Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Participants will be introduced to Google Cloud's big-data capabilities in this course. Through a combination of demonstrations and hands-on labs, participants will get a broad overview of Google Cloud and a more in-depth look at data processing capabilities and machine intelligence capabilities. This course shows the power, flexibility, and ease that big data solutions can provide on Google Cloud.

Machine learning. It is essential for your team and your boss, and a career-enhancing skill. LinkedIn ranks it as one of the "Skills Companies Most Need" in the United States and the most sought-after emerging job.

To participate in the deployment of machine learning and predictive analysis, you must have a good understanding of these concepts. You must be able understand machine learning and predictive analytics, even if you are a business leader. You need to understand how predictive models are used to make decisions, regardless of whether you're an executive, decision maker, or operational manager.

It is worthwhile to look under the hood. Machine learning is an intriguing science that can be intuitively interpreted. Machine learning is having a rapid growing impact on the world. It is crucial to understand its predictive power and scientific implications.

This course will cover machine learning. This course will cover the basics of machine learning and how insights can be obtained from data. This course also covers how to ensure that these insights are reliable. It also demonstrates the effectiveness of predictive models. This can all be done using very basic math. These are vital information every business professional must know.

This course covers more than just machine learning techniques. It also covers advanced, cutting-edge techniques and how to avoid common pitfalls. These topics are well-covered, but the course can be used by both technical and non-technical learners.

This course will show you the difference between what works and what doesn't.

- Predictive modeling algorithms work, including logistic regressions and neural networks.

There are many dangers, such as hacking, overfitting and presuming that all correlations are causal.

How to interpret and explain how a predictive model works

Advanced methods such as ensembles and persuasion modeling (also called persuasion modeling) are possible.

How do you choose the right tool among all available machine learning software options?

How to evaluate a predictive model in order to report its performance in business terms

How to screen a predictive model for biases against protected groups Also called AI ethics.

IN-DEPTH YET ACCESSIBLE. This curriculum was created by Eric Siegel, an industry leader and winner of Columbia University's teaching awards. This course is unique and engaging. It's also very accessible.

NO HANDS-ON, HEAVY MATH. This course does not provide hands-on training, but rather an overview of the most recent techniques and dangerous pitfalls. This course is useful for both data scientists and business leaders. These exercises don't require any coding or machine-learning software. However, for one assessment you will need to create a predictive model using Excel or Google Sheets and then see how it changes before your eyes.

BUT, TECHNICAL LEARNERS MUST TAKE A OTHER LOOK. This curriculum provides complementary knowledge that top techies should also learn. This curriculum provides a solid conceptual framework that contextualizes core technology. This curriculum covers topics that are often left out of technical courses like persuasion and uplift modeling.

VENDOR-NEUTRAL. The course includes software demonstrations that show machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives will apply regardless of which machine learning software you use.

PREREQUISITES. Before taking this course, learners must have taken the first two courses in this specialization: "The Power of Machine Learning" and "Launching Machine Learning".

First, we will give a brief history about machine learning. Next, we will discuss why neural networks are so efficient in solving many data science problems. Next, we'll discuss how to set up a supervised learning problem with gradient descent. We will discuss how to create datasets that allow generalization, and the best methods to do so in a repeatable way that allows experimentation.

Course Objectives: Learn why deep learning is so popular. Assess models using performance metrics and loss functions to identify common issues in machine learning. Make repeatable and scalable evaluation, testing, and training datasets

This guided project course is part the "Tensorflow For AI" series. It builds on the DeepLearning.AI TensorFlow Developer Professional Certificate at Coursera. It will help learners improve their skills and create more Tensorflow projects. This 1-hour-long project-based course will teach you the basics of Tensorflow. You will also learn how to create, compile, and train a neural networks with Tensorflow. Additionally, you will be given a bonus project that uses Tensorflow. This project will give you a better understanding of Tensorflow and help you to create scalable models for real-world problems. This class is intended for those who want to learn Python to build AI models with TensorFlow. It also applies to learners who have completed a basic deep-learning course or are looking for practical deep learning with TensorFlow. This project will provide learners with a deeper understanding of Tensorflow's main components as well as improve their Tensorflow skills. This project will help them achieve their career goals and add this project to their portfolios.

How can you improve the accuracy of your ML models How can you determine which columns contain the most useful features? We are pleased to welcome you to Feature Engineering. Here we will discuss the good and bad features as well as how to preprocess and transform them so that they can be used in your models.

This course demonstrates a real-world, practical approach to ML workflow. This course includes a case study of an ML team faced with multiple ML business needs. This team will need knowledge about the tools required for data management and governance as well as the best method to preprocess data. BigQuery can be used to preprocess data.

Three options are available to the team for creating machine learning models that can be used in specific cases. This course will explain why AutoML, BigQueryMLMLML and custom training are used to achieve their goals. This course provides an in-depth look at custom training. This course covers custom training needs, such as storage and loading large datasets, code structure and exporting a trained computer.

This will enable you to create a machine learning model that can be used for training. It can also be used to build containers images using very little knowledge about Docker.

This case study discusses hyperparameter tuning using Vertex Vizier and how it can increase model performance. In this section, we will discuss model improvement. We will be discussing regularization, sparsity, and other important concepts. The discussion ends with model monitoring and prediction and how VertexAI can help manage ML-models.

This course will teach you how to implement different types of production machine learning systems: continuous, continuous, and continuous training; dynamic, dynamic and static inference; batch processing, online inference, and dynamic and dynamic Inference. TensorFlow abstractions will be covered as well as the different options available for performing distributed training. You will learn how to use custom estimators for creating distributed training models.

This course introduces participants the MLOps tools, best practices, and MLOps methods for deploying, evaluating and monitoring production ML systems on Google Cloud. MLOps refers to the testing, monitoring and automation of ML systems in manufacturing. Machine Learning Engineering professionals employ tools to continuously improve and evaluate the models they have deployed. They can work with Data Scientists (or could be), who create models to allow speed and rigor when deploying the most performant models.

This course is intended for Data Scientists who want to rapidly move from prototype to production and deliver business impact. Software Engineers who want to learn Machine Learning Engineering skills. ML Engineers looking to adopt Google Cloud in their ML production projects.

This course will teach you from ML Engineers and Trainers, who are involved in the development of ML pipelines at Google Cloud. TensorFlow Extended (or TFX) will be the first module. This is Google's machine learning platform that uses TensorFlow to manage ML pipelines and metadata. Learn about pipeline components and how to orchestrate your pipeline with TFX. Learn how to automate your pipeline with continuous integration and continuous deployment. Also, how to manage metadata ML.

Next, we'll shift our focus to how we can reuse ML pipelines across multiple ML frameworks like tensorflow and pytorch. Cloud Composer is another tool that Google Cloud offers to help you manage your continuous training pipelines. Finally, we'll cover how to use MLflow to manage the entire machine learning lifecycle. This course is for advanced learners only. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<