Managing Machine Learning Projects with Google Cloud

Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.

This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for the business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for the business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.

Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common predispositions and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.

This module explores five general themes for discovering ML use cases within the day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.

When you thoroughly understand the fundamentals of machine learning and considerations within each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes five critical reviews for successfully managing an ML project end-to-end:

• Identifying the business value
• Developing a data strategy
• Establishing data governance
• Building successful ML teams
• Enabling a culture of innovation

You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.

This module summarizes the key points covered in each module in the course.

A: Three stages of building a model are:-

Model Building: During this phase, the user must select an appropriate algorithm for the Model and train it following the specified requirements. Testing: Utilizing test data and the Model, the user must now determine whether the Model is accurate. At this point, the user must test the finished Model, make the necessary adjustments, and use it for live projects.

Here, it's vital to remember that the Model must be periodically tested to ensure it operates correctly. To ensure that it is current, it should be changed.

A: It is said to overfit when a model learns the training set too well and interprets random oscillations in the training data as ideas. These affect the Model's exceptional capacity to generalize but don't apply to new data.

When a model is given the training data, it shows 100 % accuracy—technically, a slight loss. But, when users use the test data, there may be a chance of an error and low efficiency. This condition is known as Overfitting.

There are various ways of avoiding Overfitting, such as:

• Regularization, which involves a cost term for the features involved with the objective function
• I am making a simple model. With a lesser amount of variables and parameters, the variance can be reduced.
• Cross-validation methods such as k-folds can also be used.
• If some model parameters are likely to cause Overfitting, techniques like LASSO can be used to penalize these parameters.

A: Some of the business benefits of machine learning are listed below:-

• Generally, it simplifies Product Marketing and Assists in Accurate Sales Forecasts.
• Facilitates Accurate Medical Predictions and Diagnoses.
• Simplifies Time-Intensive Documentation in Data Entry.
• Improves Precision of Models and financial rules.
• Easy Spam Detection.

A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are onboarded in the company.

Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.

Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.

Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.

Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.

To ensure quality learning, we provide support sessions even after the training program.

A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs. 

A: We recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.

A: Radiant Techlearning has a data center containing a Virtual Training environment for participants' hand-on-practice. 

Participants can easily access these labs over Cloud with the help of a remote desktop connection. 

Radiant virtual labs allow you to learn from anywhere and in any time zone. 

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