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9 Best Computer Vision Online Courses


This course gives you an overview of Computer Vision, Machine Learning with AWS. In this course, you will learn how to build and train a computer vision model using the Apache MXNet and GluonCV toolkit. This course tells you about AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker GroundTruth, and Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS. In the final project, you have to select the appropriate pre-trained GluonCV model, apply that model to your dataset, and visualize the output of your GluonCV model. Now, let's see the syllabus of the course-

Here's why a great gaming laptop is the best all-around computer for college


If you're tackling a degree in science, technology, engineering, or mathematics, there's nothing more frustrating than a machine that can't keep up with the apps you need for your coursework. Here's where a powerful gaming laptop proves its mettle. With GPU acceleration, your machine delivers super-fast image processing, real-time rendering for complex component designs, and it lets you work quickly and efficiently. For engineering students, this means more interactive, real-time rendering for 3D design and modeling, plus faster solutions and visualization for mechanical, structural, and electrical simulations. For computer science, data science, and economics students, NVIDIA's GeForce RTX 30 Series laptops enable faster data analytics for processing large data sets -- all with efficient training for deep learning and traditional machine learning models for computer vision, natural language processing, and tabular data.

Machine Learning in Python - Extras - CouponED


Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works? In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle. We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.

Machine Learning using Python Programming - CouponED


Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3 New Rating: 4.4 out of 54.4 (215 ratings) 32,564 students What you'll learn Description'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python.

Spell unveils deep learning operations platform to cut AI training costs


All the sessions from Transform 2021 are available on-demand now. Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms. The platforms currently employed to train AI models are optimized for machine learning algorithms. AI models based on deep learning algorithms require their own deep learning operations (DLOps) platform, Spell head of marketing Tim Negris told VentureBeat. The Spell platform automates the entire deep learning workflow using tools the company developed in the course of helping organizations build and train AI models for computer vision and speech recognition applications that require deep learning algorithms.

Machine Learning with SciKit-Learn with Python - CouponED


Get a practical understanding of the Scikit-Learn library and learn the ML implementation New Rating: 4.2 out of 5 What you'll learn Description The goal of this course is to help the trainee's expertise working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand.

Python for Data Science and Machine Learning Bootcamp


If you are interested in learning to use NumPy, Pandas, Machine Learning, and more from the comfort of your home then you have landed to the right course. In this course, you will be taught all about using Python for data science and machine learning in the best possible manner. The instructor will explain how you can use spark for big data analysis in detail. Then you will get a chance to understand how to implement machine learning algorithms. Going further, you will get a chance to understand how to use Matplotlib for python plotting.

Quantitative Finance & Algorithmic Trading in Python


Understand stock market fundamentals Understand the Modern Portfolio Theory Understand stochastic processes and the famous Black-Scholes mode Understand Monte-Carlo simulations Understand Value-at-Risk (VaR) You should have an interest in quantitative finance as well as in mathematics and programming! This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main. Markowitz-model is the first step.

Why and how should you learn "Productive Data Science"? - KDnuggets


Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Let's imagine somebody is teaching a "Productive Data Science" course or writing a book about it -- using Python as the language framework. What should the typical expectations be from such a course or book? The course/book should be intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. Readers should be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box.

11 Best PyTorch Courses - (2021 Edition)


PyTorch is a deep learning library developed by Facebook to develop machine learning models for NLP, Computer Vision and AI, to name a few. It was developed by Facebook's Artificial Intelligence Research Group and is used to run deep learning frameworks. PyTorch is an excellent framework for entering the actual machine learning and neural network building process. It is ideal for complex neural networks such as RNNNs, CNNs, LSTMs and neural networks that you want to design for a specific purpose. PyTorch is a very different kind of deep learning library (dynamic vs. static) that was adopted by many researchers if not most, and it's flexible approach and easy-to-understand style have won over newcomers and industry veterans alike.