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Professional Machine Learning Engineer

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped?

Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets?

    NOTE : Ready to boost your cloud career? With Machine Learning with TensorFlow Certificate on Agilitics, you can strengthen your cloud knowledge, earn a digital certificate, and start preparing for an industry-recognized Google Cloud certification.

    1:1 Coaching

    24*7 Support

    CloudLabs

    High Success Rate

    Globally Renowned Trainer

    Real-time code analysis and feedback

    Course Description

    Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

    Learning Objectives

    This course teaches participants the following skills:

    • Frame a business use case as a machine learning problem
    • Create machine learning datasets that are capable of achieving generalization
    • Implement machine learning models using TensorFlow
    • Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
    • Build and operationalize distributed TensorFlow models
    • Represent and transform features

    Schedule

    Enroll your course with
    $374.50
    20-24 Nov, 2021
    09:00AM – 05:00PM
    Trainer
    Ajit Kumar / Puneet Vats
    Singapore
    Weekend
    Enroll your course with
    $374.50
    11-15 Dec, 2021
    09:00AM – 05:00PM
    Trainer
    Ajit Kumar / Puneet Vats
    Singapore
    Weekend

    Certification Curriculum

    Module 1
    How Google Does Machine Learning
    • What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important not to skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them. Course Objectives:
      Develop a data strategy around machine learning
      Examine use cases that are then reimagined through an ML lens
      Recognize biases that ML can amplify
      Leverage Google Cloud Platform tools and environment to do ML
      Learn from Google’s experience to avoid common pitfalls
      Carry out data science tasks in online collaborative notebooks
      Invoke pre-trained ML models from Cloud Datalab
    Module 2
    Launching into Machine Learning
    • Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course 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
    Module 3
    Intro to TensorFlow
    • We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives:
      Create machine learning models in TensorFlow
      Use the TensorFlow libraries to solve numerical problems
      Troubleshoot and debug common TensorFlow code pitfalls
      Use tf.keras and tf_estimator to create, train, and evaluate ML models
      Train, deploy, and productionalize ML models at scale with Cloud ML Engine
    Module 4
    Feature Engineering
    • A key component of building effective machine learning models is to convert raw data to features in a way that allows ML to learn important characteristics from the data. We discuss how to represent features and code this up in TensorFlow. Human insight can be brought to bear in machine learning problems through the use of custom feature transformations. In this module, we talk about common types of transformations and how to implement them at scale.
      Turn raw data into feature vectors
      Preprocess and create new feature pipelines with Cloud Dataflow
      Create and implement feature crosses and assess their impact
      Write TensorFlow Transform code for feature engineering
    Module 5
    The Art and Science of ML
    • Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of ML problems. We discuss regularization, dealing with sparsity, multi-class neural networks, reusable embeddings, and many other essential concepts and principles.
      Optimize model performance with hyperparameter tuning
      Experiment with neural networks and fine-tune performance
      Enhance ML model features with embedding layers
      Create reusable custom model code with the Custom Estimator

    Prerequisites

    To get the most out of this specialization, participants should have:

    • Experience coding in Python
    • Knowledge of basic statistics
    • Knowledge of SQL and cloud computing

    Download Brochure

    Join Machine Learning with TensorFlow on Google Cloud Platform and gain the knowledge and skills needed to land an entry-level job in Machine Learning. A Path to In-Demand Jobs.

    Download Brochure

    Certification Assessment

    When you complete the course in the program, you’ll earn a Certificate to share with your professional network as well as unlock access to career support resources to help you kickstart your new career. Many Professional Certificates have hiring partners that recognize the Professional Certificate credential and others can help prepare you for a certification exam. You can find more information on individual Professional Certificate pages where it applies.

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    Training FAQ's

    Is this training conducted as an online/ virtual course?

    As a response to the COVID-19, we have moved our classes online. Our Live Virtual format delivers the same benefits as our face-to-face training: expert instruction, hands-on labs and exercises, peer-to-peer collaboration, and high-quality instructional material.

    If I cancel the enrollment, will I get a refund?

    Cancellation requests received within 24 hours of registration would be offered a full refund (minus payment gateway charges), please reach out to our support team through drop a refund request to enquiry@agilitics.sg. Visit our page for more details about Cancellation & Refund Policy.

    What Payment Options Are Available?

    The process of enrolling for this classroom training is simple. The payment can be made through different options by using a debit/credit card which includes MasterCard, Visa Card, American Express or through PayPal. Acknowledgment will be issued automatically via email to the candidates once payment is done.

    What are the modes of payment available for payment of accreditation fees?

    Payment can be made via credit card, debit card, UPI, and internet banking.

    Have more doubts?

    Please send in an email to enquiry@agilitics.sg, and we will answer any queries you may have!

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