Machine Learning Mastery With Python ((FREE))
This book will lead you from being a developer who is interested in machine learning with Python to a developer who has the resources and capability to work through a new dataset end-to-end using Python and develop accurate predictive models.
Machine Learning Mastery with Python
From here you can start to dive into the specifics of the functions, techniques and algorithms used with the goal of learning how to use them better in order to deliver more accurate predictive models, more reliably in less time.
Jason is a software engineer and research scientist with a background in artificial intelligence and high-performance computing. He has authored more than 20 technical books on machine learning and has built, operated, and exited online businesses.
For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below.
Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience. These prerequisites are understandable given that machine learning is an advanced discipline.
Missing a few subjects? Good news! Some of this experience can be acquired through our recommendations in the first two articles (programming, statistics) of this Data Science Career Guide. Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.
Released in 2011, it covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms. The estimated timeline is eleven weeks, with two weeks dedicated to neural networks and deep learning. Free and paid options are available.
Ng is a dynamic yet gentle instructor with a palpable experience. He inspires confidence, especially when sharing practical implementation tips and warnings about common pitfalls. A linear algebra refresher is provided and Ng highlights the aspects of calculus most relevant to machine learning.
The Analytics Edge (Massachusetts Institute of Technology/edX): More focused on analytics in general, though it does cover several machine learning topics. Uses R. Strong narrative that leverages familiar real-world examples. Challenging. Ten to fifteen hours per week over twelve weeks. Free with a verified certificate available for purchase. It has a 4.9-star weighted average rating over 214 reviews.
Python for Data Science and Machine Learning Bootcamp (Jose Portilla/Udemy): Has large chunks of machine learning content, but covers the whole data science process. More of a very detailed intro to Python. Amazing course, though not ideal for the scope of this guide. 21.5 hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 3316 reviews.
Implementing Predictive Analytics with Spark in Azure HDInsight (Microsoft/edX): Introduces the core concepts of machine learning and a variety of algorithms. Leverages several big data-friendly tools, including Apache Spark, Scala, and Hadoop. Uses both Python and R. Four hours per week over six weeks. Free with a verified certificate available for purchase. It has a 4.5-star weighted average rating over 6 reviews.
AWS Machine Learning: A Complete Guide With Python (Chandra Lingam/Udemy): A unique focus on cloud-based machine learning and specifically Amazon Web Services. Uses Python. Nine hours of on-demand video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 62 reviews.
Big Data: Statistical Inference and Machine Learning (Queensland University of Technology/FutureLearn): A nice, brief exploratory machine learning course with a focus on big data. Covers a few tools like R, H2O Flow, and WEKA. Only three weeks in duration at a recommended two hours per week, but one reviewer noted that six hours per week would be more appropriate. Free and paid options available. It has a 4-star weighted average rating over 4 reviews.
Machine Learning for Data Science and Analytics (Columbia University/edX): Introduces a wide range of machine learning topics. Some passionate negative reviews with concerns including content choices, a lack of programming assignments, and uninspiring presentation. Seven to ten hours per week over five weeks. Free with a verified certificate available for purchase. It has a 2.74-star weighted average rating over 36 reviews.
Machine Learning for Musicians and Artists (Goldsmiths, University of London/Kadenze): Unique. Students learn algorithms, software tools, and machine learning best practices to make sense of human gesture, musical audio, and other real-time data. Seven sessions in length. Audit (free) and premium ($10 USD per month) options available. It has one 5-star review.
Machine Learning with Apache SystemML (Big Data University): Taught using Apache SystemML, which is a declarative style language designed for large-scale machine learning. Estimated completion time of eight hours. Big Data University is affiliated with IBM. Free.
Introducción al Machine Learning (Universitas Telefónica/Miríada X): Taught in Spanish. An introduction to machine learning that covers supervised and unsupervised learning. A total of twenty estimated hours over four weeks.
Unsupervised Learning in Python (DataCamp): Covers a variety of unsupervised learning algorithms using Python, scikit-learn, and scipy. The course ends with students building a recommender system to recommend popular musical artists. Thirteen videos and 52 exercises with an estimated timeline of four hours.
Undergraduate Machine Learning (Nando de Freitas/University of British Columbia): An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). de Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available (see below).
Neural Networks is one of the most popular machine learning algorithms and also outperforms other algorithms in both accuracy and speed. Therefore it becomes critical to have an in-depth understanding of what a Neural Network is, how it is made up and what its reach and limitations are.
Wayne, Josh and Ta talk about Esri's growing collection of 'pre-trained' packages for deep learning - available now in ArcGIS. From building footprint detection to point cloud classification and everything in between, there is something for everyone. The team share their must-have Machine Learning models along with simple tips for getting started, where to access useful resources, and the proven pathway to deep learning mastery.
Wayne: It doesn't have to be that hard and it is much easier these days. All right let's set the scene a bit with a little bit of background. I'm going to start with one of my favorite electronic music bands, it's a Canberra group called, um, B(if)tek and the opening track to their album 2020 was called "machines work". And in it there's a sound clip that claims, machines can do the work so that people have time to think.
Now this clip was from a 1967 TV advert for IBM, which was made by Jim Henson, the puppet master himself. So, what's the point of this little side note? Well, it's the idea that machines can be taught to do our boring tasks and it's by no means new. So, for over 50 years now, we've been revisiting and revisiting this idea with mixed results until fairly recently.
And today we've come a long way, especially in the field of machine learning or AI as it's often called. And now we have some really, really great tools that are ready to use out of the box without having to be a machine learning expert.
So I'll kick it off with number one. First of all you've got to get data, lots of it, because you've got to be able to train your deep learning model with plenty of examples of what a solar panel looks like. The more, the better.
So, number two, after you've done that, you take that data and you train an object detection deep learning model, and you show it all of those examples of solar panels, some of them tweaked, you tweak some parameters of the machine learning model. Give it some real beefy, gutsy computing resources. Plenty of time, go make yourself a coffee.
Ta: Now I mentioned two here. So, my bonus model is crowd counting. It's a deep learning model, to count the number of people in an image. I know we were talking recently just about the amount of work that we've been doing and just looking at, you know, what's happening with this pandemic at the moment. And I think this is an extremely relevant model.
So, what we've discovered with deep learning models is, you can get a very well-trained model, you can snap off the last layer of it, show it some more of your specialist data, and it will learn just the little extra bits that it needs to learn, to be more specific and more accurate at recognising things in your data.
To start learning Python with a free, beginner-level course, check out Learn to Program: The Fundamentals from the University of Toronto, Problem Solving, Python Programming, and Video Games from the University of Alberta, Foundations of Data Science: K-Means Clustering in Python from the University of London, or Data Processing Using Python from Nanjing University. If you have some Python experience, you may also be interested in the intermediate-level free course, Python and Statistics for Financial Analysis from The Hong Kong University of Science and Technology. 041b061a72