AI Development with TensorFlow Training

Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.

Enrolment validity: Lifetime

About This Course

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

  • Machine Learning Engineer
  • Data Scientist
  • Data Engineers
  • Data Analyst
  • Software Developer/Engineer (AI/ML)
  • Human-Centered Machine Learning Designer
  • NLP Scientist
  • Director of Analytics
  • Principal Data Scientist
  • Computer Vision Engineer
  • Algorithm Engineer
  • Computer Scientist

To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and basic knowledge of probability and statistics.

Get a custom learning plan tailored to fit your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

Learning Objectives

Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network

Target Audience

  • Anyone interested in artificial intelligence and how it can be used to solve many problems.


86 Lessons20h

Introduction to Machine Learning

Introduction to Machine Learning Algorithms
Understanding Deep Learning
Supervised and Unsupervised Learning
TensorFlow for Machine Learning
Understanding How to Install TensorFlow
Computation Graph with TensorBoard
Variables and Placeholders on TensorBoard
Feed Dictionaries
Named Scopes for Better Visualization
Practical Exercise

Simple Regression & Classification Models

Deep Neural Networks and Image Classification

CNN for Image Classification

Word Embeddings & RNNs

Sentiment Analysis with Recurrent Neural Networks

K-means Clustering with TensorFlow

Building Autoencoders in TensorFlow



All Levels
Duration 20 hours
86 lectures

Material Includes

  • Self-Paced Learning
  • Extensive content
  • Practical Exercises
  • Resume support
  • Linkedin profile optimization
  • Course Completion Certificate
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