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As per the study done by Adobe Analytics, usage of podcast mobile app has increased 60% since January 2018.

The study says that the usage of the app is likely to grow even further as 45% of listeners plan to tune into more podcasts in the future.

The study, which consisted of 193 million monthly exclusive visitors to U.S. mobile apps, discovered that 41% of podcast finding happens through online sources such as blogs and articles.

According to the report, almost 72% of respondents notice podcast quality to be on the increase, with just 6% think that quality is declining. Nearly 52% of respondents said they listen to podcasts while working or traveling.

According to the study, education, history, and documentary were found to be the four most popular genres, while video games were among the least popular.

The majority—60%—of listeners said that only after hearing it on a podcast did they look up a service or product, with 25% reporting that they ended up making the purchase. Nevertheless, 58% of respondents said they avoided podcast ads.

The podcast sector’s impetus raises the feasibility of podcast advertising to reach a mounting audience. And, since more data is on hand from diverse platforms like Spotify, advertisers have more targeting skills to help them get closer to the listeners that are almost certain to be interested in their products or services.

Machine Learning which leads all other jobs among technology world. Data Science and Machine Learning are playing a boss role for all other technologies. Be it restorative or building it is adaptable to serve all sort of fields. To deal with Data Science, we should have the top Machine Learning tools, software, and frameworks to work on. These tools should give the outcomes which lead to 100% accuracy as we have an immense measure of training data which may be totally messy (in most cases). What’s more? These tools give the best results when we build well-defined software for them. However today, a machine can act by itself and is working well in this environment. This fruitful change is only achievable by making machine learning tools much effective. There are a lot of machine learning tools for beginners. The absolute best machine learning software and tools to learn in 2019 are mentioned below irrespective of any ranking:

1. Google Cloud ML Engine

Data, if look closely it has detailing in it and if talking about individual’s data all around the world then you have millions and billions of training data. Your PC will not be able to execute this much long listing or even your algorithm doesn’t work here. What will you do now? Here comes a tool named as Google Cloud ML Engine. This tool can let your data train the way you want. Data scientists run their high-quality machine learning models using this tool.

Key Features:

• Gives ML model building, predictive modelling, training, and deep learning.
• Prediction and training can be used in both ways either jointly or independently.
• Google Cloud ML Engine is utilized by the ventures to work on client’s email or messages or to detect the clouds you have in the image captured by satellites.
• It very well may be utilized to prepare an unpredictable and complex model.

2. Amazon Machine Learning (AML)

A cloud-based machine learning software which is strong and easy to use by the developers working on different levels. To generate predictions and building high-quality machine learning models, developers are using this tool. Data integration is done by coordinating different sources like Amazon S3 and more.

Key Features:

• The highlighted features AML provides are the visualization tools and wizards.
• Binary classification, multi-class classification, and regression models are utilized.
• MySQL database utilization to create data source object.
• Amazon Redshift is another source provided where data is stored from which you can create data source object.
• Evaluating the concepts of Data sources, ML models, Evaluations, Batch predictions, and Real-time predictions.

3. Apache Mahout

A simple framework works on linear algebra and expressed in Scala DSL. A free and open source undertaking of the Apache Software Foundation. This system works on the objective of implementing the algorithm rapidly for information researchers, mathematicians, and analysts.

Key Features:

• Helps in building adaptable algorithms by the framework which is designed to allow new capabilities and functionality.
• Perform machine learning techniques, some of them are clustering, classification, and recommendation.
• It incorporates matrix and vector libraries which helps in handling the data easily.
• It used the paradigm of MapReduce and Apache Hadoop.

4. Accord.NET

Another machine learning framework which work on both audio and image processing libraries written in C# is Accord.NET. It comprises of different libraries having a wide scope of utilization incorporating linear algebra, pattern recognition, and statistical data processing.

Accord.Math, Accord.Statistics, and Accord.MachineLearning are the libraries working for Acoord.NET.

Key Features:

• Accord.NET helps in multiple departments like signal processing, computer vision, computer audition and statistics applications.
• Comprises of in excess of 40 parametric and non-parametric estimation of statistical distributions.
• Having hypothesis testing ability which includes more than 35 tests like non-parametric tests, one way and two-way ANOVA tests, and more.
• Having more than 38 Kernal functions.

5. Shogun

Shogun, an open source machine learning library created in 1999 by Soeren Sonnenburg and Gunnar Raetsch. A C++ written tool which solves machine learning problems by the algorithms and data structures it provides. It bolsters numerous languages like Python, R, Octave, Java, C#, Ruby, Lua, and so on.

Key Features:

• The development of this tool is done for large scale learning.
• It centers around Kernal machines like support vector machines for classification and regression problem.
• It helps in connecting other machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, and more.
• Python, Lua, Octave, Java, C#, Ruby, MatLab, and R can use the interface of this tool.
• Data that can be processed on this tool can be in million samples.

6. Oryx 2

An acknowledgment of the lambda engineering. Oryx 2 is based on Apache Spark and Apache Kafka. It is utilized for ongoing enormous machine learning works. Oryx 2 works for developing applications like end-to-end applications for filtering, classification, regression, and clustering. Oryx 2.8.0 is the latest version running in the market.

Key Features:

• Updated version of Oryx 1 with more advancements to lead the data with accuracy.

Tiers:

• Generic lambda architecture tier
• Specialization on top providing ML abstractions
• End-to-end implementation of the same standard ML algorithms

Layers:

• Batch layer
• Speed layer
• Serving layer

Also a data transport layer is there for transferring the data among layers to receive the input from external sources.

7. Apache Singa

In 2014, Apache Singa came into existence by DB System Group at the National University of Singapore in a joint effort with the database group of Zhejiang University. This software used for Natural Language Processing (NLP) and image recognition and bolster a wide scope of mainstream deep learning models. This software comprises three fundamental parts: Core, IO, and Model.

Key Features:

• Adaptable engineering for versatile distributive training
• Tensor abstraction is taken into account further developed machine learning models
• This device incorporates upgraded IO classes for composing, learning, encoding and decoding documents and information
• This device incorporates upgraded IO classes for composing, learning, encoding and decoding documents and information
• Device essential featuring is supported for running on hardware devices

8. Google ML Kit for Mobile

It is safe to say that you are a versatile developer? No worries. Google’s Android Team brings an ML KIT for you which bundles up the machine learning aptitude and innovation to grow progressively robust, customized, and advanced applications to keep running on a gadget. You can utilize this instrument for face and image recognition, image labelling, locating places and standardized identification examining applications.

Key Features:

• It offers ground-breaking advancements
• Run on a specific device or in the devices connected with cloud system
• Works on the solutions by making custom models

9. Apple’s Core ML

To integrate machine learning models into your app another machine learning framework is playing a role known as Apple’s Core ML. Use your ML model by dropping it’s file into your project and the Objective-C or Swift wrapper class consequently is formed by the Xcode.

This software provides maximum execution and extreme performance on every processor you are using (CPU and GPU).

Key Features:

• Goes about as an establishment for the systems and functions that are domain-specific
• On-device performance is optimized at maximum level
• It is easy to understand by low-level natives

10.  TensorFlow

TensorFlow is another big name known by every machine learning expert. Created by Google for building ML models on its open-source platform. It has an adaptable plan of libraries and assets that enable developers to create applications.

Key Features:

• A listing of deep learning mechanism
• Exceedingly adaptable open-source software
• Using high-level APIs to train and execute the ML models
• Making numerical calculations easy by data flow graphs

Some new content through the Web is not appearing in search results as Google is confronting problems related to index today. For instance, a quick glance at the Top Stories in Google News reveals that some of the stories are from the last few hours, but many are from yesterday. The issues were first reported by Search Engine Land.

On Thursday, the company established that it was reviewing reports of indexing problems, later adding that it was seeing “issues in the URL Inspection tool within Search Console.”

According to Google, the URL problem was rectified as of 11:41AM ET, but no update has been provided on indexing. Google announced that it is working to fix the issue.

To find new text to display users, Google is continually crawling the internet. When Google undergoes issues with indexing, like it does today, search results won’t be as accurate as users expect from tech behemoth.

Of late, Google has faced a slew of indexing issues, including problems that lasted for a week in April and more than three days in May.

Augmented analytics solutions have gained traction in the business intelligence world. Augmented analytics is intended in practical terms to facilitate more development and help create more revenue.

 According to Gartner:

“Augmented Analytics Is the Future of Data and Analytics”

“Augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management and deployment.”

In simpler terms, everything related to the process of analytics and business intelligence will be changed by artificial intelligence analytics by simplifying or eliminating some steps and thoroughly changing and improving the others.

How is Augmented Analytics Different than a Typical Business Intelligence Model?

If you compare augmented analytics with a usual business model, data analysts reach data through testing their hypotheses and theories while operating on a principle of knowledge. Although, we cannot doubt the knowledge of data analysts, but their viewpoints will always limit people in some capacity. It is quite difficult to show a thorough, unbiased and entirely correct conclusion without being aware of every factor that might influence the results. This implicates a lot of business are potentially working with limited views of their data landscapes, leaving money on the table.

But if we talk about augmented analytics, it invokes the power of machine learning to process way faster than humans can.  As machine learning has a very little human interference which makes algorithms uninclined to the biases.

Augmented analytics, therefore, empowers business users to perform on the insights they receive, allowing data scientists to concentrate on much more complicated queries.

How Can Augmented Analytics Add Significance to Business Intelligence?

Augmented analytics approach to business intelligence has countless advantages and, with the right self-serve analytics and data discovery tool, the average enterprise can expand these benefits and add even more value. We are listing some of the benefits to augmented analytics approach to business intelligence. Or in other words, here are just a variety of ways that self-serve augmented analytics can add value to business intelligence.

Sets Focus

Augmented Analytics solution helps analysts, data scientists, and inner IT staff to center on crucial projects as well as offer support to strategic problems which results in releasing them from centering on the business community’s day-to-day analytical requirements.

Empowers Business Users

As augmented analytics provide critical information itself, it enables business users to put a focus on their goals. Also, it provides objective metrics and allows data sharing which advance business interests.

Easy to Use Interface

In an easy-to-use interface, it offers complex, advanced techniques and tools to collect data from disparate data sources and enable confident, precise and accurate decisions to be made.

Simplified Toolkit

With augmented analytics simplified tools you can improve user adoption, data sharing, data popularity advancement, the integration of social BI within the organization and data and metrics literacy.

Enhanced ROI and TCO

Augmented Analytics delivers immediate, objective results and enhances ROI and TCO.

Apt Business Forecast

Another business benefit of this approach is perfect business forecasting and predictions. Augmented analytics offers metrics so that one can be sure that the right decisions are made and that the business takes suitable action with regard to products and services, pricing, competition, and other key business factors.

Augmented Analytics Tools and BI

In order to automate the operations of data, augmented analytics uses the power of machine learning which helps find insights as well as share the insights for business users, operational staff and data scientists.

Augmented data discovery, augmented data science and machine learning, and augmented data preparation are counted among the key capabilities of augmented analytics tools. Augmented data preparation has its focus on automating data intake into analytics systems in a system that involves data modelling, metadata adding, data profiling, data quality assurance, and catalogue storage. With augmented data discovery, relevant data is provided to users via automating, visualizing and narrating relevant findings. The skill-gap needed for model building to test new hypotheses or write algorithms can also be attained with the help of machine learning. Startups and big vendors have the potential to disrupt data integration, leading BI and analytics, data science and embedded data analytics vendors.

A more precise take on Augmented Analytics Tools

In their interaction with top BI vendors, an analytics manager must consider supporting the following five augmented analytics capabilities:

• It has been predicted by Gartner that by 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms. So, users must be able to use the power of text-based-voice enabled technology in order to interact with data in a conversational mode.

• BI tools with augmented analytics functionality should be able to use the power of language to narrate performance results in an interactive way leaving behind complexities.

• The system should be easily able to forecast; what is the best visual for specific data, how to enrich data for deeper analysis and understanding, and how to clean and plan data for business use. This also comprises the use of algorithms to train predictive models for all types of cases of use, such as churn, attrition or behavior.

• Augmented analytics tools should be able to drill more profoundly into what drives particular performance. These tools should be able to bring in their own insights to the process to help their hypotheses by seeking out results. The tools need to identify the behavior of outliers differently from the expected outcomes. Furthermore, describing the data, identifying key drivers and explaining what segments affect the results are essential for these algorithms.

• These tools must have the ability to predict and predict a trend; recognize cluster groups and outliers by clicking a button like values. Additionally, this process involves the use of algorithms to train predictive models for all types of use cases based on churn, attrition or user behavior.

According to the company statement, technology colossus Google is closing its Trips app for cellphones. However, the company is integrating much of the functionality from the service into its Maps app and Search features.

While today is the last day for support for the app, information such as notes and saved places will be on hand in Search so long as a user signs into their Google account.

If you want to find popular destinations, events, and other attractions, you can search for “my trips” or go to the revamped Travel page on Google.

In September 2018, Google came up with changes, which comprised several of the features that had been broken out into the Trips app.

Users will shortly be able to add and oversee notes from Google Trips in the Travel section on a browser and find saved attractions, trips and hotels for imminent and previous excursions.

In Maps, you can search a destination or find particular legendary places, guide lists, events or eateries by swiping up on the “Explore” tab in the app.

And you can go to destinations you have used under the “Your Places” section by tapping the menu icon. The maps app will soon also include forthcoming reservations planned by trip which will be on hand offline, obviating the need to download them.

It’s reported that Huawei will first boast Hongmeng OS at its developer conference, which will commence this week on Friday, August 9th, in Dongguan, China.

The report compares Hongmeng OS to Google’s long-in-the-works Fuchsia, which is likewise a new operating system designed to run on various form factors.

It’s said that Hongmeng OS is also built around a microkernel so it can effectively accommodate artificial intelligence and can run on numerous platforms.

Nevertheless, it’s also said that a Hongmeng OS smartphone is very much happening and already involved in being tested.

The first device could unveil in conjunction with Huawei’s Mate 30 Pro flagship later in the year, with a release date set for the fourth quarter. Nevertheless, the phone is likely to target the lower- and middle-income segments, with pricing set at around 2,000 yuan (~$288).