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Janani Ravi

This course covers the use of PyTorch to build various predictive models, using Recurrent Neural Networks, long-memory neurons in text prediction, and evaluating them using a metric known as the Mean Average Precision @ K.

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This course covers the use of PyTorch to build various predictive models, using Recurrent Neural Networks, long-memory neurons in text prediction, and evaluating them using a metric known as the Mean Average Precision @ K.

PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. In this course, Predictive Analytics with PyTorch, you will see how to build predictive models for different use-cases, based on the data you have available at your disposal, and the specific nature of the prediction you are seeking to make.

First, you will start by learning how to build a linear regression model using sequential layers. Next, you will explore how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Then, you will apply such an RNN to the problem of generating names - a typical example of the kind of predictive model where deep learning far out-performs traditional natural language processing techniques. Finally, you will see how a recommendation system can be implemented in several different ways - relying on techniques such as content-based filtering, collaborative filtering, as well as hybrid methods.

When you are finished with this course, you will have the skills to build, evaluate, and use a wide array of predictive models in PyTorch, ranging from regression, through classification, and finally extending to recommendation systems.

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What's inside

Syllabus

Course Overview
Implementing Predictive Analytics with Numeric Data
Implementing Predictive Analytics with Text Data
Implementing Predictive Analytics with User Preference Data
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches predictive analytics, which is a core field in both industry and academia
Instructor Janani Ravi has published several peer-reviewed journal articles on predictive analytics
Develops skills in PyTorch, which is becoming a leading library for predictive analytics
Covers relevant topics for practitioners who want to learn about predictive analytics, including text prediction and recommendation systems
Teaches machine learning algorithms like linear regression and recurrent neural networks (RNNs), which are valuable tools for data scientists and analysts
Requires students to have some prior programming experience, which may be a barrier for beginners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predictive Analytics with PyTorch with these activities:
Read "Deep Learning for Natural Language Processing" by Jason Brownlee
This book provides a comprehensive overview of deep learning for natural language processing.
Show steps
  • Read the book.
  • Take notes on the key concepts.
  • Work through the exercises in the book.
  • Apply the concepts you learned to your own projects.
Explain the difference between linear regression and logistic regression
This will help you understand concepts in implementing predictive analytics using numeric data.
Browse courses on Linear Regression
Show steps
  • Read the documentation for both linear regression and logistic regression.
  • Implement a simple linear regression model in PyTorch.
  • Implement a simple logistic regression model in PyTorch.
  • Compare the results of your linear and logistic regression models.
  • Explain the difference between linear and logistic regression to your tutor or another student.
Meet with a peer to discuss the course material
This will help you clarify your understanding of the course material.
Show steps
  • Find a peer to meet with.
  • Set up a time to meet.
  • Review the course material together.
  • Discuss any questions or concerns you have.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Develop a Random Forest Regression Model in PyTorch
Guided tutorials can help students to more deeply understand the process and implementation details of building predictive models using PyTorch. This specific activity helps familiarize learners with Random Forest Regression specifically.
Show steps
  • Review the PyTorch documentation on Random Forest Regression
  • Use the provided code template as a starting point
  • Follow the instructions in the guided tutorial to complete the model development
Follow a tutorial on how to build a neural network for text classification
This will help you understand concepts in implementing predictive analytics with text data.
Browse courses on Neural Networks
Show steps
  • Find a tutorial on how to build a neural network for text classification.
  • Follow the steps in the tutorial to build your own neural network.
  • Test your neural network on a dataset of text data.
  • Evaluate the performance of your neural network.
Attend a workshop on PyTorch
This will help you learn more about PyTorch and how to use it to build predictive models.
Show steps
  • Find a workshop on PyTorch.
  • Register for the workshop.
  • Attend the workshop.
  • Take notes on the key concepts.
  • Apply the concepts you learned to your own projects.
Contribute to a PyTorch open-source project
This will help you learn more about PyTorch and contribute to the community.
Show steps
  • Find a PyTorch open-source project to contribute to.
  • Read the project's documentation.
  • Identify a way to contribute to the project.
  • Make your contribution to the project.
  • Submit a pull request to the project.
Solve Kaggle Competitions on Recommendation Systems
Kaggle competitions provide an excellent platform for learners to apply their knowledge and skills in a practical setting. This specific activity helps students test their understanding of predictive analytics and recommendation systems.
Show steps
  • Choose a relevant Kaggle competition
  • Download the competition data
  • Train and evaluate your recommendation system model
  • Submit your results
Build a recommendation system for a movie streaming service
This will help you understand concepts in implementing predictive analytics with user preference data.
Show steps
  • Gather a dataset of user ratings for movies.
  • Implement a content-based filtering algorithm to recommend movies to users.
  • Implement a collaborative filtering algorithm to recommend movies to users.
  • Evaluate the performance of your recommendation system.
  • Deploy your recommendation system to a web service.
Build a predictive model for a real-world problem
This will help you apply the concepts you learn in this course to a real-world problem.
Show steps
  • Identify a real-world problem that you would like to solve.
  • Gather data relevant to the problem.
  • Build a predictive model using PyTorch.
  • Evaluate the performance of your model.
  • Deploy your model to a web service.

Career center

Learners who complete Predictive Analytics with PyTorch will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist combines programming skills with knowledge of mathematics and statistics to extract meaningful insights from data. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to extract meaningful insights from data.
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. This often involves using data analysis to identify inefficiencies and develop solutions. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify inefficiencies and develop solutions.
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This information is used to make investment decisions or to develop trading strategies. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to analyze financial data and make investment decisions.
Actuary
An Actuary is responsible for assessing and managing financial risk. This often involves using data analysis to develop models that can predict future events. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to develop models that can predict future events.
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and maintains machine learning models. These models can be used for a variety of tasks, such as making predictions, detecting fraud, or recommending products. Predictive Analytics with PyTorch may be useful as it teaches students how to build and train machine learning models.
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This often involves using data analysis to identify trends and patterns. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify trends and patterns.
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This often involves using machine learning models to improve the performance or functionality of software applications. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to improve the performance or functionality of software applications.
Data Analyst
A Data Analyst helps organizations understand their business by analyzing existing data. This often involves predicting future trends or customer behavior. Predictive Analytics with PyTorch may be useful as it teaches students how to build predictive models, which can be used to make predictions about future events or customer behavior.
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making investment recommendations. This often involves using data analysis to identify investment opportunities and develop investment strategies. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify investment opportunities and develop investment strategies.
Business Analyst
A Business Analyst helps organizations understand their business and make better decisions. This often involves using data analysis and modeling to identify opportunities and solve problems. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify opportunities and solve problems.
Research Scientist
A Research Scientist conducts research in a variety of fields, such as computer science, biology, or physics. They use their knowledge and skills to develop new technologies and products. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to develop new technologies and products.
Sales Manager
A Sales Manager is responsible for leading and motivating a sales team. This often involves using data analysis to identify sales opportunities and develop effective sales strategies. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify sales opportunities and develop effective sales strategies.
Product Manager
A Product Manager is responsible for the development and launch of new products. This often involves working with engineers and designers to create products that meet the needs of customers. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to develop products that meet the needs of customers.
Operations Manager
An Operations Manager is responsible for overseeing the day-to-day operations of a business. This often involves using data analysis to identify inefficiencies and develop solutions. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify inefficiencies and develop solutions.
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns. This often involves using data analysis to identify target markets and develop effective marketing strategies. Predictive Analytics with PyTorch may be useful as it teaches students how to use PyTorch to build and train machine learning models. This knowledge can be used to identify target markets and develop effective marketing strategies.

Reading list

We've selected 11 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 Predictive Analytics with PyTorch.
Delves into the fundamentals of building, training, and deploying deep learning models using PyTorch, covering a comprehensive range of topics. It is an excellent reference for gaining a deeper understanding of the concepts and techniques employed in this course.
Offers a practical approach to machine learning with PyTorch, focusing on hands-on examples and real-world applications. It provides valuable insights for implementing the concepts explored in this course.
Specializes in recurrent neural networks (RNNs) with PyTorch, covering advanced topics such as long short-term memory (LSTM) and gated recurrent units (GRUs). It valuable resource for delving deeper into the specific techniques used in this course.
Focuses on natural language processing (NLP) with PyTorch, providing essential knowledge for understanding the NLP techniques utilized in this course. It covers topics such as text classification, sentiment analysis, and machine translation.
Covers advanced deep learning techniques for natural language processing tasks. While not directly tied to PyTorch, it provides a comprehensive overview of the latest research and developments in the field, complementing the practical aspects explored in this course.
Serves as a comprehensive introduction to machine learning using Python. It offers a solid foundation in the fundamental concepts and techniques, providing a good starting point for further exploration of the topics covered in this course.
Offers a gentle introduction to machine learning using Python. It covers the basics of supervised and unsupervised learning, providing a good starting point for those new to the field.
Focuses on deep learning using Keras, a high-level neural networks API. While not specific to PyTorch, it provides valuable insights into deep learning concepts and architectures, complementing the topics covered in this course.
Provides a practical approach to deep learning using Fastai, a high-level deep learning library built on PyTorch. It covers various deep learning tasks such as image classification, object detection, and natural language processing, complementing the topics explored in this course.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, model evaluation, and deployment.

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