In this Art and Science of Machine Learning course, we take a deeper at machine learning models. We begin by looking at the signals that models used to make updates during optimization, called loss functions, and contextualize these as one of potentially a large number of so-called hyperparameters–things that can be set to different values but don’t change while the model is training. We then introduce a procedure for determining the optimal values for hyperparameters and practice using it. We then dive deeper into neural networks, talk about some neural network-specific hyperparameters and build a neural network using the Estimator class. We then introduce the idea of an embedding, a lower-dimensional representation that ideally captures the semantic properties of the domain being modeled, and show how to create one in professionalr model. We finish by showing how to build a custom estimator.


Radiant Techlearning offers Art and Science of Machine Learningtraining program in Classroom & Virtual Instructor Led / online mode.


Duration: 15 Days


Learning Objective:

  • Machine Learning


Audience Profile:

  • Programmers, Data Engineers and Machine learning enthusiasts interested in its practical application.
  • Professionals who want to learn and apply Machine learning in their enterprise.

Course Content

Welcome to this course of art and science of machine learning. In this data science course,professionals will get to learn about the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize their ML models for the best performance. In this course,professional will also learn about the various knobs and levers involved in training a model. Professional will first manually adjust them to see their effects on model performance. After the familiarity with the knobs and levers, also known as hyperparameters, we will cover how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.



  • Course overview highlighting the key objectives and lessons. First, professional will learn about aspects of Machine learning that require some intuition, good judgment and experimentation. We call it the Art of ML. Professional will learn the many knobs and levers involved in training a model. Professional will manually adjust them to see their effects on model performance.


The Art of ML

  • In this course,professional will learn about The Art of Machine Learning. Through this course, we will review the aspects of machine learning that require intuition, judgment and experimentation to find the right balance and what’s good enough.


Hyperparameter Tuning

  • In this lessonprofessional will learn how to differentiate between parameters and hyperparameters. Then they willlearn how to think beyond it with smarter algorithms with the help of traditional grid search approach. Finally professional will learn how Cloud ML engine makes it so convenient to automate hyperparameter tuning.


A pinch of science

  • In this module, we will introduce the science accompanied with the art of machine learning. We’re first going to talk about how to perform regularization for sparsity so that we can have simpler, more concise models. Then we will talk about the logistic regression and learn how to determine performance.


The science of neural networks

  • In this lesson we will now have a closer look into the science, with special emphasis on neural networks.



  • In this lesson, professionals will learn how to manage sparse data, make machine learning models that use sparse data consume less memory and train faster by using embeddings. Embeddings are also a way to implement dimensionality reduction, and in that way, make models simpler and more generalizable.


Custom Estimator

  • In this lesson we will go beyond using canned estimators by writing a custom estimator. By writing a custom estimator, professional will be able to gain greater control over the model function itself.



  • Review the key concepts we covered in the Art and Science of ML course


Q: What is cross validation?


A: Cross-validation is essentially a technique which is used to assess how well a model performs on a new independent dataset. One of the example of cross-validation is when user split their data into two groups: training data and testing data, where user cab easily use the training data to establish the model and the testing data to test the model.


Q: What does NLP stand for?


A: Full form of NLP is Natural Language Process. Basically it is a branch of artificial intelligence which gives machine the ability to read and understand human languages.


Q: What is a decision tree?


A: Decision trees are a popular model which is used in operations research, strategic planning, and machine learning. Decision tress is expanded in the form of node, and the more nodes user have, the more accurate their decision tree will be (generally). The last nodes of the decision tree, where a decision is made, are called the leaves of the tree. Decision trees are intuitive and can be build easily but fall short when it comes to accuracy.


Q: What we will learn in this course?


A: In this course  students will learn the many knobs and levers involved in training a model. Initially students will learn about manually adjust them to see their effects on model performance. Once students get familiar with the knobs and levers, otherwise known as hyper parameters, students will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.


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.


Q: How the training will be delivered or conducted?


A: Radiant Techlearning offers customized training delivery solutions for individuals, teams and businesses depending upon what they require. Here is how we help each one through our diverse formats.

Dedicated Classroom Training program

Onsite: To meet the needs & expectations of our corporate clients all over the world, our expert will travel all the way to your location to deliver the training program at a premise of your choice & convenience.

Offsite: Our client and Individual professionals across the world travel all the way to India to attend our classroom training sessions. We assist them in services like accommodation, Airport pick & drop, daily cab & Visa assistance.

Public Batches: Corporates & Individual professionals across the world can nominate their employees or themselves in our classroom or online public batches. Our public batches would have limited number of participants to ensure individual attention. As the participants are from different background and companies, you learn from everyone’s experience.

On-the-Job Learning: Our team of consultant would help you to execute end-to-end project and simultaneously learn the technology.


Q: Does my employer can pay the fees of my courses?


A: Yes, your employer can pay your fees.


Q: Who will be the instructor of training program?


A: Radiant Techlearning has large pool of in-house certified trainers & consultants with strong background and working experience on the technology.

Radiant Techlearning offers more than 800+ courses and for each course Radiant have identified best-in-class instructors.

Radiant has highly intensive selection criteria for Technology Trainers & Consultants, who deliver you training programs. Our trainers & consultants undergo rigorous technical and behavioural interview and assessment process before they are on boarded in the company.

Our Technology experts / trainers & consultant carry deep dive knowledge in the technical subject & are certified from the OEM. Our faculty will provide you the knowledge of each course from fundamental level in an easy way and you are free to ask your doubts any time from your respective faculty.

Our trainers have patience and ability to explain difficult concepts in simplistic way with depth and width of knowledge.

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