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Big Data: 4 Manuscripts

Data Analytics for Beginners, Deep Learning with Keras, Analyzing Data with Power BI, Convolutional Neural Networks in Python
Narrated by: William Bahl
Length: 8 hrs and 37 mins
Categories: Non-fiction, Technology
5 out of 5 stars (69 ratings)

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Summary

Big Data - four-book bundle!

Book 1: Data Analytics for Beginners

In this book you will learn:

  • Putting data analytics to work
  • The rise of data analytics
  • Big data defined
  • Cluster analysis
  • Applications of cluster analysis
  • And of course much more!

Book 2: Deep Learning with Keras

In this book you will learn:

  • Deep neural network
  • Neural network elements
  • Keras models
  • Sequential model
  • Functional API model
  • Keras layers
  • Core Keras layers
  • Convolutional Keras layers
  • Recurrent Keras layers
  • And of course much more!

Book 3: Analyzing Data with Power BI

In this book you will learn:

  • Basics of data analysis processes
  • Fundamental data analysis algorithms
  • Basic of data and text mining, data visualization, and business intelligence
  • Techniques used for analysing quantitative data
  • Basic data analysis tasks
  • Conceptual, logical, and physical data models

Book 4: Convolutional Neural Networks in Python

In this book you will learn:

  • Architecture of convolutional neural networks
  • Solving computer vision tasks using convolutional neural networks
  • Python and computer vision
  • Automatic image and speech recognition
  • Theano and TenroeFlow image recognition

©2017 Anthony Williams (P)2017 Anthony Williams

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Big Data Essentilas


This book does just that. It puts the many technical bricks and otherwise organizational and managerial challenges in context, all while tying it all to real-world implementation examples.

Neural networks are in fact a various collections of algorithms and deep learning models that are loosely modeled after the structure of the human brain. Neural networks are mainly designed in order to recognize different patterns of big data. In order to do neural networks interpret different sensory data through a type of machine learning perception as well as through clustering and labeling raw inputs.

In order to use neural networks properly, you first have to ask yourself a couple of questions since various neural networks are used in order to obtain a different solutions. Therefore, depending on what outcome is relevant to you, you will use different neural networks to come to a useful solution. Deep learning techniques are all about various inputs to different outputs which outputs, which are in fact a universal correlation. Deep learning algorithms provide us these universal correlations between inputs and outputs.

One of the principal laws of machine learning states that the more data that is operable by the deep learning algorithm, the greater accuracy will be. Therefore, in other words, we can say that unsupervised deep learning has a great potential of producing highly effective and accurate algorithms.

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Kickstart for cutting edge technology

For people that is new to the data word is interesting and even easy to read. it provides a good set of recomendations dug deeper in the subject. Once the data cleaning process is done, data is ready to be analyzed. Data analysts can apply different methods and techniques which are referred to as exploratory data analytics for a better understanding of the messages and information contained in obtained data. This process of exploratory data analytics can result in some additional requests or additional data cleaning, so activities like these can be iterative in nature. Descriptive statistics like as the median or average can be generated to help better understanding of data. Also, data visualization may be used in order to examine collections of data in graphical form, and in order to obtain better insight regarding the information contained within the data. The analyst also may consider using data visualization in order to determine how to communicate the outputs. In this case, data visualization can help to efficiently and clearly communicate the information to the users. Data visualization uses charts and tables which are proper information displays and help communicate important information within the data. Tables are really helpful to a user who is looking for certain numbers, and charts can help to explain various quantitative information which is contained in the data.

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Great book a lot to learn

One of the most common buzzwords floating around online today is data analysis, and while you may have heard of it, figuring out exactly what it means might be more difficult than you might expect. The reason for this is that there are several different definitions for the phrase depending on who you ask. While it can mean more specific things in context, in general, a definition that you can work with is that it is the process by which data is modeled, transformed, cleaned and inspected by businesses, with the ultimate goal being its use in the decision-making process. As such, this makes a data analyst the person whose job it is to find the best answers to the questions that businesses come up with. They take the lines and lines of data that they find and paint a clear picture of just what it means so that those without the skills to see the pictures in the data still have a firm grasp on what is going on in the market or even with their very own businesses. The data that is analyzed varies radically based on the business that is looking and what it is they are looking for, so much so that it is currently created at a rate of more than 2 quintillion bytes each day worldwide.

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Would take other courses

This course provides a detailed overview of what someone starting out needs to learn in order to feel like they have a solid foundation to build on. A lot of Power BI courses on the Audible platform seem to break into multiple courses what Chris has explained in one comprehensive study package. I found Bahl's teaching style to be reassuring and paced very well for my comprehension. I will feel reassured coming back to review aspects of the Audios as I try some of this stuff out in the real world. Of course, it's unlikely the course will have covered everything there is to know on Power BI but again, for what it is titled as it fulfils its remit and I feel equipped to push on & learn lots more. Would take other courses by this instructor. One suggestion for future development would be for Anthony and team to put together a course to introduce more advanced concepts of Power BI use e.g. I've encountered YouTube videos or web pages showing Variables & multi-line DAX measures and also the importance of Cardinality and sometimes unavoidable many to many relationship data models. I would be keen on a course that broke down these concepts in a student friendly manner like I found this course to be

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The complex world of big data

Have you realized that it is not just advertising and marketing that organizations are taking online these days? If you consciously think about what takes you online on a daily basis, you will realize that a good part of it is business, often because you feel you can find a wider choice of items there, plenty of information about the product or service that you want, and even a wide range of pricing. The web also seems to provide much more entertainment than you could physically reach in a short time and, most probably, at a relatively smaller price. When it comes to connecting with others, individuals, private and public institutions, and all manner of organizations are taking their communication online, where they can reach a wider audience much faster and more cheaply. This book pushes the boundaries, in all respects, when it comes to anyone looking to manage massive data sets across a data lake. Using the back-propagation methods, the certain output values are being compared with the different correct answers in order to compute the certain values or already defined error-function. By different techniques, the error is further fed back through that particular neural network. Using gathered information, the model further is able to adjust weights of every connection to reduce a certain value of that error function by some other smaller amount. Highly recommended as a place to start as you start your journey in BD.

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Most effective source to absorb the knowledge!

The main aim of computer vision is to make these actions possible by computers in human alike performance. In reality, it really took more decade of dedicated research to finally discover as well as impart the ability to recognize and detect various objects to a computer having reasonable accuracy and well performance.

One of the fundamental and most basic applications when it comes to the computer vision is object detection. Further computer visions developments are in fact achieved by making enhancements on top of the fundamental object detection. Think of it as the same things occurring in real life, since every time you open your eyes you unconsciously detect various objects that are around you. Since we do this object detection unconsciously, we do not appreciate various fundamental challenges that are involved when someone tries to design a system that is similar to human eye.

However, it should be noted we are far away from the system that would be able to get close to human eye performance. In fact, the brilliance of human body was the main reason for major researchers to try breaking the computer vision enigma and their fundamental approach is based on the visual mechanics of human eye.

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Good learning experience

It was good learning experience of understand what is analytics and where to put our focus. Keep on sharing the good works.

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Very energetic and passionate

I feel the instructor is very energetic and passionate about what he does (as an instructor and a data analyst). Some parts are a bit confusing but overall the course is informative and gives you the overview of this field.

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How data really works...

Great beginner's course to understand how data really works. There are so many tools, to-do's, and what to learn next out there but this course really gives you a concise understanding of many general terms, how analyst go through their workflows and the main tools they use in their day to day work. The lectures also summarizes the people you'll be working with, the different functions in the data world, and related key functions of how you get your data and why it matters

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TAKE THIS COURSE.

You'll be happy you did. The instructors really took the time to present the information in a clear, concise way. I had questions answered that I've been trying to understand myself for a long time and many others I didn't even think about... This is a golden ticket for data

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  • James Drew
  • 10-05-20

Especially in many fields like human resources

What this means is that organizations will be able to use software like Hadoop to analyze the data at their disposal in a way that will bring future possibilities to the fore. The speed and accuracy with which data will processed will enable organizations to take prompt action where there are business opportunities emerging, where damage control is needed, and where other actions that call for accurate assessment are required. There are already big organizations using Hadoop with great success. These organizations include eBay, Twitter, Facebook, Disney, and others. The demand for Hadoop is rising rapidly. IDC, a renowned market research firm, has predicted that by 2016, the conservative worth of this software will be $813 million.

A team of researchers in 2012 used a deep neural network in order to predict the biomolecular target of a certain drug. Later in 2014, researchers used deep learning neural network in order to detect the toxic effect and off-target effects on an environmental chemical in food, various households products, and particular drugs. Researchers improved deep learning neural networks for various drug discovery. They combined data from various sources. For instance AtomWise in 2015 introduced the first learning model which is used for structure-based drug design. This model is called AtomNet and later was used to make various predictions of novel biomolecules for different disease targets like multiple sclerosis and Ebola virus. Various autoencoders are used in bioinformatics in order to predict gene ontology and gene-function relationships. Also in medical informatics, neural network models are used in order to predict sleep quality which is based on various data from wearables. Electronic health record model which is based on the deep neural network is used to predict various health complications.

As previously explained, there are many innovators trying to create analytical and data management tools that will turn massive data into an economic advantage. In 2012, the big data market was worth $5 billion. If you remember what we said earlier that big data has been growing exponentially, you will not be surprised to know that if the trend today continues, the market for big data is projected to reach $50 billion by 2017.

Highly recommended reading, offered at an unbeatable price.

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  • Sidney Shook
  • 11-05-20

You can boast about this book!

A great primer and reference for Hadoop and Big Data beginners. Not a programming or configuration reference, but you will learn how traditional and modern data systems have come about, and how they are used. A great way to get up to speed on the modern jargon surrounding Big Data, plus you get enough context that you will figure out what book (if any) you need to read next for your needs. Data visualization is another data analytics method often referred to as equivalent to visual communication. Data visualization involves the designing and study of various visual representations of data gathered. In other words, data visualization means that information that has been gathered is further abstracted in schematic form which includes variables and attributes for each unit of information. The main goal of data visualization is to gather information and communicate efficiently and clearly via various plots, statistical graphs, and information graphics. In data visualization, numerical data also can be encoded using lines, bars, and dots in order to communicate visually any quantitative message. Effective and clear data visualization also helps users to analyze any evidence and data and even more complex data collections are very accessible, usable and easily understandable. It should be noted that data visualization is at the same time both science and are, often referred to like a certain branch of statistical description. Data visualization is also grounded theory of developing various data analytic tools as well. Data created by various Internet activity is rapidly increasing, and an expanding number in the environment of various sensors is referred to Internet of things or big data also used by data visualization.

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  • Dollie Ring
  • 12-05-20

Years of experience in one book!

I work in this field, and I am glad to have their excellent "Big Data for Beginners" on my bookshelf. It includes plenty of practical guidance for practitioners, and ample context for tech and business readers who may be new to the topic. I recommend it without reservations.

Deep learning methods lie under an assumption that hidden level representations in gathered data are in fact, generated by the relationship and interaction of various features of these layers. Also further, there is an assumption that these certain layers and their features, in fact, correspond to multiple levels of composition and certain levels of abstraction. It should be noted that a different number of layers as well layer sizes might provide a wide range of different structures and abstractions
Deep learning computational theory is greatly concerned with these training classifiers when it comes to the very limited amount of data, which is available. It is commonly stated that neural networks will perform well when it comes to the generalizing examples that are not within the training data collection. Another common problem of the backpropagation structures is, in fact, the speed of data convergence as well as the possibility of getting to a local minimum of that error function.

So think of neural networks as a small enactment of other scientific methods like delivering hypothesis, which requires constant and repeated trying just as neural networks perform. In feed-forward neural networks, the first input will be entering the network. In the end, certain coefficients, as well as weights, will be placed. A collection of inputs is also at the end.

This is a great read for anyone either planning on building a data lake for the first time or struggling to derive value from a data lake that’s already been implemented. The author provides an overview of the benefits of a data lake (in addition to relevant terminology) but then wastes no time discussing all that must go into the planning, implementation and management phases to ensure your data lake is a success. He spends a great deal of time talking about self-service business intelligence, which is the Holy Grail for any enterprise that wants to more effectively compete as a data-driven organization.

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  • Mandy Palmer
  • 08-08-19

Best practical course

This is definitely one of the best practical courses available on Audible. This course perfectly combines enough theory and practice. And it is really great that high-level API such as Keras has been chosen. Bahl has a unique talent to explain complex things clear and illustrate them with the relevant code. I really hope for the following course on advanced topics =) Best regards

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  • Darnell
  • 08-08-19

Really helps you

This guy has practical experience in the subject, as well as practical teaching experience in the subject. He went over all the pitfalls I had, and really helps you hit the ground running.

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  • Roberta
  • 08-08-19

This one delivers

Like pretty much every course made by Lazy Programmer, this one delivers, and then some. It was a great match for me: I do not intend to become a big data engineer, yet I am aware I need to learn and practice with the Data Analytics tools available.

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  • Sidney
  • 13-05-20

Compelling read on how to achieve

So, after you are done with reading, you will be able to create your first neural network model using Keras, you will get familiar with the practical usage of Keras, and its implementation and you will see what are some of the realworld examples of Keras and what is its overall influence in deep learning studies. The main features of Keras are implemented from commonly used neural network building blocks like objectives, layers, activation functions and optimizers. With Keras it is easy to work with text and image data since it features a host of tools suitable for any beginner. Facts are speaking for itself, Keras is the second-fastest growing framework, and that's not surprising. Keras is used to solve problems without having to interact with the TensorFlow or Theano, which are underlying backend engines. So, without any interaction with the background, end-to-end problems can be solved. Initially, Kears was developed on top of Theano, but shortly after the release of TensorFlow, Keras added it as backend. As new generation computation graph engines are designed, Keras will extend to support those as well in the future. Keras is not dependable on backend engines since it features its built-in graph data structure which can handle computational graphs. Therefore, it doesn't rely on TensorFlow's or Theano's native graph data structure.

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  • Williams
  • 14-05-20

Ingenious! How to to do big deal and think step

Data analytics is closely connected to data visualization. Data integration and data dissemination. Therefore, term data analytics is often used to describe any data modeling. Text analytics term is also known as text mining is the process of gathering high-quality various information from any text source. This high-quality information gathered from the text are typically derived through the devising of various trends and patterns which correspond to different terms including statistical pattern learning. Data analysis is breaking a whole into its components which will be further examined separately. Data analytics is, in fact, a process for obtaining necessary data components and transforming it into various information which is useful and significant for any decision-making. In other words, any data is analyzed with the main aim which is to answer important questions, to test a certain hypothesis or to disprove theories. Data analysis applications are used to improve and measure the performance of past and current business operations. They use collections of past data in order to provide tools and information which are useful to business users, and that will let them make significant improvements. Levels for business analytics are operational reporting, business dashboard, analytic application and analytics reporting.These applications of data analytics may further extend to a domain of predictive analysis. Business analytic applications mostly relate to analyzing business processes important in support of users decision making. For instance, a business analytic application may relate to various sales analysis, risks involved in profitability analysis or accounts analytics.

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  • Marisol
  • 08-08-19

Bn a brief format

This was a great introduction to SQL. I took it as a primer to get me ready for a certification at work and it was the best way to reinforce the basics in a brief format.

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  • Sean Johnson
  • 08-08-19

Vitals of keras

This is an awesome course covering the vitals of keras. A great course to explain the incomprehensible details in DL.

10 people found this helpful