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.
Job Roles
- 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
Prerequisites
To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and basic knowledge of probability and statistics.
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.
Curriculum
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
Assessment
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
