Get an introduction to the field of machine learning with this free online course from Coursera. In this course, you’ll learn about the basics of machine learning with TensorFlow.

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## TensorFlow Basics

TensorFlow is a powerful tool for doing machine learning. The hard part is understanding how it works and what can be done with it. This course will give you a gentle introduction to the basics of TensorFlow so that you can get started using it for doing machine learning.

## Linear Regression with TensorFlow

In this module, we’ll study linear regression, which is perhaps the simplest and most well-known machine learning algorithm. Linear regression is used to predict a continuous value, such as an individual’s annual income. We’ll use a linear regression model to guess the annual income of persons based on various features like age, education, and experience.

Linear regression is a supervised learning algorithm, which means that we need labeled training data to train our model. The labels are the values that we’re trying to predict, in this case annual incomes. The features are the independent variables that are used to predict the label, in this case age, education, and experience.

To train our linear regression model, we’ll use the Gradient Descent algorithm. Gradient Descent is an optimization algorithm that can be used to find the minimum of a function. In machine learning, we often want to find the minimum of a cost function or objective function. The cost function represents the error of our model on the training data. We want to minimize this cost function so that our model can generalize well from the training data to unseen data.

## Logistic Regression with TensorFlow

Logistic regression is a supervised learning technique that can be used for binary classification problems, where the target variable can take on two values: 0 or 1. In this tutorial, we’ll build a logistic regression model to predict whether or not a student gets admitted to a university.

To build our logistic regression model, we’ll be using TensorFlow, which is a powerful and popular open-source software library for data analysis and machine learning. TensorFlow was created by Google and released under the Apache 2.0 open source license.

If you’re new to TensorFlow, don’t worry – this tutorial will explain everything you need to know! Let’s get started.

## Neural Networks with TensorFlow

In this module, we’ll learn about neural networks and how to train them effectively using the popular TensorFlow library. We’ll start by taking a look at the basic concepts of neural networks, then we’ll move on to training neural networks effectively with TensorFlow. By the end of this module, you’ll be able to build and train your own neural networks to tackle difficult tasks such as image classification and machine translation.

## Convolutional Neural Networks with TensorFlow

This course will teach you how to build convolutional neural networks (CNNs) in TensorFlow. You’ll learn how to use TensorFlow to automatically tune parameters in a CNN, and how to use it efficiently on a GPU.

## Recurrent Neural Networks with TensorFlow

RNNs are a type of neural network well-suited to time series or text data. Sequences in TensorFlow are represented using the RNN class which takes as input a list of RNN cells, each of which take as input the previous state and an input element, and produce an output and a new state. A popular variant of recurrent neural networks is long short-term memory (LSTM) cells. Using LSTM cells instead of vanilla RNN cells often leads to improved performance on challenging tasks.

## Autoencoders with TensorFlow

Autoencoders are a powerful tool for unsupervised learning, and can be used to learn features in data using only unlabeled data. In this course you will learn how to build autoencoders using TensorFlow, and you will use them to learn features from data that can be used for classification or other tasks.

## Reinforcement Learning with TensorFlow

Reinforcement learning is a type of machine learning that enables agents to learn by taking actions in an environment and receiving feedback for those actions. With reinforcement learning, an agent can learn to perform a wide variety of tasks, such as playing video games, flying drones, or driving cars.

One of the key benefits of reinforcement learning is that it allows agents to learn by trial and error, without needing to be explicitly programmed with how to perform the task. This makes it well suited for tasks that are too difficult or expensive to program with traditional methods.

TensorFlow is a powerful tool for implementing machine learning algorithms, and it is especially well suited for reinforcement learning. In this course, you will learn how to use TensorFlow to implement reinforcement learning algorithms. You will also get hands-on experience with several popular reinforcement learning algorithms, such as Q-learning and SARSA. By the end of this course, you will be able to apply reinforcement learning to solve a variety of problems.

## Generative Models with TensorFlow

In this module, we’ll explore how to use TensorFlow to build generative models. Specifically, we’ll cover the following topics:

– What is a generative model?

– Why are generative models powerful?

– What are some of the challenges in building generative models?

– How can we use TensorFlow to build generative models?

– What are some specific types of generative models that we can build with TensorFlow?

We’ll also look at some specific examples of how TensorFlow can be used to build generative models, including a simple linear model and a more complicated nonlinear model. By the end of this module, you should be able to:

– Understand what is meant by a generative model.

– Explain why generative models are powerful.

– Understand some of the challenges in building generative models.

– Use TensorFlow to build simple linear and nonlinear generative models.

## Deploying TensorFlow Models

At the end of this course, you will be able to deploy your TensorFlow models to a production environment. You will learn how to choose the right deployment platform, how to optimize your models for performance, and how to monitor and troubleshoot your deployments.

Keyword: Coursera Machine Learning with TensorFlow