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Type: Organization
Type: Organization
Perform DataFrame operations in Pandas for a more in-depth look at data wrangling in practice
Explore more advanced data structures in Python and how to handle them with some basic file operations
This module covers the implementation of advanced RNN models that overcome the drawbacks of plain RNNs. We will particularly look at LSTM, GRU-based model, Bi-directional and Stacked RNNs.
Decode responses and extract text from the Request and BeautifulSoup libraries, read and scrape data from XML files, and implement regular expressions to practice advanced web scraping on APIs.
This module enables you summarize and identify the quality of the data using concepts such as aggregation and window functions.
Learn the fundamental concepts of data wrangling and statistics, and understand how they relate to data visualization.
This module covers performing descriptive analytics on time series data, geospatial data, complex data types (arrays, JSON, and JSONB), and text.
In this module you will look at AWS AI services and examine an emerging computing paradigm – the Serverless Computing. We will then proceed to applying NLP and the Amazon Comprehend service to analyze documents.
Analyze marketing campaign data related to new financial products. Discover linear and logistic regression models, and explore the relationships between the different features in the data
Identify missing values, outliers and trends in medical data. Create bar charts, heatmaps and other visualizations to understand how the features impact the target column of the data set
Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data
Fix, clean, merge, and connect new data to perform data wrangling tasks on UN and GDP data
This module covers probability theory and looks at how you can use NumPy and SciPy to solve probability problems.
This course will take a look at autoencoders and their applications will help you see how autoencoders are used in dimensionality reduction and denoising. You will implement an artificial neural network and an autoencoder using the Keras framework. By the end of this course, you will be able to implement an autoencoder model using convolutional neural networks.
Discover what it means to be "Pythonic", learn to write succinct, readable expressions for creating lists; use Python comprehensions with lists, dictionaries, and sets.
This module covers derivatives and integrals and how Python can be used to perform basic calculus.
This module will cover the key stages involved in building a comprehensive program. It also explains how to build and save a model such that you get the same results every time it is run and call a saved model to use it for predictions on unseen data.
Learn how to build a machine learning mode and get started on the popular deep learning framework PyTorch. You will delve into one of the most exciting fields in deep learning research - reinforcement learning - and take a closer look at the deep Q-learning algorithm
Review the mathematics that comprise Artificial Neural Networks, apply linear transformations in Python, and build a logistic regression model with Keras
This module provides you with a good understanding what deep learning is and how programming with TensorFlow works
This module covers the concept of clustering in machine learning. It explains three of the most common clustering algorithms, with a hands-on approximation to solve a real-life data problem. The three clustering algorithms covered are k-means, mean-shift and DBSCAN algorithms.
This chapter will get you introduced to the fundamentals of Clustering which will be illustrated with two unsupervised learning algorithms. You will be implementing flat clustering with the k-means algorithm and hierarchical clustering with the mean shift algorithm. By the end of this chapter you will have a firm grasp on the basics of Clustering.
Explore the architecture of CNNs and related techniques to build image processing applications and classify models with Keras
Discover classes in Python, one of the cornerstones of object-oriented programming. Also explore how to use methods in Python programming.
Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.
Discover how to use Matplotlib to create visualizations using the built-in plots that are provided by the library. Customize your visualizations and write mathematical expressions using TeX.
Study the form and function of two major cross-validation methods, build a scikit learn interface, and use cross-validation to perform image classification and selection on example datasets
This module explores how important Recurrent Neural Networks (RNNs) are for sequence modeling. It particularly focuses on deep learning approaches for sequences, particularly plain RNNs and 1D convolutions Foundations more advanced RNN-based models are laid in this module
This module demonstrates the power of word embeddings and explains the popular deep learning-based approaches for embeddings
Experiment with Neural Network architectures to build and evaluate both single and multi-layer sequential models in Keras
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.