Learning Management
Google and Coursera launch a new machine learning specialization
Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus. The new specialization, called "Machine Learning with TensorFlow on Google Cloud Platform," has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.
Machine Learning with Python and scikit-Learn: 3-in-1
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This comprehensive 3-in-1 course is your one-stop solution to everything that matters in mastering machine learning algorithms and their implementation. Develop pipelines and process data through manipulation, extraction, and data-cleansing techniques. Learn clean coding techniques which are applicable to any scalable Machine Learning projects.
Learn Data Science with Python Udemy
Python is a popular general purpose programming language used for both large and small-scale applications. Python's wide-spread adoption is due in part to its large standard library, easy readability and support of multiple paradigms including functional, procedural and object-oriented programming styles.This Course follows pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries. By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.
New Product Forecasting Using Machine Learning Udemy
All businesses introduce new products for various reasons. The new products poses challenge for the planners and marketing executives to estimate the demand for them for merchandise and supply planning purposes. The primary reason being the lack of historical data that can be used for forecasting. These techniques are'By Analogy' and'Bass Diffusion' including a live demonstration using a planning software. While Analogy is the more popular technique, the issue most planners face in this technique is in choosing the right analogue product.
The Four Keys to Natural Language Processing Udemy
With the acceleration and growth of technology we are in a new age. With technology like Natural Language Processing we are able to create and build applications that can change the world. We have the tools in the palm of our hands just need to understand how use them. Learn and build applications on one of the most cutting edge technology fields today with this easy to understand course on the Four Keys to Natural Language Processing. With this course you will be able to understand why NLP is a driving force to change the way we interact with computers, learn from data, and solve problems.
Data Visualization with Python: The Complete Guide
Data is becoming a force to recon with. With the amount of data that is being generated every minute, dealing with data has become more important. The importance of data lies in the fact that it allows us to look at our history and predict the future. Data Science is the field that deals with collecting, sorting, organizing and also analyzing huge amounts of data. This data is then used to understand the current and future trends.
Learning Path: Data Science With Apache Spark 2
The real power and value proposition of Apache Spark is its speed and platform to execute data processing and data science tasks. Let's see how easy it is! Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.
AI and big data have huge potential for China's edtech market: Ellabook ยท TechNode
Ahead of the event in May, we are taking a look at some the companies and people who are taking part in the massive unconferenceโan open space event with organization powered by participants. TechNode is organizing the Explore Expo, an exhibition area for young tech startups looking for exposure. The education industry is generally viewed as traditional, dogmatic, and oppressive in many Asian countries, especially in China. As China's edtech sector takes off and begins to attract a deluge of investment, tech companies are exploring more ways to spice up the learning experience. "The compulsory education system is rigid," Chu Liang, CTO of Ellabook, told TechNode, "but over the past decade, technology has been transforming many industries and sectors. Ellabook (ๅฟๅฆ็ไนฆ) is an ebook reading platform, like Kindle, but for kids from 3 to 12 years-old. The app is animated and interactive, which encompasses a wide range of learning categories like reading skills, English, mathematics, and art.
From 0 to 1 : Spark for Data Science with Python
This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
Applied Statistical Modeling for Data Analysis in R
The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects immediately! TAKE ACTION NOW:) You'll also have my continuous support when you take this course just to make sure you're successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you're not completely satisfied with the course.