Taking a look at Machine Learning
Did you know that having the ability to think critically, evaluate, and solve complex problems is what makes a human being the most intelligent and advanced species on earth? This ability has led to research and investment in trying to understand how the human brain works. This curiosity is the major drive to making a machine that learns without being necessarily programmed.
Today we look at Machine learning(ML). We shall try to understand what ML is, What revolves between machine learning, and the need to understand ML concepts
What is Machine Learning?
We could say that ML is the science of making a machine function/ behave as a human being does. In simple terms Machine learning, is the concept that a machine can learn from a given data-set without being programmed.
Looking at a deeper meaning ML is a type of Artificial Intelligence that can extract patterns out of raw data by using algorithms or methods. Machine learning aims at making sense out of data in a similar manner that a human being does. ML is the science of making a computer learn on its own and perform or make decisions without necessarily being programmed.
The need for Machine learning.
You are probably asking why should a machine learn? or Why do should we even teach a machine? well, the answer to this is pretty simple Data. Data is the main reason why a machine should learn. In our previous articles we have looked at the era of data and, according to the 6th report by DOMO “Over 2.5 Quintilian bytes of data, are created every single day, and it’s only going to grow from there? By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.”
This amount of data created is numerous for humans to analyze tabulate and would take ages to get insights from the data. The second reason is to make decisions based on the data with much efficiency and scale.
In the recent past, we have seen organizations investing heavily in technologies like Artificial intelligence, Deep Learning, and Machine learning included why? These technologies all have one thing in common, they are data-driven Technologies. This means that the decisions and processes are dictated by the data.
Machine Learning approaches
ML approaches are divided into three categories. Reinforcement learning, Supervised Learning, and Unsupervised Learning.
- Reinforcement learning. A computer program interacts with a dynamic environment in which it must perform a particular task such as driving. As it navigates the problem, the program is given feedback that’s analogous to rewards, which it tries to maximize on.
- Supervised learning. In this category, a computer is presented with examples of inputs and their desired outputs, to learn a general rule that maps inputs to outputs.
- Unsupervised learning. In this category No labels are given to the learning algorithm, hence leaving it on its own to find out the structure in its input. This involves discovering hidden patterns in data.
When do we make a Machine learn?
Now that we have looked at the need and the approach to use in machine learning let us now look at the scenario that we need a machine to learn. We shall look at several cases where the need arises for a machine to learn.
- Dynamic situations. As the name suggests this is cases that are always changing due to various factors example, (The availability of infrastructure in an organization). In such scenarios and behaviors, we want the machine to make data-driven decisions.
- Low or lack of human expertise. This is another case that we will need a machine to make data-driven decisions. An example of a case where human expertise lacks is in the exploration into unknown territory such a deep-sea or Space. These two cases will be suitable for a machine as the risk is also minimal.
- Pattern Recognition. This is a wide scope that includes speech recognition, Image Recognition, and cognitive tasks. ML can be applied in this case the ML can translate and discover patterns that might be hidden from the human experts. The ML gives thorough feedback.
- Making predictions. ML algorithms are good when it comes to making predictions given that the model used is not biased. This is quality as well as a task that will need a machine to learn from the data and make data-driven decisions.
Applications of Machine learning
ML is a new and growing technology and with it comes numerous applications.
- Object Recognition
- Speech recognition
- Emotion Analysis
- Error detection
- Traffic predictions
- Medical diagnosis
- Self-driving cars
- Product recommendation
Drawbacks to Machine Learning
There are always two sides to a coin. Let us look at what will affect the quality of ML.
- The quality of data used this affects the work low quality or bias in the data will always lead to the wrong results.
- Deployment of machine learning models in real life is a challenging task.
- Dimensionality this is due to the numerous features in the data.
- Overfitting and Underfitting in machine learning models.
- Lack of specialized personnel given that ML is a new field there is a gap when it comes to getting the skilled persons in the field.
- Time-consuming tasks.
We are living in the age of data. We are creating more data than we are using therefore the main challenge we are having at this age is making sense of the data that we create. With the high computational power and storage in this era, Machine learning comes into play. ML algorithms will help us to make sense of the data that we are creating.
This decade will experience more and more machine intelligence and a dependency on technology will influence our everyday life.
“In the era where artificial intelligence and algorithms make more decisions in our lives and organizations, the time has come for people to tap into their intuition as an adjunct to today’s technical capabilities. Our inner wisdom can embed empirical data with humanity.”
― Abhishek Ratna. In this era, we are becoming dependent on the data that we are creating daily.
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