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Janani Ravi
TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for...
Read more
TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for...
Read more
TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for classification. Finally, you'll discover how to implement more powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, you'll have a better understanding of how to build classification models with TensorFlow.
Register for this course and see more details by visiting:
OpenCourser.com/course/n1nq7r/building
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes techniques such as Logistic Regression and k-Nearest Neighbor, providing a strong foundation in traditional machine learning
Leverages the capabilities of TensorFlow, a widely adopted platform in the industry
Explores advanced deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks, for building powerful classification models
Guides learners in evaluating classification models using metrics like accuracy, precision, and recall, ensuring a thorough understanding of model performance
Suitable for individuals with a basic understanding of machine learning concepts
Requires familiarity with TensorFlow and its core concepts
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Building Classification Models with TensorFlow.
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Career center
Learners who complete Building Classification Models with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models for a variety of applications. This course can help you succeed in this role by giving you a solid understanding of the principles of classification modeling. TensorFlow is a leading framework for building deep learning models and as such, mastery of it is required for Machine Learning Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analyst use mathematical and statistical models to analyze markets and investments. This course can help you succeed in this career by giving you a strong foundation in machine learning and AI models, which are used extensively to improve investment and trading outcomes.
Data Scientist
Data Scientist
Data Scientists use machine learning and other techniques to extract knowledge from data. Convolutional Neural Networks and Recurrent Neural Networks are important in roles such as image recognition and text generation. This course can help build a foundation for success in this role by teaching you the key principles and techniques used in building classification models with TensorFlow.
Statistician
Statistician
Statisticians use data to solve problems and improve decision making. This course can help you in this career by teaching you techniques, mathematical principles, and algorithms for building classification models.
Business Analyst
Business Analyst
Business Analysts help companies improve their performance using data. This course can help you succeed in this role by giving you a strong foundation in building classification models, a key skill for this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software for companies and organizations. This course will help you succeed as a Software Engineer by teaching you the underlying mathematical principles, algorithms, and techniques used in machine learning models.
Research Scientist
Research Scientist
Research Scientists investigate and develop new technologies and products for various organizations. This course can help you in this role by providing you with the foundational knowledge and techniques required to build machine learning models.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers research, develop, and implement deep learning models. This course may be helpful for you as a Deep Learning Engineer as it covers TensorFlow, a leading framework for developing deep learning models.
Data Analyst
Data Analyst
Data Analysts use data to help companies improve their products and services. This course can help you in this role by giving you a working knowledge of machine learning techniques and algorithms important to this role.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and maintain cloud computing systems. It may be useful to take this course to get a strong foundation in deep learning models, which are needed for the development of some of the most advanced cloud computing services.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
AI Engineers build, implement, and maintain AI systems. This course may be helpful for this career as it teaches some of the foundational principles and techniques for building deep learning models used by AI.
Front-End Engineer
Front-End Engineer
Front-End Engineers design and develop the user interface for websites and other applications. A course in TensorFlow may not be the highest priority for your career, but it can help you to better understand the machine learning and AI programming models that your user interface interacts with.
Database Administrator
Database Administrator
Database Administrators maintain and manage computer databases. This course may not be the best for your career. However, it can help build a foundation in TensorFlow, which is a leading framework for building deep learning models.
Blockchain Developer
Blockchain Developer
Blockchain Developers design and develop blockchain systems. This course may not be the best fit for your career path, but it can give you an introduction to building models in TensorFlow, which is a framework commonly used by data scientists.
Web Developer
Web Developer
Web Developers design and develop websites and other web applications. This course is unlikely to be the best use of your time as your career path does not heavily rely on building machine learning models with TensorFlow.
For more career information including salaries, visit:
OpenCourser.com/course/n1nq7r/building
Reading list
We've selected four 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
Building Classification Models with TensorFlow.
Provides a comprehensive introduction to machine learning with TensorFlow, making it a good choice as background or prerequisite knowledge.
Provides a broad introduction to deep learning with Python, making it useful for background and prerequisite knowledge, as well as additional reading.
Provides a quick introduction to TensorFlow 2.0, which may be useful as background or prerequisite knowledge.
Provides a beginner-friendly introduction to TensorFlow, making it useful as background knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/n1nq7r/building
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Time
3 hours
Via
Pluralsight
Instructor
Janani Ravi
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 (viewing)
- 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
- Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Good to know
Emphasizes techniques such as Logistic Regression and k-Nearest Neighbor, providing a strong foundation in traditional machine learning
Leverages the capabilities of TensorFlow, a widely adopted platform in the industry
Explores advanced deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks, for building powerful classification models
Guides learners in evaluating classification models using metrics like accuracy, precision, and recall, ensuring a thorough understanding of model performance
Suitable for individuals with a basic understanding of machine learning concepts
Requires familiarity with TensorFlow and its core concepts
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