16:04In part 3 of this video series, learn how to evaluate time series model predictions using mean absolute error and Python’s statistics and matplotlib packages. We look at plotting the differences between actual versus predicted values, and calculate the mean …
12:19In part 2 of this video series, learn how to build an ARIMA time series model using Python’s statsmodels package and predict or forecast N timestamps ahead into the future. Now that we have differenced our data to make it …
10:34In part 1 of this video series, learn how to read and index your data for time series using Python’s pandas package. We check if the data meets the requirements or assumptions for time series modeling by plotting to see …
02:27In this quick machine learning tutorial, we introduce you to the concepts of one-versus-one and one-versus-all in classification. In classification models, you will often want to predict one class from another. This is called binary classification, or one-versus-one. But what …
03:13In this quick tutorial, we learn that machines can not only make sense of words but also make sense of words in their context. N-grams are one way to help machines understand a word in its context by looking at …
05:13We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning technique that consists of grouping data points and creating partitions based on similarity. The ultimate goal is to …
30:14Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level …
1:49:31This presentation will discuss building a business case for machine learning models. In this talk, our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model for a machine learning idea …
01:25:42From distorting experiments with systemic bias to imposing human ethics on machine learning models, data scientists have far more to worry about than the raw numbers in their spreadsheet. Join Raja Iqbal on an exploration of data science’s past ethical …
01:11:28According to some estimates, bots constitute close to 50% of the overall traffic. In this introductory talk to Feature Engineering for Bot Detection, we will cover various aspects of feature engineering for bot detection of automated web traffic. We will …
