Given a set of incomplete data, consider a set of starting parameters. Expectation step (E – step): Using the observed available data of the dataset, estimate (guess) the values of the missing data. Maximization step (M – step): Complete data generated after the expectation (E) step is used in order to update the parameters. Repeat step 2 and step 3 until convergence.
The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.
How to begin when you don’t know where to start on Machine Learning?
This article is on where to start with Machine Learning.
Thank you. Very good noticeable point. Artificial intelligence is the prerequisite of machine learning.
Understanding why Machine Learning?
Before getting into the definition, let’s first understand why we need Machine Learning and how it came into existence.
Great work. Liked its flow and thanks for writing this down.Like I said The only difference is that earlier was used and known to very few people and it was very time consuming. I
Machine Learning Algorithm
Machine Learning Algorithm used most commonly and the Types of ML. Just a short-notes on these.
How many algorithms are really put in use? What are the best practices? a great note and points to reflect
Understanding Analytics tools and their usage.
Understanding Analytics and Data analytics is also helping businesses to predict problems before they occur and map out possible solutions. - Quite a point
Hands-On with First ML Model
Thinking about using a Machine Learning model? Get your hands dirty with a practical lesson on SKLEARN.
you'll need a problem statement, data to process and enough time to try different approaches without deadlines pressing on you. - Great point
SQL for Data Science
Learn the significance of SQL in Data-Science and Machine Learning. And understanding the basics of SQL.
Together with Python and R, SQL is now considered to be one of the most requested skills in Data Science. - Absolutely agree
Pivotal Data Labs Helps Predict Viewer Behavior and Build a Better TV Show
Media companies have long been interested in viewer habits and preferences. In the past, they were forced to rely on traditional sources of data to determine this information, which would then be used to help formulate the next series of TV shows, many of which would ultimately “flop.” However, the rise of unstructured data has given media companies and TV executives the means to dig deeper and make better decisions.
In the past, they were forced to rely on traditional sources of data to determine this information, - Quite a point
Mosaic Data Science Delivers Optimized Shipping Solutions for Airfreight Client
The field of logistics is vast, indeed. Shipping, particularly by air, is a process fraught with potential problems, headaches and hassles for both logistics companies and their clients. Mosaic Data Science was able to find a solution to one unnamed airfreight client’s situation through the use of modern data science techniques.
Shipping, particularly by air, is a process fraught with potential problems, headaches and hassles for both logistics companies and their clients. - Quite apoint
Data Science Allows Fashion Industry to Gain a More Complete Understanding of Consumers
The fashion industry is pretty visible in terms of high-profile events held in cities like New York or Milan. Fashion designers drape models with their latest creations, and then those models stroll down the catwalk while photographers turn night to noon with flashes. However, while that might be the public image of the industry, fashion companies face significant challenges. Rather than leading consumers with new designs and styles, these companies still must understand their audience, what drives them, and what consumer behaviors affect their success. Data science is being used to do just that.
Data Science Allows Fashion Industry to Gain a More Complete Understanding of Consumers article nicely explained.