10 AI, Data Science, Machine Learning Terms You Need to Know in 2020

Original article can be found here (source): Artificial Intelligence on Medium

10 AI, Data Science, Machine Learning Terms You Need to Know in 2020

Planning to make a career in the field of data science, machine learning, or artificial intelligence, well, there is no better time than today. There are a number of AI certificate programs, ML certification, and data science certification programs that will help you get acquainted with complete information about these technologies and its implementation.

As a part of your data science training program, or AI certificate programs, or even in machine learning, it becomes essential that you must learn some of the common terminologies that you will come across during your certification in Artificial Intelligence, machine learning, and data science program.

Key terms that you must know as AI, ML, and data science expert:

  1. AutoML- Or Automated Machine Learning. It is the process of automating the process of applying the concepts of machine learning to real-world problems. As a part of machine learning for beginners, you will cover concepts like algorithm architecture structure, data pre-processing, feature engineering, algorithm selection under AutoML.
  2. Bayesian– If you are planning to pursue certification in ML, then it’s important to know about the statistical tools. One such tool is Bayesian, which also plays a key role in decision making. It belongs to the category of evidential probabilities; as a part of this method, we study the probability of a hypothesis. This methodology stipulates a prior probability.
  3. Data Engineer– They are responsible for optimizing and managing the storage and retrieval of an organization’s data. They set a road map on how to acquire the data and form a database for storing it. These engineers deal with cloud services to optimize data storage and form algorithms that will be helping drawing inferences from the data. A data engineer requires knowledge in SQL, computer science, and database design.
  4. Deepfakes- These are fake images, videos, or audio created using Deep Learning and Generative Adversarial Networks technology. This is a very real technology which gives a more apt result. Initially, it became popular where the popular celebrities’ photos were morphed in adult videos. This was misused in Sep 2019, when a company was scammed $243K wherein fake CEO’s voice was used.
  5. MLOps & AIOps– MLOps is the implementation of the basic practices for developing and deploying machine learning models via effective collaboration with data scientists and IT professionals. Extending the above term, AIOps revolves around leveraging AI into the operations of a company. It incorporates machine learning technologies that derive useful information from IT systems. This methodology involves the combination of human intelligence with the AI algorithm, which enables IT teams to make a more formed decision.
  6. Clickstream analytics- If you are willing to become a data science expert, then knowing about certain terminology is important. Clickstream analytics is the analysis of the way humans interact with machines, like recording and then analysis of where a mouse is clicked on a screen. This is just one of the examples of clickstream analysis, it basically is a measure of manual operation of machinery.
  7. Natural language processing– It is important for learning Artificial Intelligence certification programs. Here the AI is trained to comprehend human communication. This forms the basis for various services like chatbots and translational services. Siri and Alexa are an example of the same.
  8. Decision Trees- As a data science expert, you would need to know about decision trees. It is used by computers to analyze and classify the information. The working is simple; a series of questions about a particular data is fed into the system, and then it is channeled as different branches into different outputs.
  9. Transfer learning- As a part of AI certificate programs, you need to learn about different processes of AI and how the system works. One such concept is transfer learning. The simplest way to explain this is that when AI has learned about a certain program, it will keep on building its knowledge base on it, even if you haven’t asked the machine to learn about the same. For example, if AI can determine, an image is a cat or not. It will continue to learn the same. Let’s say you take an AI, and it spends a week identifying shoes; after that, it can return back to its work on cats with great improvements and accuracy.
  10. Reinforcement learning- It is a type of machine learning algorithm where the agency decides what would be the right course of action based on previous study. It learns optimal actions and works on trial and error.

These were some of the terms that you must know as data science, machine learning, and AI expert.

What’s next?

Data science, AI, and Machine Learning are wider terms, and it involves wider terminologies and uses cases. To have a complete understanding of how each of these technologies work, then you need to go for data science training, ml certification, and certification in artificial intelligence. Global Tech Council offers industry standards certification in artificial intelligence, ML, and Data Science. Once you have completed the certification program, you become eligible to implement the concepts in the practical field.