10 Terms You Need To Know About AI, ML and Data Science in 2020

Original article was published on Artificial Intelligence on Medium


10 Terms You Need To Know About AI, ML and Data Science in 2020

10 Terms You Need To Know About AI, ML and Data Science in 2020

If you are planning to make a career in the field of data science, machine learning, or artificial intelligence, well, this is the better time to get into this. There are so many AI certification programs, ML certification, and data science certification programs that will help you get more familiar with complete knowledge of these technologies and its practical implementation.

As a part of your Artificial Intelligence, Machine Learning or Data Science certification program, it becomes needed that you must learn some of the common terminologies that you will come across during your training in Artificial Intelligence, machine learning, and data science certification program.

Main terms that you must know to be an expert in ML, AI, or in Data Science:

  1. AutoML- Or Automated Machine Learning. It is the process of automating the process by applying the concepts of machine learning to real-world problems. If we look about 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. Data Science Engineers deal with cloud services to optimize data and maintain safe and form algorithms that will be helping build inferences from the data. A Good data engineer requires good knowledge in SQL, computer science, and designing of the database.

4. Deepfakes- These are fake images, videos, or audio created using Deep Learning and Generative Adversarial Networks technology. 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 all about implementing the basic practices for the development and deployment of 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 includes machine learning technologies that derive useful information from IT systems. This methodology involves the good combination of human intelligence with the algorithm of AI, which enables Information Technology, teams, to make a more valuable and accurate decision.

6. Clickstream analytics- If you are going to become a data science expert, then know about certain terms is important. Clickstream analytics is the analysis of the way to analyze how machines are talking to a human, like recording and then analyze where the click done on 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 very 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 must learn about decision trees algorithm. It is used by computers to analyze the working and steps of the data process. 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 Artificial Intelligence certification programs, you need to learn about different processes of Artificial Intelligence and how the processing 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 Artificial Intelligence can determine, an image is a dog 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 dogs 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 the previous study. It learns optimal actions and work processes errors.

These were some of the terms that you must learn to be good 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 all understanding of how each of these technologies works practically, then you essentially go for data science training, ml certification, and artificial intelligence training. Global Tech Council offers you the best industry standards certification and training 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.