“A breakthrough in Machine Learning would be worth ten Microsofts”- Bill Gates
10 Microsofts! Can you imagine the power of machine learning?
- You ask Alexa to remind you of your medicine, ask Google to send messages for you.
- You use Google Maps for going somewhere you don’t know the way for.
- Face recognition system for unlocking your gadgets or preventing them from unauthorized users.
- “People You may know” by Facebook
- Customer support like chatbots when you book some products online
- You get refined results when you browse through search engines
- Email spam detection
- Recommendations of related products that you buy online or search for them
Above is the list of examples of machine learning that surrounds us in daily life that we don’t even realize. We come across these examples almost every hour of our day. When this is affecting our life significantly, how well it can affect your career?
While major tech companies are reorienting their workflow around Machine Learning and AI, the tech pros are finding opportunities to a lucrative career in the ever-evolving show. How about taking up acquiring a certification and making a career in machine learning.
Machine Learning Defined:
According to Arthur Samuel, “Machine Learning is a subset of Artificial Intelligence that gives computers the ability to learn without being explicitly programmed.”
It implies that you are not going to program or teach your computer with some specific set of rules. Instead, you feed the computer with the required data, and it learns from the patterns and other information. So, a machine learns with experience by performing some tasks given to it. Autonomous learning is what happens in machine learning.
Now it is clear that machine learning purely depends on the data it is served with, so you have to care about the quality of data you are putting into it. Also, the set of algorithms are equally important as underlying datasets.
Almost every time, machine learning and AI are used interchangeably, but actually ML is a subset of AI and is a way to achieve it. The reasons for ML being adopted widely are:
- Advancement in algorithms of machine learning
- Need for revenue streams
- The explosion of Big Data
- Storage capacity
- Development of machine which is very powerful and fast in computing
The tasks that were performed by humans only such as making decisions, judgments, gaming, etc, can now be performed by machines as well, and that too more efficiently and accurately. Machines can now analyze and go through patterns and remember the conclusions for use in the future.
Machine Learning: The Journey of Automation
When machine learning is considered, there are various algorithms to be managed between the source of data and the cloud, where it is stored. There is a centralized infrastructure in all machine learning cases today. The methodologies that were adopted earlier is depicted in the image below.
The evolution of machine learning has happened through Artificial Intelligence. Some tech terms that were fascinating a few decades ago are new normals today, such as IoT, Blockchain, wireless connections (4G/5G), quantum computing, and many more.
The very first step, as you can see in the image, as the Turing machine. From Turing Machine, it has now reached to very intelligent robots that can even diagnose your diseases. It is difficult to determine how far machine learning has come from its origin, but the results are visible. Recent years have seen high growth in the popularity of machine learning among developers and businesses.
In the mid-’90s the focus was on natural language, search, and retrieval of information. The tools that were used at that time were much simpler than those used today. Some of them were SVM (Support Vector Machine), logistic regression, PageRank, etc. With the help of these technologies, Google got to write its success stories such as GMail’s spam filters, and Google News.
Around 2005, the comeback of neural networks took place, which was a technology active in the 80s. Though they are advanced now and have made their way to natural language processing and translation of machines, they were not so advanced earlier. The most challenging task then was to set up a distributed neural network learning system.
Can you guess who did it successfully? It’s the tech giant Google.
It successfully managed to distribute neural networks and got the buzz.
Machine Learning Approach
Machine Learning is categorized into three types based on the way it learns:
- Supervised learning: the inputs that the machine gets are labeled and desired outputs. The machine is meant to learn a general rule that maps input to output.
- Unsupervised learning: the machine is fed with inputs without the required outputs. The machine is required to find out the structure in inputs.
- Reinforcement learning: the machine interacts with a dynamic environment and is required to perform a certain goal without a teacher or guidelines.
Currently, machines are learning and performing tasks and are making better decisions, judgments. Machines can now perform analysis and observe patterns and remember the results to be used in the future. It has now become challenging to get skilled professionals who can apply machine learning methodologies for creating innovative services or products.
Subsets of Machine Learning
First comes neural networks, which are considered crucial for training computers to think and learn by observing patterns. Second is NLP or Natural Language Processing, which provides machines with the ability to learn and human language and behavior. The third is at the cutting-edge of intelligent automation and is referred to as Deep Learning. It deploys machine learning tools for problem-solving.
Conclusion
By now, you have come across how machine learning evolution happened and what it is up to now. It is clear that with tech advancements, this domain is going to reach new heights.
A career in machine learning can be rewarding for you if you are a tech pro who is always ready for doing something innovative.
Taking up online training is the best move you can take for achieving a career of your dreams. Registration is all you need.