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Implementing Predictive Analytics with TensorFlow
Justin Flett
Justin Flett
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.
Read more
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.
Read more
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.
Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Implementing Predictive Analytics with TensorFlow, you will learn foundational knowledge of solving real-world data science problems. First, you will explore the basics of implementing supervised learning problems including linear regression and neural networks. Next, you will discover how recommendation systems can be implemented using TensorFlow. Finally, you will learn how to understand and implement reinforcement learning systems. When you are finished with this course, you will have the skills and knowledge of TensorFlow needed to solve data science and machine learning problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/oge404/implementing
What's inside
Syllabus
Course Overview
Implementing Supervised Learning Systems
Implementing Recommendation Systems
Implementing Reinforcement Learning Systems
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Syllabus
Course Overview
Implementing Supervised Learning Systems
Implementing Recommendation Systems
Implementing Reinforcement Learning Systems
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for learners whose background includes programming and some data science knowledge
Teaches skills, knowledge, and tools that are highly relevant to industry
Develops professional skills or deep expertise in a particular topic or set of topics
Teaches tools or software that are on the decline or no longer widely used in industry
Requires learners to come in with extensive background knowledge first
Teaches material that may be dated by software updates
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Career center
Learners who complete Implementing Predictive Analytics with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Science methodologies help guide businesses to make decisions that drive optimal outcomes. Data Scientists are professionals with the ability to extract meaningful information from big data sets. This ability is made possible via the use of machine learning and artificial intelligence. Implementing Predictive Analytics with TensorFlow can help you build a foundation in these areas, which will make you an attractive candidate for Data Scientist positions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning (ML) Engineers work with data scientists to design, develop, and build ML models. They also work to identify the best ML algorithms for solving specific business problems and to integrate ML models into existing systems. This course can help you build a solid foundation in ML, which will make you a more competitive candidate for Machine Learning Engineer roles.
Data Analyst
Data Analyst
Data Analysts play a critical role in transforming raw data into usable insights for businesses. They use their skills in data analysis, statistics, and programming to identify trends, patterns, and anomalies in data. Implementing Predictive Analytics with TensorFlow can help you develop the skills you need to become a successful Data Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Many Software Engineers who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Software Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions about future events. Many Quantitative Analysts who work in finance and risk management use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Quantitative Analysts.
Research Scientist
Research Scientist
Research Scientists conduct research and develop new technologies and products. They use their knowledge of science, engineering, and mathematics to solve complex problems. Many Research Scientists who work in data science and machine learning use TensorFlow in their research. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Research Scientists.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. Many Business Analysts who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Business Analysts.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make recommendations to investors. Many Financial Analysts who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Financial Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. Many Operations Research Analysts who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Operations Research Analysts.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics, including data science and machine learning. Many Consultants who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Consultants.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. Many Actuaries who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Actuaries.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to businesses. Many Risk Managers who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Risk Managers.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. Many Product Managers who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Product Managers.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. Many Statisticians who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Statisticians.
Data Architect
Data Architect
Data Architects design and build data systems that support the needs of businesses. Many Data Architects who work in data science and machine learning use TensorFlow in their work. By taking this course, you can learn the basics of TensorFlow and gain a competitive edge in the job market for Data Architects.
For more career information including salaries, visit:
OpenCourser.com/course/oge404/implementing
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Implementing Predictive Analytics with TensorFlow.
Provides a comprehensive overview of reinforcement learning concepts and algorithms. It valuable resource for anyone who wants to learn more about the fundamentals of reinforcement learning.
Save
Provides a comprehensive overview of deep learning concepts and algorithms, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about the fundamentals of deep learning.
Provides a hands-on guide to using Scikit-Learn, Keras, and TensorFlow for machine learning projects. It covers everything from data preprocessing to model evaluation.
Provides a hands-on guide to using deep reinforcement learning for solving real-world problems. It covers everything from the basics of deep reinforcement learning to advanced topics such as multi-agent systems and continuous control.
Provides a comprehensive overview of data science and machine learning concepts and algorithms, using Python as the programming language. It valuable resource for anyone who wants to learn more about data science and machine learning using Python.
Provides a comprehensive overview of machine learning concepts and algorithms, using Java as the programming language. It valuable resource for anyone who wants to learn more about machine learning using Java.
Provides a practical guide to using TensorFlow for deep learning projects. It covers everything from the basics of TensorFlow to advanced topics such as natural language processing and computer vision.
Provides a comprehensive overview of machine learning concepts and algorithms, using R as the programming language. It valuable resource for anyone who wants to learn more about machine learning using R.
Provides a comprehensive overview of machine learning concepts and algorithms, using Python as the programming language. It valuable resource for anyone who wants to learn more about machine learning using Python.
Provides a hands-on guide to using Python for machine learning projects. It covers everything from data preprocessing to model evaluation.
Provides a collection of recipes for solving common machine learning problems using TensorFlow 2.0. It covers everything from data preprocessing to model evaluation.
Provides a gentle introduction to machine learning concepts and algorithms. It valuable resource for anyone who wants to learn more about machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oge404/implementing
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Time
81 hours
Level
Intermediate
Via
Pluralsight
Instructor
Justin Flett
Language
English
This course belongs to a
collection
called
Building Machine Learning Solutions with Tensorflow. It's comprised of 11 courses:
- TensorFlow 1: Getting Started
- Understanding the Foundations of TensorFlow
- Building Regression Models Using TensorFlow 1
- Building Classification Models with TensorFlow
- Building Unsupervised Learning Models with TensorFlow
- Debugging and Monitoring TensorFlow Programs
- Deploying TensorFlow Models to AWS, Azure, and the GCP
- Language Modeling with Recurrent Neural Networks in TensorFlow
- Implementing Image Recognition Systems with TensorFlow 1
- Implementing Predictive Analytics with TensorFlow (viewing)
- Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Good to know
Builds a strong foundation for learners whose background includes programming and some data science knowledge
Teaches skills, knowledge, and tools that are highly relevant to industry
Develops professional skills or deep expertise in a particular topic or set of topics
Teaches tools or software that are on the decline or no longer widely used in industry
Requires learners to come in with extensive background knowledge first
Teaches material that may be dated by software updates
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