Online Course – Certified Professional Internship in Machine Learning with TensorFlow from Google Cloud Institute

Explore the world of AM with Google Cloud. Explore hands-on experiments across all processes, and expand your knowledge of the most advanced technologies.

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קורס אונליין - התמחות מקצועית מוסמכת בלמידת מכונה עם TensorFlow של Google Cloud Institute

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Communication skills
  • Critical thinking
  • Troubleshooting
  • Teamwork
  • Time management
  • leadership
  • Presentation skills
  • Creative thinking
  • Coping with stress
  • Technological skills

What you will learn in the course

Courses for which the course is suitable

  • Data Scientist
  • Machine Learning Engineer
  • AI Researcher
  • Data Analyst
  • Machine Learning Developer
  • Deep Learning Specialist
  • Big Data Engineer
  • Business Intelligence Developer
  • Quantitative Analyst
  • Software Engineer with Machine Learning focus

Expertise – Course Series of 5 Courses

What is machine learning and what problems can it solve?

  • The five steps to converting a potential machine learning use case
  • It is important not to ignore these steps.

Why are neural networks so in demand today?

  • Problem definition in supervised learning
  • Finding an optimal solution with slope descent
  • Creating data sets well

Using TensorFlow

  • Maintaining distributed machine learning models with scale
  • Performing horizontal scaling for model training
  • Offering high-quality forecasts

Convert raw data to attributes

  • Understanding important data features in machine learning
  • Offering human insight to support the problem

Parameter combination

  • Generating accurate and comprehensive models
  • Introducing theory to solve specific problems in machine learning

Hands-on labs with Google Cloud Platform

  • All stages of machine learning
  • Preparing a machine learning-focused strategy
  • Training, optimization, and model generation
Terms of Service

By signing up for this specialization, you agree to the Qwiklabs Terms of Service as detailed in the FAQ section. View the Terms of Service here: https://qwiklabs.com/terms_of_service

Hands-on Learning Project

This specialization offers hands-on labs using the Qwiklabs platform. With this hands-on training, you will be able to apply everything you learned in the video lectures.

  • Projects will include topics such as Google Cloud Platform products
  • Practical experience with the concepts discussed in the modules

Details of the courses that make up the specialization

How Google is doing machine learning in Portuguese language courses

Course 1

  • 19 hours
  • 4.8 (73 ratings)
Course Details
What you’ll learn
  • How the Vertex AI platform is used to create, train, and deploy machine learning models using AutoML without writing a single line of code.
  • Describe the best practices for implementing machine learning on Google Cloud.
  • Use Google Cloud Platform tools and environment to work with ML.
  • To detail the recommended principles of responsible artificial intelligence.

Course 2

  • 11 hours
  • 4.5 (31 ratings)
Course Details
What you’ll learn
  • Describe how to improve data quality and perform exploratory analyses from it.
  • Create and train AutoML models using Vertex AI and BigQuery ML.
  • Optimize and evaluate models using loss functions and performance metrics.
  • Create repeatable and scalable data sets for training, evaluation, and testing.
Skills you will acquire
  • Category: 1.96
  • Category: Values
  • Category: A-priori and A-posteriori
  • Category: Critical Value

Course 3

  • 18 hours
  • 4.6 (23 ratings)
Course Details
What you’ll learn
  • The goal of the course is to take advantage of the flexibility and ease of use of TensorFlow 2.x and Keras to create, train, and deploy machine learning models.
  • You will learn about the TensorFlow 2.x API hierarchy and become familiar with the main components of TensorFlow through hands-on exercises.
  • We will see how to work with data sets and attribute columns.
  • You will learn to design and create a TensorFlow 2.x data input pipeline.
  • You will gain hands-on experience with loading CSV data, Numpy arrays, text data, and images using tf.Data.Dataset and how to create numeric, categorical, category, and hash attribute columns.
  • We will introduce the Keras Sequential and Keras Functional APIs to show how to create deep learning models.
  • We will discuss activation, loss, and optimization functions.
  • In Jupyter hands-on labs, you can create machine learning models with basic linear regression and basic and advanced logistic regression.
  • You will learn to train, deploy, and create machine learning models at scale with the cloud’s AI Platform.

Course 4

  • 8 hours
  • 4.5 (15 ratings)
Course Details
What you’ll learn
  • Describe the Vertex AI Feature Store and compare the key aspects required to achieve a good feature.
  • Used in feature measurement in BigQuery ML, Keras, and TensorFlow.
  • Analyze how to perform preprocessing and use features with Dataflow and Dataprep.
  • Implement tf.Transform.

Course 5

  • 18 hours
  • 4.7 (15 ratings)
Course Details
What you’ll learn
  • This is the course “The Art and Science of Machine Learning”. The course includes six modules. We will talk about essential skills of intuition, reasoning, and experimentation in ML to adapt and optimize models and improve performance.
  • You will learn to train models using regularization techniques and become familiar with the effects of hyperparameters, such as dataset size and learning rate, on model performance.
  • We will also discuss some of the most common algorithms for optimizing a model and show how to specify an optimization method in TensorFlow code.

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