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Building Machine Learning Solutions with Tensorflow

Justin Flett, Jerry Kurata, Jon Flanders, Janani Ravi, and Vitthal Srinivasan

TensorFlow is an open-source machine learning software library developed Google. Since it was released in 2015, it has become one of the most widely-used machine learning libraries. This skill will teach you how to implement the machine learning workflow using TensorFlow, and apply the library from Python to solve simple and complex machine learning problems.

What You'll Learn

Design and implementation of machine learning solutions using TensorFlow with Python

Applying Tensorflow to common analytical problems, such as classification, clustering, and regression

Debugging Tensorflow projects

Deploying Tensorflow projects to the cloud

Applying Tensorflow to more advanced problems spaces, such as image recognition, language modeling, and predictive analytics.

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What's inside

Nine courses

TensorFlow 1: Getting Started

(2 hours)

This course teaches you how to install and use TensorFlow, a leading machine learning library from Google. You'll learn how to create a range of machine learning models, from simple linear regression to complex deep neural networks.

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Understanding the Foundations of TensorFlow

(2 hours)

This course introduces TensorFlow, an open source data flow library for numerical computations using data flow graphs. In this course, you'll learn the TensorFlow library from very first principles. First, you'll start with the basics of machine learning using linear regression as an example and focuses on understanding fundamental concepts in TensorFlow.

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Building Regression Models Using TensorFlow 1

(2 hours)

TensorFlow is the tool of choice for building deep learning applications. In this course, you'll learn how neurons in neural networks learn non-linear functions, and how neural networks execute operations such as regression and classification.

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Building Unsupervised Learning Models with TensorFlow

(3 hours)

Unsupervised learning techniques work with large datasets to find patterns. This course teaches you clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.

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Debugging and Monitoring TensorFlow Programs

(2 hours)

This course delves into tfdbg and TensorBoard, two essential tools for debugging and monitoring TensorFlow programs. These tools enable you to inspect the internal state of your programs and visualize execution metrics and state.

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Deploying TensorFlow Models to AWS, Azure, and the GCP

(2 hours)

This course teaches data scientists and engineers how to deploy TensorFlow models to production locally or on AWS, Azure, or GCP. It covers saving model parameters, scaling with Docker, using AWS SageMaker, and deploying on Google Cloud Platform.

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Implementing Image Recognition Systems with TensorFlow 1

(1 hours)

TensorFlow is a popular library for implementing a range of deep learning solutions, especially those that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.

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Implementing Predictive Analytics with TensorFlow

(1 hours)

TensorFlow is a widely-used data science and machine learning software library. This course will teach you the basics of implementing predictive analytics using TensorFlow, including supervised learning, recommendation, and reinforcement systems.

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Sentiment Analysis with Recurrent Neural Networks in TensorFlow

(2 hours)

Recurrent neural networks (RNNs) are ideal for considering sequences of data. In this course, you'll explore how word embeddings are used for sentiment analysis using neural networks. Sentiment analysis is a common problem to solve using machine learning techniques. This course will teach you how to utilize RNNs to classify movie reviews based on sentiment.

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Time

21 hours

Via

Pluralsight

Institution

Instructors

Justin Flett

Jerry Kurata

Jon Flanders

Janani Ravi

Vitthal Srinivasan

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