilities of AI in their chosen field, and understand how to build a business case for incorporating advanced technologies. Commitment to integrating AI into your business should come from the top-down, and be driven by partnerships with specialists who understand how it can work for you.

Machine Learning is often the first step towards a more comprehensive AI strategy.

Popular Machine Learning Methods

2 out of 4 methods are widely accepted and implemented, they are: Supervised learning and Unsupervised learning. Let’s take an overview of all the four types.

1. Supervised Learning: Trained algorithms with labeled examples are known as supervised learning method. In this, any piece of tool or machine could have either of the two data points: Failed (“F”) or Runs (“R”). A set of inputs with the corresponding correct outputs is received by the learning algorithm and to find errors the algorithm learns by comparing its actual output with correct outputs. Then comes the process of modifications. Supervising Learning follow the method of classification, regression, prediction and gradient boosting. It uses certain patterns to foresee the values of the label on add-on data that are unlabeled. It helps the applications to predict future events on the grounds of historical data. For instance, insurance claims, credit card fraudulent, etc…
2. Unsupervised Learning: In this, algorithm needs to figure out what data point is to be displayed, the system will not display the correct answer. This method of Machine Learning is used against non-historical labelled data. The aim here is to find a proper structure within the data by exploring it. For instance, this works best on transactional data. In other words, Unsupervised learning helps in identifying a particular section of buyers who can be managed/treated similarly in a marketing campaigns as they possess same attributes.
3. Semi supervised Learning: This method uses both unlabeled and labeled data for training. This learning helps in classification, regression and prediction. Identifications of a person’s face on a web cam can be one of the examples of this method.
4. Reinforcement Learning: A method in which the learning discovers the algorithm through trails and errors which action yield with great results. Reinforcement learning has 3 primary elements: (a) the agent- the decision maker, (b) the environment (everything the agent interacts with, (c) the actions (everything agent does or can do). So, the goal here for the agent is to make strong decisions that yield best results with faster approach.

Machine learning has recently gained a lot of popularity, since its inception in the early 90s. It enables business transform technology. However, businesses that want to get into the Digital Transformation scene sometimes find Machine Learning beyond their reach because it is inherently complex in nature.

Today, lot of tech players offer Machine Learning platforms. Microsoft Azure’s suite of machine-learning offerings is fairly comprehensive, targeting everything from companies seeking simple, on-demand services through to those looking to train their own models using in-house data scientists.

Therefore, turn your challenges into competitive advantages by finding CloudMoyo as your perfect Machine Learning consulting partner that will help you in making strategies to reduce redundant processes, optimize operations and enhance the business efficiency. Talk to our ML Expert today!