Art and Science of Machine Learning

Course Link: https://www.coursera.org/learn/art-science-ml

Art and Science of Machine Learning
This course will cover the art and science of machine learning in high school science. We will learn about the computational methods used to design and train models for supervised learning and unsupervised learning in Python. We will also cover the basic techniques for modeling and exploring the data, as well as the types of models that are used. We will also cover the “meat” of the course, modeling and exploring the data. We will cover modeling problems in Python, introducing conditional and unconditional rules, as well as an introduction to supervised learning. We will learn about the state-of-the-art in Python models and how to use them in the real world. We will cover modeling, unsupervised learning, and exploration of the data, with an emphasis on modeling problems that arise from the data itself. We will cover modeling problems in Python, introduction to supervised learning, and an introduction to unsupervised learning. We will cover the state-of-the-art in Python models and how to use them in the real world. We will cover modeling, unsupervised learning, and exploration of the data, with an emphasis on modeling problems that arise from the data itself.

Prerequisites

Learners should be comfortable writing machine learning models using Python, having some basic knowledge of Python programming and statistics, and have previous experience with Python programming (including working with NumPy and Pandas). We will assume that learners are comfortable working with the typical “programming the same thing over and over” routine that is common in machine learning. This means you should be comfortable writing machine learning code in Python, and should have experience working with data in a NumPy/Pandas environment.

Suggested Read

C++ Programming, Chapter 9.3, Part 1, “Introduction to C++” by Michael F. Neibut (NIIHB, C++AM)

This course has been designed to be enjoyable even for those that have mastered advanced computer science. Much of the material will still be familiar to those of you that have taken introductory courses in computer science, but the course will challenge you to think critically and creatively about the issues and problems that you will face in the course. The course will also give you the opportunity to practice and to share your own approaches to the problems that you will solve.

The course has been divided into 4 sections: (1) Machine Learning, (2) Convolutional Neural Networks, (3) Regularization, and (4) Optimization. Each section is introduced and completed using examples from a wide range of fields. The final section of the course provides an overview of the field and a review of what is to come.

The course is intended for advanced computer science majors and high-school students. It will be challenging at first but rewarding after you master the material and become familiar with some of the key concepts

Course Link: https://www.coursera.org/learn/art-science-ml

Browser-based Models with TensorFlow.js

Course Link: https://www.coursera.org/learn/browser-based-models-tensorflow

Browser-based Models with TensorFlow.js
This course is all about how to build Hadoop clusters on a laptop, using the TensorFlow library (Squirrel/MongoDB). We’ll use the pipelining technique to deploy your projects to Heroku, and use the Elastic MapReduce framework to run your computations in parallel. It will also cover the basics of web services and how to use Docker for containerized container hosting.

This is the second course in the Data Analytics for Business specialization. In the first course, you were introduced to tabular data and how to manipulate those tabulars. In this course, you learn how to use a GPU to compute a result for your data in a way that is efficient and suitable for transmission. You also learn how to use a CPU to run your computations and ensure that your data are protected. You’ll learn all of these things by looking at the benchmarks.

You’ll need some computing power to run all of the examples and benchmarking tasks, but you’ll get plenty of practice in how to use a GPU to serve you up the results. The course will also focus on practical issues in data science that are likely to come up in many practical data science jobs, such as how to secure your data, how to collect data in a reliable way, and how to make sure that your data are processed by the correct tools.

Be sure to follow us on Twitter and Facebook!
https://twitter.com/DataAnalecta/status/869682435493632

https://www.facebook.com/groups/datassage-machine/
=======================================================================================================================
Technology and Knowledge Areas
=======================================================================================================================
Machine Learning and Neural Networks
Machine Learning and Neural Networks. This course introduces the concepts and applications of ML for machine learning, and delves into the algorithms behind ML techniques used for image recognition, text prediction, natural language processing and general understanding of machine learning. We’ll also look at the challenges of using ML for machine learning, and review the areas of expertise of machine learning researchers. We’ll also look at deep learning and deep learning applications that are likely to emerge in the coming years.

Deep learning and deep learning are areas of research that are likely to emerge in the coming years. Machine learning is broadly considered to be one of the most exciting areas of Research Excellence in the world, but currently there is little evidence that deep learning are actually making big advances. Deep learning are processes that rely on very large amounts of data, and much more research needs to be done in this area before they can be considered serious threats to our ability to make accurate predictions using existing data sets.

We’ll continue our look at ML discoveries, and look at both the real and theoretical foundations of ML progress, as we move on to deep learning for image and text detection, natural language processing, and text classification. We’ll also look at recurrent neural networks

Course Link: https://www.coursera.org/learn/browser-based-models-tensorflow

Convolutional Neural Networks

Course Link: https://www.coursera.org/learn/convolutional-neural-networks

Convolutional Neural Networks
This course covers the convolutional neural network techniques and algorithms that are used in many computer vision, audio, and audio-imaging applications. It is designed for students who are comfortable with C++, C and C++AM, as well as others who want to learn how these algorithms work from a practical perspective.

The course assumes some prior knowledge of machine learning, but it is possible to learn these techniques without. For those of you who are curious about deep learning, or for anyone who wants to understand how deep learning works, this course will take you on a tour of the deep learning space.

Before diving into the details of the neural network, I recommend taking a few minutes to explore the NN in action. You can find a larger collection of code in the convolutional neural network library as well as in the training and test datasets used in this course here on Github.

The course assumes that you already know how to use C++ and C++AM, and most of the algorithms in this course are adapted from the Convolutional NN library used in the previous course in this specialization. If you are not sure, feel free to skip ahead or take the first course in this specialization.

This course is part of the fMRI to ML pipeline, a project that I started while I was at University of Illinois Urbana-Champaign. The inspiration for this course came from personal experience using the convolutional neural network in my own research, and from a project I started at the University of Illinois. The name of the project refers to the spot on the floor where the line of sight between two objects would be the shortest.

The course is designed to accompany Convolutional Neural Networks: https://www.coursera.org/learn/coroutines-latin-america-17011601

You can find a smaller collection of code in the convolutional neural network library here: https://github.com/olafbauer/coroutines-losososos

The inspiration for this course came from personal experience using the convolutional neural network in my own work, and from a project I started at the University of Illinois. The name of the project refers to the spot on the floor where the line of sight between two objects would be the shortest.

The course is designed to accompany Convolutional Neural Networks: https://www.coursera.org/learn/coroutines-losos-17011601

You can find a smaller collection of code in the convolutional neural network library here: https://github.com/olafbauer/coroutines-losos-17011601

The inspiration for this course came from personal experience using the convolutional neural network in my own work, and from a project I started at the University of Illinois. The name of the project

Course Link: https://www.coursera.org/learn/convolutional-neural-networks

Convolutional Neural Networks in TensorFlow

Course Link: https://www.coursera.org/learn/convolutional-neural-networks-tensorflow

Convolutional Neural Networks in TensorFlow on GCP
This course is the second in a sequence that introduces the basic concepts of convolutional neural networks, including architectures and networks. We will use convolutional layers (i.e., layers with n layers), and also layers with 1 layer. We will use machine learning techniques for convolutional layers, and also how to implement convolutional layers on TensorFlow. We will use an example implementation of a convolutional neural network that is trained on a huge dataset of images taken by several users. This is a deep neural network trained on tens of millions of images taken by humans. We will use OpenCV, CNTK, and Spark ML. The final output from this class will be a trained TensorFlow model that can process images for several different applications, including image captioning, speech recognition, and also image annotation.The Structure of Convolutional NN
Building Convolutional NNs
Training Convolutional NNs
Final Result
Communication Strategies in the Virtual Classroom
Effective communication is vital to the success of the classroom. Effective verbal and non-verbal communication skills are essential to preparing students to succeed at school and in life. This course is designed to help you gain a competitive advantage in the virtual classroom.

This course is the fourth and last course in the specialization about communication strategies in the virtual classroom. We will learn important communication strategies to use in the midst of a heated argument or other tense situation. We will also learn how to manage the argument and make all necessary demands in a reasonable time. We’ll also look at strategies to make sure both sides know their positions and are being heard. All of this will prepare you for any classroom or school situation where communication is a crucial element of the learning process.

Upon completing this course, you will be able to:
1. Speak persuasively in a tense and argumentative situation
2. Understand and communicate important aspects of the argument
3. Manage communication effectively during arguments
4. Avoid common traps and give each other a chance to demonstrate their arguments.
5. Understand and communicate important aspects of the argument
6. Avoid common traps and give each other a chance to demonstrate their arguments.
7. Understand and communicate important aspects of the argument
8. Listen and learn
9. Develop strong and consistent speaking and writing skills
10. Understand and communicate important aspects of the argumentCommunicating in the Tense
Communicating in the Moment
Communicating in the Moment and Between Arrangements
Entering and

Course Link: https://www.coursera.org/learn/convolutional-neural-networks-tensorflow

Data Pipelines with TensorFlow Data Services

Course Link: https://www.coursera.org/learn/data-pipelines-tensorflow

Data Pipelines with TensorFlow Data Services
In the first course of this specialization, we will learn how to use a data pipeline. We will learn a little about pipelining, how to use ports and how to manage your traffic. We will also learn how to build 1:1 replicas of our pipelines. We will then dive into TensorFlow and dive into the most popular platforms for working with TensorFlow: gRPC, HTTP Server, and Websockets. We will then learn how to use HttpClient to connect our services to our users and drive their data to our users. We will then dive into WebPack and dive into the most popular task runners for task execution: Kotlin and Scala. We will then learn how to use Akka Streams and how to use Utils. We will then dive even deeper into HttpClient and WebSocket and end with an in-depth overview of RxKotlin and RestfulAsync.

Note: This is the first course in the specialization. We will build on your understanding of dataflow concepts, but at first glance, it may seem like a daunting subject. Don’t worry, we will get you up and running quickly. Dataflow is a wonderful concept if you can get your hands on some hardware to process your data, but if you don’t have access to a computer, this course should help you to get you up and running quickly.Data Naming
Working with the Data
Building 1:1 Trains
End-to-End Data Processing with the Data
Data Visualization with Advanced Excel
In this course, you will explore Excel’s Advanced Regression and Series Control (R&C) to gain a very deep understanding of Excel’s capabilities. You will also learn how to customize the shape and color of your data visualization to suit your needs.

After completing this course, you will be able to:
– Set up and use custom R&C curves
– Manipulate data with Excel
– Customize the visualizations of Excel

This course has been designed to give you a chance to learn both how to use Excel and to practice its core capabilities. You will be able to use Excel to build complex graphs, charts and other visualizations. It will also give you a chance to practice complex manipulation skills that you will learn to master in subsequent Courses of the Specialization.

You will need a computer with a stable Internet connection and a stable Internet browser. You will not be able to watch videos or play games on your own computer, but you will be able to use the commands in Excel to put things together.

The course will take about 4-5 weeks to complete and you will get a lot of done. When you complete it, you will have a finished spreadsheet that you can use to build any kind of visualization.

Note: If you want to watch videos of

Course Link: https://www.coursera.org/learn/data-pipelines-tensorflow

Device-based Models with TensorFlow Lite

Course Link: https://www.coursera.org/learn/device-based-models-tensorflow

Device-based Models with TensorFlow Lite
We will learn how to use a simple XR (Video Rasterizer) application written in python, on a windows laptop, to render a scene in 3D space. We will then use a TensorFlow implementation of a dsparc64 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for both linear and gradient approximations for rendering. These models will then be used to implement a simple dsparc32 to render a scene in a 2D space. We will then use TensorFlow models for

Course Link: https://www.coursera.org/learn/device-based-models-tensorflow

Google Cloud Platform Big Data and Machine Learning Fundamentals

Course Link: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals

Google Cloud Platform Big Data and Machine Learning Fundamentals
This 1-week, accelerated online course introduces the machine learning and data science concepts that you need in order to be an effective machine learning expert. You’ll learn:
* Modeling and optimization problems in computer vision and natural language understanding
* Identify the unique attributes of deep learning and apply them to solve training and testing problems
* Leverage common machine learning algorithms and Python libraries to solve training and testing problems
* Train and reuse neural networks
* Build a deep neural network using Python

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<https://www.coursera.org/learn/gcp-big-data-ml-fundamentals

Image Understanding with TensorFlow on GCP

Course Link: https://www.coursera.org/learn/image-understanding-tensorflow-gcp

Image Understanding with TensorFlow on GCP
This specialization teaches you the fundamentals of Image Understanding using the TensorFlow on Google Cloud Platform. We assume you have some basic knowledge of programming and machine learning.

If you have no background in computer science, this is an easy course for you. If, however, you have some background in programming, you will notice a lot of new skills and concepts. Image Understanding is the name given to the process of transforming an input image into a representation for other purposes, such as describing or representing in a data representation. You will learn about:
* What an input image is and how to use it
* The architecture of a machine learning system
* The types of images (non-visual) and their representations
* Representing images using functions
* Functions

Week 1: Image Understanding

Week 2: Function Overload

Week 3: Representing Images Using Functions

Week 4: Distributions and Linear Regression
Imagining Communications Networks
In this MOOC, we will learn the basic principles of signal processing, propagation, and reception. We will learn the design of a network path using the fundamental principles of signal processing, propagation, and reception. We also introduce the basic concepts of computer network architecture, how data are transferred over a network path, and how the path is filtered and encapsulated. We will learn the basics of computer network operations, the common operations that occur in a network path, and how they are implemented in a TCP/IP round-robin system. We will learn the fundamentals of packet analysis, the common operations that occur in a transparent packet, and how the system processes packets. We will also learn how the packet dynamics are represented in a packet model, and how this is used for performance. We will also introduce basic concepts of packet dynamics and filter priming, and how these are implemented in a TCP/IP round-robin system.Week 1: Signal Processing
Week 2: The Path and Filtered Packets
Week 3: The Path and Unfiltered Packets
Infographics: How to Use them
Infographics are everywhere these days. They’re ubiquitous in our lives, they’re ubiquitous in the workplace, they’re ubiquitous in our cultural lives and they supplant other visual modes in an increasingly fast-paced and dynamic world.

But making an infographic is no magic wand. It takes work, practice, and understanding of the underlying principles. In this course, we’ll focus on the key components that make an infographics valuable and influential. We’ll learn how to organize them, use them to tell a compelling story, and leverage their power through effective language.

After this course, you’ll be able to:
1. Use basic tools to make inf

Course Link: https://www.coursera.org/learn/image-understanding-tensorflow-gcp

Intro to TensorFlow

Course Link: https://www.coursera.org/learn/intro-tensorflow

Intro to TensorFlow
This course introduces the basic concepts of TensorFlow. It will cover topics such as integration, machine learning, and deep learning, as well as the use of libraries on the TensorFlow side. TensorFlow is a modern GPU-based deep learning framework that is very popular in industry and is used by big names in industry-level research. TensorFlow is a high-performance ML framework that is very valuable in HPC applications. This course also covers the use of linear models in TensorFlow.

Pre-requisites
To get the most out of this course, learners should have access to:
*Pre-bought hardware or recent NVIDIA GTX 1080ti or GTX 1080m video cards
*Basic proficiency with common query language such as Python
*Experience with data modeling and visualization
*Knowledge of machine architecture such as CPU, GPU, and FPGA
*Basic familiarity with Python
*Basic familiarity with client-server architecture
*Knowledge of common data modeling frameworks, including linear models, matrix and array
*Basic familiarity with linear algebra and data manipulation
*Basic familiarity with basic data analysis

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<https://www.coursera.org/learn/intro-tensorflow

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Course Link: https://www.coursera.org/learn/introduction-tensorflow

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
This course introduces the fundamentals of Linear Algebra and the Functor Theorems for Machine Learning, as well as the main concepts of the Linear Algebra of the class. Throughout the course you will learn specific techniques for working with linear models. You will also learn how to use the combination of functions, layers, layers of trees and layers of texts to stabilize your models. This includes modeling the optimization problem, and also applications in deep learning and artificial intelligence.

At the end of this course, you will be able to:
1. Use the linear algebra and functions to compute “clustering” parameters for your models
2. Solve tasks involving linear models using the combination of functions, layers, layers of trees and layers of texts
3. Apply the linear models to other linear models, including optimization problems
4. Transform and blend linear models
5. Use the solver to solve linear problemsChapter 1: Linear Algebra
Chapter 3: Functions
Chapter 4: Clustering
Chapter 5: Transform and Blending
Introduction to Data Structures in Java
This course introduces the data structures and algorithms for working with large graphs. We will start by introducing the graph type, its elements and their values. We will then learn how to access data in various ways, including vectors, text, and binary search. We will then learn about the basic concepts of data organization, and how to represent data in a graph. We will then begin to explore different data structures in a simple manner, including simple linkages between data and their data environments. We will then discuss the methods in the linkages between data and their data environments, and will complete a small proof of concept. We will then finish by introducing various data manipulation techniques, including traversals, recursive data structures, and hashing. We will then discuss the basic design of data-intensive applications, including the design of links between data and their environments, and the design of paths and trees. We will then discuss various data visualization techniques, and will complete a small proof of concept. We will then introduce the key concepts for designing large-scale graph-based applications, and will complete a small proof of concept. We will then introduce data compression and representation schemes, and will complete a small proof of concept. We will then complete a larger subset of the topics in the larger topic, and will complete a larger assignment than the previous course.

After successfully completing this course, you will be able to:
1. Describe the core data structures and algorithms in a graph
2. Leverage data structures efficiently by designing algorithms
3. Describe the design of data-intensive applications in a large-scale graph application
4. Apply various data visualization techniques to a large-scale graph application
5. Explain the design of data-intensive applications in a larger-scale graph application

In this course, you will work

Course Link: https://www.coursera.org/learn/introduction-tensorflow