Over the course of several engagements and projects, I have stumbled upon several somewhat distinct patterns that have emerged from the need to on-board new folks into ML type of project or provide quick training to folks that already on the project and who like to get hands-on with ML, AI and DL type of work. So, in short, the question that we had to answer repeatedly “how do we get folks, who’s interested in learning ML-AI, trained with minimal time away from the work and get them hands-on as much as possible?”. Initially, to answer that question, we had to do quite a bit of legwork, come up with few approaches and go through many hops of trial-and-error. Ultimately, the outcome was satisfactory and the team still uses these frameworks and processes to get folks up to speed with ML. But, this post is not about that. The aim of this post is to share collection of materials, training courses and links that we have gathered and compiled while developing the process for training and on-boarding.
All materials and links posted here are selected at discretion of the research team and somewhat subjective given team’s background and the objectives.
First things first — some terminology articles and differences between AI, DL and ML.
I absolutely adore simplicity. One can always make more complex out of of simple. That’s why I always prefer to start from simple and add complexity later.. so, first link to article called “The simplest explanation of machine learning you’ll ever read”.
The simplest explanation of machine learning you’ll ever read.
Are you using the term ‘AI’ incorrectly?
A simple way to understand machine learning vs deep learning
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
Learning Courses
Machine Learning (Class Central) — Study free online Machine Learning courses & MOOCs from top universities and colleges. Read reviews to decide if a class is right for you.
Machine Learning — Stanford University — Free course created by Stanford University, taught by Andrew Ng.
Artificial Intelligence (AI) — Learn the fundamentals of Artificial Intelligence (AI). Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
Machine Learning. Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Learning From Data — Online Course (MOOC) — More than 4M views on Youtube and iTunes. Featured on edX. Free Course, Lecture Videos Available.
Machine Learning — Free Course with Lecture Slides and Video recordings
Department of Computer Science, 2014–2015, ml, Machine Learning.
Machine Learning — University of Washington — Master machine learning fundamentals in four hands-on courses.
Neural Networks for Machine Learning — Learn about artificial neural networks and how they’re being applied to speech and object recognition, image segmentation, modeling language and human motion. Free Course.
Video Courses and Video Lessons
Practical Machine Learning Tutorial with Python Intro (playlist)
DeepLearning.TV
Machine Leaning Recipes
Sentdex
Practical Deep Learning For Coders (Jeremy Howard & Rachel Thomas)
Machine Learning, Udacity (Georgia Tech)
Intro to Machine Learning, Udacity (Sebastian Thrun)
Neural Networks for Machine Learning, Coursera (Geoffrey Hinton)
Keras — Python Deep Learning Neural Network API
TensorFlow Tutorials
Deep Learning TensorFlow and Deep Learning With Neural Networks by Simplilearn
Deep Learning with R tutorial
The art if neural networks by Mike Tyka
Blogs
Below you find list of blogs in ML/DL/AI related topics.
Cheat sheets
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
2. Essential Cheat Sheets for Machine Learning and Deep Learning Engineers
Github resources
Where can you practice?
Machine Learning Kaggle Competition
I highly recommend trying out Kaggle competitions even though you have a slim chance of even getting to top-100. The value of Kaggle competitions is the community. Read the kernels and take good practices from them. Read comments and engage in discussions. That’s where you will learn tremendously.
…and a few articles related to the subject
Data Sources
(Courtesy of Elite Data Science)
Datasets for Exploratory Analysis
Exploratory analysis is your first step in most data science exercises. The best datasets for practicing exploratory analysis should be fun, interesting, and non-trivial (i.e. require you to dig a little to uncover all the insights).
- Game of Thrones — Game of Thrones is a popular TV series based on George R.R. Martin’s A Song of Fire and Ice book series. With this dataset, you can explore its political landscape, characters, and battles.
- World University Rankings — Ranking universities can be difficult and controversial. There are hundreds of ranking systems, and they rarely reach a consensus. This dataset contains three global university rankings.
- IMDB 5000 Movie Dataset — This dataset explores the question of whether we can anticipate a movie’s popularity before it’s even released.
Aggregators:
- Kaggle Datasets — Open datasets contributed by the Kaggle community. Here, you’ll find a grab bag of topics. Plus, you can learn from the short tutorials and scripts that accompany the datasets.
- r/datasets — Open datasets contributed by the Reddit community. This is another source of interesting and quirky datasets, but the datasets tend to less refined.
Datasets for General Machine Learning
In this context, we refer to “general” machine learning as Regression, Classification, and Clustering with relational (i.e. table-format) data. These are the most common ML tasks.
- Wine Quality (Regression) — Properties of red and white vinho verde wine samples from the north of Portugal. The goal is to model wine quality based on physicochemical tests. (We also have a tutorial.)
- Credit Card Default (Classification) — Predicting credit card default is a valuable and common use for machine learning. This rich dataset includes demographics, payment history, credit, and default data.
- US Census Data (Clustering) — Clustering based on demographics is a tried and true way to perform market research and segmentation.
Aggregators:
- UCI Machine Learning Repository — The UCI ML repository is an old and popular aggregator for machine learning datasets. Tip: Most of their datasets have linked academic papers that you can use for benchmarks.
Datasets for Deep Learning
While not appropriate for general-purpose machine learning, deep learning has been dominating certain niches, especially those that use image, text, or audio data. From our experience, the best way to get started with deep learning is to practice on image data because of the wealth of tutorials available.
- MNIST — MNIST contains images for handwritten digit classification. It’s considered a great entry dataset for deep learning because it’s complex enough to warrant neural networks, while still being manageable on a single CPU. (We also have a tutorial.)
- CIFAR — The next step up in difficulty is the CIFAR-10 dataset, which contains 60,000 images broken into 10 different classes. For a bigger challenge, you can try the CIFAR-100 dataset, which has 100 different classes.
- ImageNet — ImageNet hosts a computer vision competition every year, and many consider it to be the benchmark for modern performance. The current image dataset has 1000 different classes.
- YouTube 8M — Ready to tackle videos, but can’t spare terabytes of storage? This dataset contains millions of YouTube video ID’s and billions of audio and visual features that were pre-extracted using the latest deep learning models.
Aggregators:
- Deeplearning.net — Up-to-date list of datasets for benchmarking deep learning algorithms.
- DeepLearning4J.org — Up-to-date list of high-quality datasets for deep learning research.
YouTube-8M
Datasets for Natural Language Processing
Natural Language Processing (N.L.P.) is about text data. And for messy data like text, it’s especially important for the datasets to have real-world applications so that you can perform easy sanity checks.
- Enron Dataset — Email data from the senior management of Enron, organized into folders. This dataset was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.
- Amazon Reviews — Contains ~35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.
- Newsgroup Classification — Collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. Great for practicing text classification and topic modeling.
Aggregators:
- nlp-datasets (Github) — Alphabetical list of free/public domain datasets with text data for use in NLP.
- Quora Answer — List of annotated corpora for NLP.
Datasets for Cloud Machine Learning
Technically, any dataset can be used for cloud-based machine learning if you just upload it to the cloud. However, if you’re just starting out and evaluating a platform, you may wish to skip all the data piping.
Fortunately, the major cloud computing services all provide public datasets that you can easily import. Their datasets are all comparable.
Datasets for Time Series Analysis
Time series analysis requires observations marked with a timestamp. In other words, each subject and/or feature is tracked across time.
- EOD Stock Prices — End of day stock prices, dividends, and splits for 3,000 US companies, curated by the Quandl community.
- Zillow Real Estate Research — Home prices and rents by size, type, and tier, sliced by zip code, neighborhood, city, metro area, county and state.
- Global Education Statistics — Over 4,000 internationally comparable indicators for education access, progression, completion, literacy, teachers, population, and expenditures.
Aggregators:
- Quandl — Quandl contains free and premium time series datasets for financial analysis.
- The World Bank — Contains global macroeconomic time series and searchable by country or indicator.
Zillow Real Estate Data
Datasets for Recommender Systems
Recommender systems have taken the entertainment and e-commerce industries by storm. Amazon, Netflix, and Spotify are great examples.
- MovieLens — Rating data sets from the MovieLens web site. Perfect for getting started thanks to the various dataset sizes available.
- Jester — Ideal for building a simple collaborative filter. Contains 4.1 Million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
- Million Song Dataset — Large, rich dataset for music recommendations. You can start with a pure collaborative filter and then expand it with other methods such as content-based models or web scraping.
Aggregators:
- entaroadun (Github) — Collection of datasets for recommender systems. Tip: Check the comments section for recent datasets.
Datasets for Specific Industries
In this compendium, we’ve organized datasets by their use case. This is helpful if you need to practice a certain skill, such as deep learning or time series analysis.
However, you may also wish to search by a specific industry, such as datasets for neuroscience, weather, or manufacturing. Here are a couple options:
Aggregators:
- Awesome Public Datasets — High quality datasets separated by industry.
- Data.gov — Curated government data separated by industry.
Datasets for Streaming
Streaming datasets are used for building real-time applications, such as data visualization, trend tracking, or updatable (i.e. “online”) machine learning models.
- Twitter API — The twitter API is a classic source for streaming data. You can track tweets, hashtags, and more.
- StockTwits API — StockTwits is like a twitter for traders and investors. You can expand this dataset in many interesting ways by joining it to time series datasets using the timestamp and ticker symbol.
- Weather Underground — A reliable weather API with global coverage. Features a free tier and paid options for scaling up.
Aggregators:
- Satori — Satori is a platform that lets you connect to streaming live data at ultra-low latency (for free). They frequently add new datasets.
Datasets for Web Scraping
Web scraping is a common part of data science research, but you must be careful of violating websites’ terms of services. Fortunately, there’s a whole site that’s designed to be freely scraped.
- ToScrape.com — Web scraping sandbox with two subdomains. You can practice scraping a fictional bookstore or a site that lists quotes from famous people.
Datasets for Current Events
Finding datasets for current events can be tricky. Fortunately, some publications have started releasing the datasets they use in their articles.
Aggregators:
- FiveThirtyEight — FiveThirtyEight is a news and sports site with data-driven articles. They make their datasets openly available on Github.
- BuzzFeedNews — BuzzFeed became (in)famous for their listicles and superficial pieces, but they’ve since expanded into investigative journalism. Their datasets are available on Github.
This article is a living and breathing matter. Your feedback matters to me. Please leave comments on what resources I should add and what you found the most helpful when you were learning ML/DL/AI. Thanks a lot for reading!
来源：https://www.cnblogs.com/dhcn/p/12393945.html