What is Machine Learning? 

T he robot-depicted future of our current world depends on the human ability to deploy the wonders of artificial intelligence (AI). However, transforming mere machines into full-blown thinking devices is not as simple as it seems. A strong wave of AI can only be obtained with machine learning, helping machines process what humans do. So, what is machine learning?

How Does Machine Learning Work?

Machine learning is one of the major aspects of AI that enable the systems to improve and learn from the experience without excessive need for programming. It also focuses on designing computer programs that can easily access data and learn for themselves.

Similar to how the human brain works by processing things and understanding knowledge through the brain, machine learning relies on data input and training to understand domains, other entities, and the connection between them. The ML process starts with observing data patterns and making inferences based on the examples provided. The main aim of machine learning is to let computer systems learn human actions autonomously without intervention and adjust actions accordingly. 

ML and Its Importance

As a learning concept, machine learning has been around for the longest time. The term itself was coined by a computer scientist at IBM and pioneer of AI, Arthur Samuel. He designed a computer program, especially for checker players. The more the game was played, the more it learned from experience and made useful predictions on data.

Machine learning has proved to be immensely valuable for the learning sphere since it can solve problems at a scale that the human mind cannot do alone. With the ability to compute a massive amount of data behind a single task or multiple specific works, machines can be trained to identify patterns between input data and automated routine procedures. 

Machine Learning -The Types

Machine learning is complex and that is why it has been divided into two preliminary areas: supervised learning and unsupervised learning. Each one has a purpose defined as they yield results and utilize different forms of data. Around 70 percent of machine learning is supervised while the unsupervised area falls under the 10-20 percent rate. The remaining parts are covered by reinforcement learning. 

Surprised learning is the type of learning where data scientists define variables that they want the algorithm to assess to take out correlations. The input and output, both, for the algorithm, are specified. On the other hand, unsupervised learning is the type to involves unlabeled data. The algorithm is responsible for scanning through data sets -the predetermined predictions and recommendations-  for meaningful connections. 

Another approach to machine learning is a mixture of the two preceding types: semi-supervised learning. In this case, the data scientists tend to feed the training data but the model has the flexibility to explore further algorithms on its own and develop an independent understanding of the data set. 

Reinforcement learning is used to teach a machine how to finish a multi-step process for which there are rules defined. Data scientists, in the latter case, develop a specific algorithm to complete a task, basing it on positive and negative cues. However, for the most part, the algorithm works on its own to decide what steps to take next. 

Requirements for ML

For those looking forward to pursuing a career in machine learning, there are a few prerequisites that should be met in order to ensure success: 

• Basic knowledge of linear algebra
• Learning the specifics of calculus
• Knowing how to clean and structure raw data to the desired format
• Basic knowledge of programming languages such as JavaScript, Python, etc. 

Knowing how to work with different types of data is a must-have skill especially if you are serious about becoming a data analyst for AI.

Machine learning is, undoubtedly, a critical part of artificial intelligence. The bit of success, however, is contingent upon another approach to AI, countering its shortcomings, like the “black box” problem that usually occurs when machines are left unsupervised. That approach is both symbolic AI and a rule-based methodology used to process data by experts. The former approach used a knowledge graph -an open box- to define semantic relationships as well as concepts. 

So, now you know what is machine learning. Together, AI and ML formulate a hybrid model of artificial intelligence, an approach that aids AI in understanding language and data. With additional insight into what was learned, this thunderous approach is changing the way how data is used across the enterprise.