Professional certification in Google

PROFESSIONAL MACHINE LEARNING ENGINEER TRAINING

Our IT training courses are developed with industry-standards and career-focused technologies.


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Award:
Certification
Awarding Body:
Google
Duration & Study Mode:
Full time, Part-time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

In this course, you will learn how to develop ML (Machine Learning) models to resolve business issues with the help of Google Cloud technologies which are widely used to design ML models. You will also learn the process of framing ML problems while designing ML models. You will also be able to design and develop solutions for ML Architect, as well. You will learn about how to process and prepare data to configure, enhance and troubleshoot ML solutions. Through the subject matter of this course, you will learn about the techniques of processing data, including the deployment of Simple Linear, Multiple Linear, and Polynomial Regression. You will be able to configure the process of clustering data. Here, you will also earn adequate knowledge and skills to start working on NLP (Natural Language Processing) and Deep Learning. You will also learn about the process of model boosting and model selection.

Prerequisites

  • There is no particular prerequisite mentioned to get enrolled in this course.
  • It is ideal if you have prior experience of working with ML, but not required.
  • It is expected that you are familiar with the ML Data model and ML Architect.
  • It would be better if you have the fundamental knowledge about Data Clustering and NLP (Natural Language Processing)
  • It is also expected that you are familiar with supervised learning and unsupervised learning.

What will you gain after this course

  • You can develop ML models to train the ML architecture with a particular set of data.
  • You will learn the details about Data Clustering
  • You will be able to work on ML solutions
  • You will earn adequate knowledge and skills on Deep Learning and NLP
  • You will also be able to develop ML solutions using different types of the data regression model

Jobs you can get
with a Professional Machine Learning Engineer

  • Machine learning Data Associate
  • Machine Learning – Computer Vision
  • Machine Learning – Full Stack Developer
  • Machine Learning Engineer
  • Manager of Applied Machine Learning

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • IT professionals who already have some experience working with ML
  • IT professionals who would like to accelerate their career as an ML professional
  • ML professionals who are looking forward to learning and know more about ML technology
  • Anyone willing to pursue their career as a Machine Learning Engineer.


Group-Icon2
Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

  • Translating business challenges into ML use cases.
  • Defining ML problems
  • Defining business success criteria
  • Identifying risks to feasibility of ML solutions.
  • Designing reliable, scalable, and highly available ML solutions.
  • Choosing appropriate Google Cloud hardware components.
  • Designing architecture that complies with security concerns across sectors/industries.
  • Exploring data (EDA)
  • Building data pipelines
  • Creating input features (feature engineering)
  • Building models
  • Training models
  • Testing models
  • Scaling model training and serving
  • Designing and implementing training pipelines.
  • Implementing serving pipelines.
  • Tracking and auditing metadata.
  • Monitoring and troubleshooting ML solutions.
  • Performance and business quality of ML model predictions
  • Logging strategies
  • Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)
  • Understanding Google Cloud permissions model
  • Identification of appropriate retraining policy
  • Common training and serving errors (TensorFlow)
  • ML model failure and resulting biases
  • Tuning performance of ML solutions for training and serving in production.
  • Optimization and simplification of input pipeline for training
  • Simplification techniques

When would you like to start?

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Choosing a training center for any IT course is not an easy task. In order to earn extra credential to your IT career that will add values, you can undoubtedly choose London IT Career not only for the proven reputation but also for the opportunity to get highly skilled and experienced trainers. You will be highly motivated to accelerate you IT career in collaboration with London IT Training once you get the references from many other successful IT professionals working at well recognized and reputed IT companies, who had successfully completed IT courses from here.
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