How to Boost Your Career in Big Data and Data Analytics?

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• Session 1

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• 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.

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

• 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.
• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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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.

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