Python has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka’s contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.
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- Your host as usual is Tobias Macey and today I’m interviewing Sebastian Raschka about his experiences writing the popular Python Machine Learning book
- How did you get introduced to Python?
- How did you get started in machine learning?
- What were the concepts that you found most difficult in your career with statistics and machine learning?
- One of your notable contributions to the field is your book "Python Machine Learning". What inspired you to write the initial version?
- How did you approach the challenge of striking the right balance of depth, breadth, and accessibility for the content?
- What was your process for determining which aspects of machine learning to include?
- You have made 3 editions of the book from 2015 through December of 2019. In what ways has the book changed?
- What are the biggest changes to the ecosystem and approaches to ML in that timeframe?
- What are the fundamental challenges of developing machine learning projects that continue to present themselves?
- What new difficulties have arisen with the introduction of new technologies and the rise of deep learning?
- What are some of the ways that the Python language lends itself to analytical work?
- What are its shortcomings and how has the community worked around them?
- What do you see as the biggest risks to the popularity of Python in the data and analytics space?
- What are some of the common pitfalls that your readers and students face while learning about different aspects of machine learning?
- What are some of the industries that can benefit most from applications of machine learning?
- What are you most excited about in the applications or capabilities of machine learning?
- What are you most worried about?
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- Python Machine Learning (Packt)
- Buy On Amazon (affiliate link)
- UW Madison
- Udacity CS101
- Andrew Ng
- Support-Vector Machine
- Bayesian Statistics
- Sebastian’s Blog
- Heatmaps In R
- The Hundred Page Machine Learning Book by Andriy Burkov
- Random Forest
- Logistic Regression
- Generative Adversarial Networks
- Is This Person Real / This Person Does Not Exist
- Reinforcement Learning
- Open AI
- Google DeepMind
- Google Colab
- Sebastian Raschka, Joshua Patterson, and Corey Nolet (2020). Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 2020, 11, 193
- Swift Language
- Swift for TensorFlow
- Differential Privacy
- YouTube recordings of Stat453: Introduction to Deep Learning and Generative Models (Spring 2020)