The concept of Machine Learning derives from constant development of smart technologies, happening right in front of our eyes. But how does it actually work and when did it start?

These are only a few questions we will answer in our machine learning infographic. 

Machine Learning in a nutshell

Technology today is evolving today at such a pace that predictions of trends and innovations can be out of date even before the studies are published in the form of an article or research papers. The very evolution of technology enables and accelerates the changes and subsequently causes the rapid progress towards future technologies that we see in today’s technological world.  

Technology-based careers do not often change at the same pace, but the modern world IT professional is aware that his or her role may not stay the same. The IT professional in 2020 is not limited to traditional software development but rather is always learning.  

Some of the major players in the technologically evolving world today include Artificial Intelligence (AI), Machine Learning, Robotic Process Automation (RPA), Blockchain, Edge Computing, Virtual Reality and Augmented Reality, Internet of Things, Big Data, Business Intelligence, etc. 

Machine Learning (ML) is without a doubt one of the most popular technologies today. Machine learning has begun to reshape how we live today, and it is important to understand what it is and how it influences the lives of humans.  

What exactly is Machine Learning?

The formal and traditional definition of ML can be stated as “A computer program learns from experience while doing some class of task if its performance measure improves at that task with experience”. This definition adheres to Alan Turing’s hypothesis in his 1950 paper “Computing Machinery and Intelligence”, in which he asked, “Can a machine think rationally?” In simpler terms, we can say that Machine learning (ML) scrutinizes the mathematical patterns of a task with the help of various computer algorithms and uses them to progressively improve the performance on a specific task.  

Because of the technological advances today, ML of today is largely different from the ML in the past. Machine learning originated from simple pattern recognition and on the simple ground basis that computers can learn without any human intervention. The most vital aspect about ML algorithms is that their independent adaptability as the model is exposed to a newer dataset. The model learns from the previous computational outputs to get reliable decisions and results. In that sense, ML is not a new field of science or engineering. It is a technology that has gained fresh momentum.  

ML today can be seen everywhere, regardless of whether we notice it or not. Some of the major areas where ML is enriching human lives are: 

• Online products recommendations – ML allows retailers to personalize recommendations for customers based on previous purchases and search patterns. A great example of this is the recently rolled out Interactive Recommender by Steam, an online store for video games. 
• Spam Filters – The email inbox while seeming like an unlikely place for applying ML, uses the technology for one of its most important features i.e. spam filtering. 
• Facebook’s news feed – The News Feed uses machine learning to personalize each user’s feed. If a user frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed.  

Machine Learning vs Artificial Intelligence 

In the simplest terms, while artificial intelligence (AI) is the comprehensive science of enabling machines to display human behaviour and their abilities, ML is a subset of AI that trains a machine on how to learn.  

Machine learning is technically a branch of AI, but it is far more specific than the overall concept. The concept of Generalized AI (general–purpose AI) led to the genesis and development of machine learning. The basis of Machine learning is the notion that we can build machines to process data and learn on their own, without our constant supervision. 

Both AI and ML have extensive and valuable business applications. These systems together have many applications to offer. Machine learning, however, has gotten a much larger momentum in the recent years and subsequently, many companies have focused on that source for solutions.  

Deep Learning vs Machine Learning  

The concept of deep learning like machine learning is not new. It has been around for some time. The formal definition of deep learning states that – “Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data.”  

In simpler terms, deep learning is similar to machine learning but at a greater level of specificity. While traditional ML algorithms perform fairly on a small dataset, deep learning algorithms tend to yield in such scenarios. Deep learning algorithms also usually require high-end machines, contrary to machine learning algorithms that perform well even on low-end machines. Deep learning algorithms also usually require a longer time to run than machine learning algorithms. 

Machine Learning Algorithms

Broadly speaking, there are three main categories of ML algorithms –  

• Supervised learning – These types of algorithms consist of a variable that is known as the outcome (or dependent variable) which needs to be predicted based on a previously known set of predictors (independent variables). Using these set of variables, a function is generated that maps inputs to desired outputs. The training process is carried on until the model achieves the level of accuracy in predictions expected on the training data. Examples of this type of learning algorithms are Regression, Decision Trees, Random Forest, etc. 
• Unsupervised learning – In this algorithm, the target estimate or variable is not known. It is used to cluster the population in various groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of this type of algorithms are Apriori Algorithm and K-means.
• Reinforcement learning – In this approach, the model continuously tries to find the optimal outcome using trial and error. The model learns from previous experiences and tries to capture and use the best possible knowledge to reach the most accurate decision. Example of reinforcement learning is the Markov Decision Process.   

Applications of Machine Learning and some Machine Learning start-ups

Machine learning is all around us. From social media to healthcare, the effects and benefits of ML can be felt in all major sectors. Some of the major applications of ML today are: 

• Virtual Personal Assistants 
• Social Media Services 
• Email Spam and Malware Filtering  
• Search Engine Result Refining 
• Online Fraud Detection 
• Artificial Intelligence Healthcare Sector 
• Product Recommendations and Personalization 

Machine learning in fintech startups is also a large growing trend. Similarly, ML in healthcare startups is flourishing.  

The future of Machine Learning   

In 2020 Machine Learning has the ability to process and make sense of vast quantities of data and collect metrics. ML also has the ability to apply these metrics to develop more complex algorithms that will be able to perform increasingly complex tasks. Real-time intelligence for complex decision-making is essential to businesses today. The forecasting needed for such decisions based on the performance of the markets in future years will best be accomplished using ML over human force.  

As technology advances even further, more businesses will embrace the AI and ML revolution. Competition to make the best use of the enormous data available and machine learning is bound to tighten. Businesses with strong ML applications will have a major competitive advantage over rivals.  

Conclusion

ML has taken an essential step in a learnable probabilistic model. The recent advancements in computational power and data availability has significantly helped in improving the effectiveness of ML algorithms. Primarily ML has contributed greatly in improving automation and solving complex problems that earlier seemed impossible to overcome.   

With more and more businesses starting to adopt machine learning and integrating these algorithms into their model, machine learning shows no signs to stop any time soon. Artificial Intelligence software development is also a rapidly growing field and developers of the modern world need to keep up with the trends by updating their knowledge and skills constantly.