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Data Mining Coursera

@machinelearnbot

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. You can apply to the degree program either before or after you begin the Specialization.


Hands-on Text Mining and Analytics Coursera

@machinelearnbot

About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.


Probabilistic Graphical Models 2: Inference Coursera

@machinelearnbot

About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the second in a sequence of three.


ElasticSearch and Lucene for Apache Spark and MLib

#artificialintelligence

Elasticsearch is a search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java and is released as open source under the terms of the Apache License. Official clients are available in Java, .NET (C#), Python, Groovy and many other languages. Elasticsearch is the most popular enterprise search engine followed by Apache Solr, also based on Lucene.


Sell Your Expertise by AI Chatbot - Basic Concepts

#artificialintelligence

If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.


Natural Language Processing with Deep Learning in Python

@machinelearnbot

In this course we are going to look at advanced NLP. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. In this course, I'm going to show you exactly how word2vec works, from theory to implementation, and you'll see that it's merely the application of skills you already know. We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.


Java Data Science Solutions - Analyzing Data - Udemy

@machinelearnbot

If you are looking to build data science models that are good for production, Java has come to the rescue. This unique video provides modern solutions to solve your common and not-so-common data science-related problems. We start with solutions to help you obtain, clean, index and search data. Then you will learn a variety of techniques to analyze data. By the end of this course, you will be able to perform all advanced operations it takes to analyze the complexity of data and to perform indexing and search operations.


Java Data Science Solutions - Big Data and Visualization

@machinelearnbot

With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This course will help you to learn how you can retrieve data from data sources with different level of complexities. You will learn how you could handle big data to extract meaningful insights from data. Later we will dive to visualizing data to uncover trends and hidden relationships. Finally, we will work through unique videos that solve your problems while taking data science to production, writing distributed data science applications, and much more--things that will come in handy at work.


Learning Path: Julia: Explore Data Science with Julia

@machinelearnbot

Almost all companies these days are investing thousands of dollars in data analysis to get their data analyzed. Well, in fact studies say that there are around 73% of organizations have invested in Big Data. Why do you think that is the case? What can you reap of the data, ideally just 1s and 0s? Moreover, how does this data help an organization's future?