I’m pleased to announce my fourth and final video course. The course has already been out for a couple months by now, but that doesn’t mean it’s too late for me to write about it!
Last year I started publishing video courses with Packt Publishing. These courses formed a series of introductory courses for data analysis using Python, including Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, and most recently Training Your Systems with Python Statistical Modelling. Viewers learned the basics of managing data in Python, getting it from the Internet, and how to apply machine learning to datasets to develop predictive systems.
This final course caps the series off with applications. The first half of the course covers two major areas of AI: natural language processing (NLP) and computer vision (CV). In the NLP section, I introduce basic NLP tasks and show how to use Python’s Natural Language Toolkit (NLTK) for NLP. Then in the CV section I show several CV tasks and how to use libraries from PIL to OpenCV and SciPy. These sections are brief in theory and heavy in application; nearly every video includes an extensive Python application of the concepts and software presented.
The last two sections of the course are complete Python projects. The first project is an NLP project; the objective is to train a spam detector. The second project develops a system for detecting emotions in images. In these projects, I get a dataset, prepare it for processing, apply a machine learning system and evaluate the results. These projects use techniques and concepts from all the previous courses in the series (though one may be able to appreciate the content without having seen the other courses).
All together the course lasts approximately two hours.
The videos in the course–narrated by me–include not only an explanation of the topic at hand but interactive demonstrations, so viewers can see how to use the software and follow along if they so desire. The video course includes the Jupyter notebooks I use in my demonstrations; viewers can run my code blocks to replicate my results, and edit them for their own experimentation.
You can buy the course on Packt’s website. If the price of the course is an obstacle, perhaps consider watching it on Mapt, Packt’s subscription service, which gives you access not only to all my courses but everything Packt has published (and they publish a ton of stuff), along with one free book of your choice to keep every month (likely without any DRM; just a plain ol’ PDF). (I also hear that Packt’s videos are available on other services, such as Lynda, but don’t quote me on that.)
I thank Packt for publishing this course. I also thank my editor, Viranchi Shetty, for offering feedback and keeping me on schedule. The editors at Packt had a big impact on the final product.
If you like my blog and would like to support me, perhaps consider purchasing the course. If you have no need for it or don’t have the money to spend (which I understand completely; I don’t live a life of glamour myself, being a graduate student), I’d love for you to spread the word about the course. Tell a friend wanting to get started in data analysis or data science, or even share this post on Facebook or Twitter or whatever your preferred social network is. Directing more eyes to the course helps. Write a review if you have watched it; I would love to hear your feedback, both positive and negative (though if negative, be gentle and constructive please).
My website has a new page for the new course here.
Thanks for reading! This is the final video course I have agreed to create, and I don’t plan on creating any more video courses or books in the near future. Creating these courses was a major time investment. In fact, they may have distracted from my studies as a graduate student, which worries me. I don’t plan to write any more courses until I have Ph.D. Perhaps now that the courses are done I may have more time for blogging as well!
If you want to know more about what the course is like, below are some of the videos included in the course.