Applied AI with DeepLearning

Course Link: https://www.coursera.org/learn/ai

Applied AI with DeepLearning
This one-week, accelerated online class gives you a head start on learning about machine learning models and approaches. We will cover topics such as text classification, image captioning, natural language processing, text clustering and sentiment analysis, and more. You will learn the key techniques from the deep learning community for building effective models and get you up to speed on the state-of-art in AI.

You will:

– Understand the model-building process, and how to approach it
– Know how to build effective models
– Know how to use techniques to overcomplicate models
– Know how to run tests and measure results
– Know how to use tools to under-complicate models
– Know how to use tools to under-utilize models
– Know how to utilize GPUs and other AI platforms to build effective models

This is an advanced course, intended for industry customers, engineers, and students. It assumes previous knowledge and understanding of machine learning and AI.Module 1: Text Classification
MODULE 2: Natural Language Processing
MODULE 3: Image Captioning
MODULE 4: Sentiment Analysis
Architecture of Software Systems
In this course, we will learn what is a central idea in the design of software systems and how to apply that design thinking process to solving problems in an organization. We will build on the themes of central problem solving and technology evolution that are emphasized in the School of Architecture curricula. Through a series of culminating projects, we will integrate ideas from both software design and architecture disciplines into a single overall project.

Upon completing this course, you will be able to:
1.design a class-based programming model for a distributed object-oriented software system
2.design a distributed object-oriented file-system system
3.design a distributed object-oriented data-system
4.design a distributed object-oriented workstation
5.design a distributed file-system
6.design a distributed data-system
7.design a distributed workstation

This course is part of the university course in Architecture of Software Systems focusing on the central question – what is the architecture of a software system? We will look at many different aspects of a system including how the hardware and operating system are designed, the architecture of the software systems themselves, the design of the data-processing and storage systems, the design of the communication and routing protocols, and the design of the networked systems that interface the various parts of the system.

This course is also part of the EIT-Digital technology school programme. The course has been taught by several professors from MIPT, and we hope that you will enjoy it as much as we enjoyed creating it.In this course, we first introduce the key questions that we will ask in the question-and-answer part of the course. We will consider several examples from

Course Link: https://www.coursera.org/learn/ai

Deep Learning for Business

Course Link: https://www.coursera.org/learn/deep-learning-business

Deep Learning for Business – From Neural Networks to Machine Loops
This advanced course introduces the deep learning field to NN, CMU, and deep convolutional neural networks. We will learn about their algorithms, their design choices, and their performance characteristics. We will also focus on machine translation problems, where we will apply CMYK to translate between a fixed-point and multidimensional representation of a system. We’ll also make use of linear models, random forest and greedy-lite implementations of the deep convolutional neural networks, for performance optimization. We’ll also talk about the challenges associated with implementing deep convolutional models, and the techniques used to overcome those challenges. We’ll also cover the main issues in producing high-quality videos, for the support of audiovisual and video conferencing.Deep Neural Networks
Recurrent Neural Networks
SIFT-ESL TensorFlow
Deep Convolutional Models
Datalabels: the engineering of bilinguals
This course introduces the formal methods of drawing diagrams of language acquisition, covering the geometrical and algorithmic details of the method. The main topics include the drawing of internal and external symbols, the analysis of the interpretation of language features in texts, and the use of graph-based or network-based models to explore the properties of bilingualism.

During the half-time we will explain the principles of the drawing process, while focusing on the specific of drawing external symbols, which are often used in teaching English as a second language. We will also explain how to use SIFT-ESL to analyze the content of a text, by translating it (in English) into a second language that the student can understand.

After completing this course, you will:
1. Understand the formal methods of drawing diagrams of language acquisition
2. Understand the content of texts that are being taught in English as a second language
3. Understand the type of texts that are being taught in English as a second language, and the type of learners being taught them
4. Understand the analyses that are made by SIFT-ESL implementations of the deep convolutional models

This course was created by the University of Turku, Turku University.

This course is recognized by the University of Turku and the University of Turku Technical College as part of their programmes on language acquisition.Why learn English as a second language?
How do we study language acquisition and the phonology?
How do we know what to expect from language acquisition?
Debugging Common Android Lava Pointers
This is a set of techniques to catch and correct for user-invalid code that may be causing problems on your Android device. These techniques are especially useful for debugging common char* and str* functions. They are also applicable

Course Link: https://www.coursera.org/learn/deep-learning-business

Getting Started with AI using IBM Watson

Course Link: https://www.coursera.org/learn/ai-with-ibm-watson

Getting Started with AI using IBM Watson IoT Platform
This two-week accelerated, all-access course gives you a full overview of the capabilities of the IBM® Watson IoT Platform and an opportunity to get hands-on experience in developing applications with it. You’ll learn all the variables that affect the actions an AI system will take, how to use the sensors and actuators on your PC, and how to use the cloud to run embedded and mobile apps. We’ll also teach you how to use the IoT platform to connect your PC to the cloud, including how to use the cloud IDE and debugging tools.

This is the third and final course in the specialization about using AI with MATLAB, and it includes three other topics:
– Machine Learning with Deep Learning, Machine Learning with Physically Based Deep Learning, and Adaptive Networking
– Neural Networks
– Convolutional Neural Networks
– Recurrent Neural Networks

This course should take about 4-5 hours per week, with approximately 2 hours dedicated to learning and two hours dedicated to using the cloud. You can take the course as many times as you want, and you can take it class after class. The course is designed to help you gain experience using the cloud to develop and deploy deep learning and regular deep learning applications, so that when you master the course, you can take it to the next level. The course is aimed at anyone interested in using machine learning and deep learning as a tool for data science, computer vision and natural language processing, and for any other AI-related field. You don’t need any previous experience in AI or computer science; it should be fun for all.Getting Started with IBM Watson IoT Platform
IBM Watson IoT Core Components
IBM® Smart IoT Core Software
Get Organized: How to Track and Contribute to Organizations
This course examines the most common and efficient ways to contribute to teams and organizations. We begin by examining how to contribute to organizations using the modern tools of the trade, including software tools, systems and people. We also examine the most common kinds of contributions, including proposals, standard development reports, bug fixes, patches, reports, and pull requests. We conclude by looking at how to organize all of your modern contributions, including standard development reports, and then apply the organizational principles that you will learn to gain the most from each of these processes. This course is designed to help you organize all of your contributions, including pull requests, in order to allow your contributions to be merged quickly and applied to the most recent stable release of the product or software. We use the agile method of contribution, meaning that you work on the product at an even higher level than if you were just working on the software itself. We hope that you enjoy the course and look forward to you contributing to the organization that you

Course Link: https://www.coursera.org/learn/ai-with-ibm-watson

Machine Learning Foundations: A Case Study Approach

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

Machine Learning Foundations: A Case Study Approach
Machine learning is the process of getting a computer to act without being explicitly programmed. This course will focus on the process of getting a computer to act on its own, without needing to be explicitly programmed. We will discuss the fundamentals of how a computer learns, and how this learning process can be applied to other domains of technology, such as sensors and actuators. This course will take you from a novice to a pro.

In this first part, we will start by introducing computer vision as a new technology that has yet to be integrated into a core computer system. The course will focus on the four stages of intelligent machine learning: signal generation, feature detection, feature selection and feature scaling. We will also discuss how these features are combined in a feature selection algorithm to select a good smartphone model based on a particular combination of features. In the second part, we will focus on how these same concepts are applied to the process of getting a good signal from a sensor. We will also discuss how this information can be combined with other information to select an appropriate model for a given application. Finally, we will discuss an example application of the process to get a good signal from sensors that pick up ambient light levels.

This is the second part of our specialization on Machine Learning for Data Science. To get the most out of this specialization, we will recommend that you complete the first part first to make sure you understand the system and how it works.

Mastery of these concepts will enable you to jump right in to the final model selection algorithm!

We will begin by introducing the basic concepts of machine learning. We will take a look at the different stages of machine learning and how they fit into a data science problem. We will use the open source R library and demonstrate how to use RStudio as a development environment. Next, we will take a look at different types of models and how to select one for a given problem. We will then demonstrate the R algorithm used to select the best model for a given problem. We will then show how the signal generation phase is conducted and how the selection is made competitively. The final model selection will involve a weighted objective ranking of all models.

The signal generation phase will focus on the four different stages of machine learning: signal generation, estimation, selection and validation. In addition to the signal generation phase, we will cover the topic of error estimation. You will learn how to select the best model for a given problem and how to combine error estimation with other models to make more accurate selection decisions. The course will also focus on the topic of selection validation. You will learn how to select the best model for a given problem if the other models do not agree.

This specialization is part of the university course on Machine Learning and Artificial Intelligence (ML2) and is the second part of a three-part series on ML (Machine Learning), including part 3 in a future module.Machine Learning Foundations
Sign

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

Neural Networks and Deep Learning

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

Neural Networks and Deep Learning for Vision
Neural networks are the standard in speech recognition and image processing. Deep Learning is the application of neural networks to image recognition, speech recognition, and natural language processing. The final course in this specialization is on deep learning for vision. We will learn the technical and application of specific algorithms for deep neural networks, including their architecture and implementation details. We will also cover the general concepts and algorithms for deep learning based on gradient descent. We will cover image reconstruction as well as image segmentation. We will learn the basics of convolutional neural networks, natural language processing, and image segmentation. Images are processed back-and-forth and forward-and-neither, so the architecture and implementation details must be carried forward from one course to the next.

What you’ll learn:

– The basic neural network and neural architecture
– Design of deep neural technologies
– Implementation details of various deep neural technologies
– Describes the general concepts and algorithms for deep neural networks, including architectures and details of convolutional and recurrent architectures
– Design of convolutional neural networks
– Implementation details of various deep neural networks, including architectures and details of convolutional and recurrent architectures
– Describes which deep neural technologies are appropriate for various applications, including vision, speech, and natural language.Deep Neural Networks
Neural Representation
Neural Terminology
Neural Efficiency and Attention
Networking, Security, and Planning in an Information Age
In this course, you will learn the basic concepts of networking as they relate to networking in the cloud. You will learn about various protocols and standards that help ensure that your network operates securely and that information is processed appropriately. This course will also cover things like security and privacy in the cloud, as well as the role of common standards and protocols. You will also learn about protocols like TCP/IP, UDP, and packet encapsulation. You will also learn common tasks for networked systems including routing and switching, communication protocols, and security and privacy. You will also learn the basics of communicating in the cloud. This course is the first part of a four-course trainee course series on Networking, Security, and Planning in an Information Age (https://www.coursera.org/learn/networking-security-planning-in-an-information-age).Computing Networked Systems’ Security: Principles of Completeness
Completeness is the foundation of network operations security. Completeness assures the delivery of reliable and efficient services by maintaining a high level of quality and reliability. In this course we will focus on the concept of completeness as it relates to network services, protocols, and standards. You will learn about various security mechanisms and standards that help ensure the quality and reliability of services. We will also discuss protocols and standards that help ensure the delivery of services. You will also learn about network services

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

Structuring Machine Learning Projects

Course Link: https://www.coursera.org/learn/machine-learning-projects

Structuring Machine Learning Projects
This is the last course in the specialization on optimization and machine learning, and the last course to concentrate on the topic of optimization routemapping. You will learn the foundational concepts of optimization in a simple optimization case study: a simple optimization problem to solve using the optimization algorithms profiled in the previous courses. It involves finding an optimal path for a pair of parameters, a vector p and a matrix v, and evaluating the gradient descent algorithm.

You will also learn about the use of optimization problems in the processing of sensory data, and about the use of optimization problems to reduce error. Not only does this course teach you about the basics of optimization, but it also gives you practice in optimizing for optimal results.

In the optimization case study, you will first evaluate the problem from a basic abstract perspective, then optimize a simpler case developed in the previous courses. In the simplified case, you will first implement a simple optimization problem in MATLAB using the Vector Extension API (available within MATLAB under “Tools > Options > Advanced”, and then use the optimization algorithm to reduce the error of your model. The optimization problem is in fact a simplified version of a real life problem, and it includes the inputs and outputs of your optimization algorithm. Therefore, you will need to implement your own optimization plan (or “path”) for your model, and then use this information to reduce your errors in your models.

In the end of this course, you will develop an optimized solution to your problem, and then put this into practice by applying the optimization algorithms to a real world case. Specifically, you will:
– understand the basics of optimization
– iterate through your model, learning new problems each time
– scale up your optimization code by using multiple iterations
– incorporate noise into your models to reduce the errors
– find an optimized path for your model, and then a path for reducing the errors

Take this course if you want to learn how to design and optimize for maximum accuracy and reproducibility in your algorithms. If you are looking for a job in data science or machine learning, this is an excellent introduction to the field. If you are looking for a job in computer science or statistics, however, or if you just want to brush up on your algorithms, this course will take you from beginner to pro.

Module 1: Introduction, Vector Extension, and an Optimized Model
Module 2: Error and Decay
Module 3: Noise and Gradients
Module 4: Paths and Arithmetic Preprocessing
Module 5: Discrete Optimization
Text Mining and Markov Modeling
This course focuses on the study of text mining and graph modeling, two of the most important topics in text mining. The course starts with a description of text and then discusses the fundamentals of graph models, focusing on the representation of text as nodes and edges. The course discusses

Course Link: https://www.coursera.org/learn/machine-learning-projects

Sequence Models

Course Link: https://www.coursera.org/learn/nlp-sequence-models

Sequence Models and Recursion
We have all heard the term sequence models and heard about recursive algorithms for indexing sequences. In this course we will learn about the core of recursion and how we can implement them in C. We will also learn about the important prefix notation and how we can use it to implement recursive algorithms. We will then learn about different algorithms that implement sequences and their solutions. We will then go into detail about the common issues in implementing sequences in C and cover a variety of algorithms.

This is the third course in the Data Structures and Programming Specialization. The first two courses focused on implementing and applying algorithms and the third course focused on writing code that implements sequences.

Special thanks to:

– Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road.
– Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team.
– Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course.
– Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.What are sequences and how do we implement them?
Recursion in C
Indexing and traversals
More indexing and traversals
Service Employees: A Primer for New Professionals
This course provides an introductory primer on the service employees profession. The course is geared towards those who are looking to deepen their knowledge on the life sciences, as well as those who may be new to the field of service employees.

This course is designed to introduce the learners to the basic concepts and terminology of the service employees profession, without getting into nitty-gritty detail. The course is fully online, and it will be delivered through multiple videos, step-by-step hands-on labs, and other resources.

The course will provide the students with the vocabulary and knowledge to understand the service employees profession, without getting into nitty-gritty detail. The course will also allow the students to explore the different facets of the service employees profession, from the various jobs to which they are assigned, the hours they work, the benefits they

Course Link: https://www.coursera.org/learn/nlp-sequence-models

Project: Basic Image Classification with TensorFlow

Course Link: https://www.coursera.org/learn/tensorflow-beginner-basic-image-classification

Project: Basic Image Classification with TensorFlow
In this project-based course, you will follow your own interests to solve problems in a fun and efficient way. You will work on a data set that includes 2D image data (2D TensorFlow models) and video-game assets (Lossless Recurrent Neural Networks and GPUs). You will first get a basic understanding of the common types of data that you will need to deal with in this course. We will cover many of the most prominent categories of data, including common data types such as GIFs, JPEGs, and PNGs, as well as some special cases such as audio and video streams. We will then discuss how to convert these types of data to different machine representations and how this is done. This course is designed to allow you to take the knowledge you’ve acquired throughout the Specialization into any machine-readable formats, so you can use this knowledge to perform machine learning tasks on your own data. This is the first course in the TensorFlow Specialization.Module 1: Data Preparation
Module 2: Convolutional Image Models
Module 3: Convolutional Neural Networks
Module 4: Lossless Recurrent Models
Project: Compute the Power of a Computer System on Intel Architecture
This course gives you easy access to the most efficient processor architectures used in today’s computer systems. We’ll talk about the Intel® Xeon™ processor family, its architecture and the available cores and threads. We’ll also cover the basics of systems management using the command line and Linux-based virtual machine (LVM).

For the final project, we’ll upgrade your laptop computer with a new Intel Xeon™ processor family, install a new operating system (VMM) and use the new processor for virtualization, disk I/O, storage and network access. This course is designed to give you the experience of building a new computer system from scratch, using the Intel Xeon™ processor family for building the CPU. You’ll need to download and unzip all the software tools required to get started. We’ll start by using the command line tool to get started with basic commands. We’ll then guide you through installing the processor and installing the drivers. We’ll use Linux-based virtual machine (LVM) to get started quickly. We’ll also guide you through installing the processor and its operating system on your laptop computer.

You’ll need a laptop computer with a stable Internet connection, but a good enough computer knowledge to work with the command line and basic Linux-based operating system and it’s components, and a good code knowledge to get started. We’ll guide you through all the steps to get the most out of this course!

This is the second course in the Intel Xeon Phi series. You’ll

Course Link: https://www.coursera.org/learn/tensorflow-beginner-basic-image-classification

Project: Basic Sentiment Analysis with TensorFlow

Course Link: https://www.coursera.org/learn/basic-sentiment-analysis-tensorflow

Project: Basic Sentiment Analysis with TensorFlow
In this project-based course, you will master the theory behind the most popular machine learning models of today, and apply them to solve a real-world problem, exploring the common elements common to all of them. You will then implement basic features of a machine learning model, including: recurrent layers, loss layers, and feature selection. You will then apply these features to a problem that interests you, and you will get a chance to apply them in practice, by running training and test datasets, comparing the results with the pre-trained model, and performing feature selection.

After completing this Capstone, you will be able to:
1) Describe the most important features of a neural network
2) Compute sentiment analysis ratings of documents
3) Select interest vectors from tensors
4) Model train and test datasets using the TensorFlow API
5) Select interest vectors from tensors for a document
6) Select interest vectors for a document from tensorflow.org
7) Use the opencv library to view and manipulate tensors

This course is part of the TensorFlow platform as part of the TensorFlow programming environment. TensorFlow is the open-source representation of machine learning models for the data science community.Routes to the Rescue!
End of Course Recap
Project: Write your own Python program for Machine Learning
This project will teach you how to write your own Python program that uses the TensorFlow API to access machine learning models. We’ll use Python 3.

In the first part of the course, we’ll start by creating a project and then we’ll work our way through creating a project specific to your interest.

In the second half of the course, we’ll go through creating a project specific to the data science that you’ve learned in this class.

Finally, we’ll wrap up the course with a project that demonstrates what you’ll learn by creating a complete working example dataset.

You will need to have Python 2.7 or 3.5 installed and the TensorFlow library installed.

The project will require you to use the TensorFlow Python packages from the command line. You will need to download and install the packages.

You will need to use the TensorFlow Python packages from the command line for this course, but you can download and install the packages from the repositories.

You will need to use the available Python packages from the command line, but you can download and install the packages from the repositories.

After completing all these steps, you will be able to:

1. Create a project
2. Create a project specific to the data science you’ve learned
3. Create a project specific to the data science that you want to create
4. Create a project specific to the machine learning model that you

Course Link: https://www.coursera.org/learn/basic-sentiment-analysis-tensorflow

Project: Avoid Overfitting Using Regularization in TensorFlow

Course Link: https://www.coursera.org/learn/tensorflow-regularization-avoid-overfitting

Project: Avoid Overfitting Using Regularization in TensorFlow on GCP
In the simple case where we have a lot of training data you might want to consider whether to make small changes to your codebase to avoid introducing new training examples or whether to maximize the number of examples that are trained on your data by using a regularization. Sometimes, you will want to predict a feature of your data that another computer will use in a given training example. Or, you might want to tune the parameters of your model to make sure it successfully applies to your data. In this course, we will focus on the former and take a look at the latter.

In the first week, we will introduce a variety of regularization methods that can be used to train and evaluate models. We will then learn how to evaluate the performance of a model by using features and evaluation graphs. You will then be able to predict the performance of a model by using linearization and decreasing mean values. The evaluation method is useful for debugging or improving future versions of your model.

Week 2 introduces the linearization class of regularization methods and introduces the evaluation method that you use to evaluate model performance. In week 3, we will introduce a variety of regularization methods that can be used to train and evaluate models. We will then learn how to use features and evaluation graphs to assess model performance. You will then be able to predict the performance of a model by using linearization and decreasing mean values. The evaluation method is useful for debugging or improving future versions of your model.

Week 4 introduces the evaluation method that you use to evaluate the performance of a model by using linearization and decreasing mean values. In week 5, we will introduce the formulae and algorithms to assess models. You will then be able to predict the performance of a model by using linearization and decreasing mean values. The evaluation method is useful for predicting the performance of a model and evaluating different versions of the model.

Week 6 introduces the evaluation method that you use to predict the performance of a model by using linearization and decreasing mean values. In week 7, we will introduce the formulae and algorithms to assess models. You will then be able to predict the performance of a model by using linearization and decreasing mean values. The evaluation method is useful for predicting the performance of a model and evaluating different versions of the model.

In the fourth week, we introduce the Regularization class of algorithms and formulae to fit regular and irregular data. We will then introduce the Regularization class of regularization methods and formulae to fit regular and irregular data. We will then introduce the Regularization class of regularization methods and formulae to fit regular and irregular data. We will then introduce the Regularization class of regularization methods and formulae to fit regular and irregular data. We will then introduce the Regularization class of regularization methods and formulae to fit regular

Course Link: https://www.coursera.org/learn/tensorflow-regularization-avoid-overfitting