In this Launching into Machine Learning course professionals will get foundational ML knowledge so that they can understand the terminology that we use throughout the specialization. Professionals will also learn the knowledge to bootstrap professional’s own ML models and practical tips and tricks from ML experts here at Google and walk away with the code.


Radiant Techlearning offers Launching into Machine Learning training program in Classroom & Virtual Instructor Led / online mode.


Duration: 5 Days


Learning Objectives:

  • Identify why deep learning is currently popular
  • Optimize and evaluate models using loss functions and performance metrics
  • Mitigate common problems that arise in machine learning
  • Create repeatable and scalable training, evaluation, and test datasets


Topics covered

  • Machine Learning


Audience Profile:

  • Data Engineers and programmers interested in learning how to apply machine learning in practice.
  • Professionals interested in learning how to leverage machine learning in their enterprise.

Course Content

In this course, we start from the history of machine learning; and discuss why neural networks today perform so well in a variety of data science problems. Then we will discuss how tofind a good solution using gradient descent and set up a supervised learning problem. This involves creation of datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.



  • In this course professionals will get foundational ML knowledge so that professionals understand the terminology that we use throughout the specialization. Professionals will also learn practical tips and tricks from ML experts here at Google and walk away with the code and the knowledge to bootstrap professional’s own ML models.


Practical ML

  • In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that professionals can accelerate professional’s growth as an ML practitioner.



  • In this module we will walk professionals through how to optimize professional’s ML models.


Generalization and Sampling

  • Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization — simply because a model has a loss metric of 0 for professional’s training dataset does not mean it will be efficient in the real world new data.


Q: How do we apply machine learning to hardware?


A: User have to make Machine learning algorithms in System Verilog (it is a Hardware development Language) and then user have to program it onto an FPGA to apply Machine Learning to hardware.


Q: Which machine learning algorithm is known as the lazy learner and why it is called so?


A: Basically KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner as it doesn’t learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead.


Q: What is the difference between entropy and Information gain?


  • A: Entropy is an indicator of how messy your data is. It reduces as you reach closer to the leaf node.
  • The Information achieved is based on the decrease in entropy after a dataset is split on an attribute. It keeps on increasing as the user reach closer to the leaf node.


Q: What is A/B testing?


  • A: A/B is Statistical hypothesis testing for randomized experiment with two variables A and B. Generally it is used to compare two models that use different predictor variables in order to check which variable fits best for a given sample of data.
  • Consider a scenario where user have to created two models (using different predictor variables) which can be used to recommend products for an e-commerce platform.
  • A/B Testing can be used to compare these two models to check which one best recommends products to a customer.


Q: What kind of projects are included as a part of training?


A: The learners will be enthralled as we engage them the real world and industry Oriented projects during the training program. These projects will improve your skills and knowledge and you will gain better experience. These real time projects, they will help you a lot in your future tasks and assignments.


Q: How is the Radiant Techlearning verified certificate awarded?


A: Radiant awards course completion certificate to all the participants who have completed the training program which includes various real time projects, assignments, quizzes and some other tasks.  Once the course is done you would be assigned with a project which you would have to submit in 2 weeks’ time.

Radiant Techlearning experts will be evaluating the project on various parameter. To be eligible for the verified certificate you would have to score more than 60% marks.

Only after completion of these criteria you would be awarded with Radiant verified certificate and which the participants can use for their future job purpose.

Participants will be awarded with grades according to the following criteria:

  • 90% – 100% – AAA+
  • 80% – 90% – AA+
  • 70% – 80% – A+
  • 60% – 70% – A


Q: Is there any job assistant guarantee?


A: No. These training programs are helpful to improve your skills & knowledge on the technology which would help you to land in your dream job by learning them.

Our training program will maximize your ability and chances of getting a successful job. You have to select job according to your convenience. Your performance in the training program and interview is crucial for getting good job.


Q: What if I/we have doubts after attending your training program?


A: Radiant team of experts would be available on the email to answer your technical queries, even after the training program.

We also conduct a 3 – 4 hours online session after 2 weeks of the training program, to respond on your queries & project assigned to you.

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