Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them. In addition, you'll get an overview of the general workflow involved in machine learning, as well as the tools and other resources that support it. This course also promotes the importance of ethics in AI/ML, and provides you with techniques for addressing ethical challenges. Ultimately, this course will get you thinking about the "why?" of AI/ML, and it will ensure that your more technical work in later courses is done with clear business goals in mind.

In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this 1-hour long project-based course, you will learn how to build your own Machine Learning Image Classifier using Python and Colab. You will be able to easily load the data, preview it, process and normalize it, then train and test your model! I hope you enjoy the experience! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.

Welcome to this hands-on project on using Microsoft’s Custom Vision service for automated machine learning or AutoML as it’s popularly known. In this project, you are going to use Microsoft’s drag and drop tool to train your computer to recognize images of dogs and cats. We are going to do all of this without writing a single line of code! To take this guided-project, you do not need a background in computer science, machine learning or coding. The only prerequisite for this project is that you have a Microsoft Azure account. If you don’t already have one, you will have to sign up for it. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Welcome to the “Building a Keras Horse Zebra CycleGAN Webapp with Streamlit” guided project. In this project, we will build a Streamlit web app of a keras trained computer vision CycleGAN for horse images to zebra and vice versa. This project will take a jpg image of a horse and transform it into a zebra and take a picture of a zebra and transform it to a horse. This project is an intermediate python project for anyone interested in learning about how to productionize computer vision models or more specifically a beginner GAN model with Streamlit and Python. It requires preliminary knowledge on how to build and train GAN models (as we will not be building or training models) but we will be using a model that has already been trained and provided in the workspace.

In this 2-hour long project-based course, you will learn how to Build a Crossroad AI Camera: Learning Objective 1: By the end of Task 1, you will be able to explain the OpenVINO™ Toolkit Workflow and OpenVINO™ Toolkit Components Learning Objective 2: By the end of Task 2, you will be able to operationalize models using the Model Downloader utility Learning Objective 3: By the end of Task 3, you will be able to perform Model Preparation, Conversion and Optimization Learning Objective 4: By the end of Task 4, you will be able to Running and Tuning Inference Learning Objective 5: By the end of Task 5, you will be able to create visualization of Person Attributes and Person Re-identification (REID) information for each detected person in an Image/Video/Camera input.