NumPy — Numpy Natural Language Processing - Stanford University About the Course This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning. We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford's exciting forays into large scale online instruction. Recommended Background No background in natural language processing is required. Suggested Readings
Intro to Descriptive Statistics Lesson 1 : Intro to Research Methods You will be introduced to several statistical study methods and learn the positives and negatives of each. Lesson 2 : Visualizing Data You will learn how to take your data and display it to the world. Lesson 3 : Central Tendency In this lesson you will learn to compute and interpret the 3 measures of center for distributions: the mean, median, and mode. Lesson 4 : Variability You will learn how to quantify the spread of data using the range and standard deviation. Lesson 5 : Standardizing You will learn how to convert distributions into the standard normal distribution using the Z-score. Lesson 6 : Normal Distribution You will learn how to use normalized distributions to compute probabilities. Lesson 7 : Sampling Distributions You will learn how to apply the concepts of probability and normalization to sample data sets.
Jupyter and the future of IPython — IPython Data Science specialization Inferential Statistics: Learn Statistical Analysis Inferential Statistics is a continuation of the material covered in Descriptive Statistics, and so lesson numbers follow from that course: Lesson 8: Estimation You will learn how to estimate population parameters from sample statistics using confidence intervals and estimating the effect of a treatment. Lesson 9: Hypothesis Testing You will learn how to use critical values to make decisions on whether or not a treatment has changed the value of a population parameter. Lesson 10,11: t-tests You will learn how to test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes. Lesson 12,13: ANOVA You will learn how to test whether or not there are differences between three or more groups. Lesson 14: Correlation You will learn how to describe and test the strength of a relationship between two variables. Lesson 15: Regression You will learn how to describe the way in which changes in one variable are related to changes in a second variable.
Home — TensorFlow In-depth introduction to machine learning in 15 hours of expert videos In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. (Update: The course will be offered again in January 2016!) If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. P.S. Chapter 1: Introduction (slides, playlist) Chapter 2: Statistical Learning (slides, playlist) Interviews (playlist)