Learn to start developing deep learning models with Keras.

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Transform the way you work with large-scale data. This course will teach you how to leverage the power of LangChain to efficiently handle, query, and analyze complex datasets.

Build rules- and retrieval-based chatbots in Python.

Build chatbots in Python using deep learning.

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.

Ready to dive into the world of Generative AI? Learn how ChatGPT works, how to use ChatGPT in your everyday life, and how to write effective ChatGPT prompts.

Learn about the difference between simple linear regression and multiple linear regression in R

Learn how to code and clean and manipulate data for analysis and visualization with the R programming language.

Leverage machine learning to make recommendations with recommender systems.