Are Artificial Intelligence (AI) and Machine Learning(ML) overhyped? Or are they truly two of the exploding branches of Data Science? Is there programming involved in these two technologies? Can Artificial intelligence training online help aspiring candidates become AI professionals? Let’s converse more about these two spheres and develop a view on who can become AI or ML experts. But first thing first. The tech gurus have been explicit about the prerequisites for taking up AI as a career path. And prior programming experience is definitely not on the list. Does this mean AI and ML are away from traditional programming?
That said, H2K Infosys offers the best artificial intelligence certification online to candidates who aspire to make a mark in the AI stream.
What is Artificial Intelligence?
Artificial Intelligence is an umbrella term coined way back in 1956, however, now gaining the deserved precedence. The immense data volumes combined with the development of the latest algorithms and computing power work together to make AI possible in today’s world.
Artificial Intelligence is the force that compels the machines to perform human-like functions and thinking. Computers playing chess with humans, Siri app, recommendation system on Netflix, Amazon, Google Maps, self-driven cars are perfect examples of AI applications.
The AI essentially helps in finding the hidden patterns in a large amount of data. These patterns are fed to machines and trained to accomplish certain tasks.
Applications of AI
The demand for Artificial Intelligence is increasing day by day especially being applied in chatbots, risk notifications, and medical research. AI is being used in various domains:
- Healthcare – personal healthcare assistants are capable of acting as life coaches reminding the user of activities such as taking pills, exercise, eat healthy food, etc.
- Manufacturing – AI working in tandem with IoT can forecast the demand and load of products in the factory.
- Banking – AI plays a critical role in the banking and financial domain by improving the effectiveness, speed, precision. This helps in the containment of fraudulent transactions, quicker calculations of credit score, etc.
- Retail – Artificial Intelligence helps in developing recommendation systems based on the past transaction history of the customers.
What is Machine Learning?
Machine Learning is a key component of Artificial Intelligence which gives the computers or machines to learn through repetitive learning without being programmed via coding. The current ML applications can be broadly classified into three categories.
- MI application at tech giants like Google or IBM
- When a MI learner can limited skills can develop applications
- When a layman can access the ML applications
We are currently between the 2nd and 3rd levels.
However, the Machine Learning tasks can be classified into three methods
- Supervised Learning – where the learning happens under the influence of the teacher. The data is present and a pattern is identified. The prediction of the unknown is made based on the input-output pairs.
- Unsupervised Learning – This happens without the support of a teacher. The data is present, however, the goal is to find hidden patterns.
- Reinforcement Learning is where a machine is left in a dynamic environment. The machine itself learns and unlearns by taking feedback and uses it to navigate in that space.
AI/ML Vs Traditional Programming
Traditional programming is only a part of what AI or ML is all about. So, we can say that AI begins where traditional programming ends. AI or ML techniques are a supplement to traditional coding.
A programmer ideally builds an algorithm, implements the code, and inputs the parameters to obtain results.
For example, when a software programmer tries to predict the exchange rate, they need to feed previous day’s rates, incumbent economic changes in the country, etc and predict the new rate.
Now, let’s see the Machine Learning approach…
So, instead of first developing an algorithm and then pass the parameters, they use the historical data to build a semi-automatic model. A model is said to be trained when the historical data is able to produce satisfactory results with the model. Then, new data is fed as input to predict new results.
So, ML/ AI experts involve a part of coding, however, the emphasis is on ML algorithms, the ability to use different libraries such as NumPy, Pandas, SciPy, and expertise in creating distributed applications using Hadoop, etc.
If playing with data and Machine Learning/Artificial Intelligence streams excite you, contact us at www.h2kinfosys.com for pursuing artificial intelligence training online.