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There are several common machine learning packages in Python, including:

  • NumPy: a fundamental package for scientific computing with Python, providing support for arrays and matrices.
  • SciPy: a library for scientific and technical computing with Python, providing support for advanced mathematical functions, optimization, and statistics.
  • Pandas: a library for data manipulation and analysis, providing support for data structures and operations for manipulating numerical tables and time series.
  • scikit-learn: a machine learning library for Python, providing implementations of a wide range of machine learning algorithms and utility functions for preprocessing and model evaluation.
  • TensorFlow: an open-source software library for machine learning, providing support for building and training machine learning models using neural networks.
  • Keras: a high-level neural networks API for Python, providing a simple and modular interface for building and training machine learning models.
  • PyTorch: an open-source machine learning library for Python, providing support for building and training deep learning models using tensors.
  • Plotly: Plotly is a data visualization library for creating a wide range of interactive plots and charts.

Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

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Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more

Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … Read more

Leveraging Distributed Computing for Weather Analytics with PySpark

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Apache Spark is a popular distributed computing framework for Big Data processing and analytics. In this tutorial, we will work hands-on with PySpark, Spark’s Python-specific interface. We built on the conceptual knowledge gained in a previous tutorial: Introduction to BigData Analytics with Apache Spark, in which we learned about the essential concepts behind Apache Spark … Read more

Getting Started with Big Data Analytics – Apache Spark Concepts and Architecture

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Apache Spark is an absolute powerhouse when it comes to open-source Big Data processing and analytics. It’s used all over the place for everything from data processing to machine learning to real-time stream processing. Thanks to its distributed architecture, it can parallelize workloads like nobody’s business, making it a lean, mean data processing machine when … Read more

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

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Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … Read more

Cluster Analysis with k-Means in Python

Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more

Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card Fraud

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Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more

Build a High-Performing Movie Recommender System using Collaborative Filtering in Python

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The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? What song to listen to next? Nowadays, the internet and its vast content offer too many choices. But there is hope – recommender systems are here to solve this problem and support our decision-making. They … Read more

Automate Crypto Trading with a Python-Powered Twitter Bot and Gate.io Signals

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This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … Read more

Requesting Crypto Price Data from the Gate.io REST API in Python

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In this tutorial, we will demonstrate how to use the Gate.io spot market API to stream cryptocurrency prices in real-time using Python. Streaming prices is crucial for implementing use cases such as analyzing an incoming stream of price data in real-time for generating trading signals or conducting price analytics. One of the benefits of using … Read more

Predictive Policing: Preventing Crime in San Francisco using XGBoost and Python

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In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working with a dataset from Kaggle that contains information on 39 different types of crimes, including everything from vehicle theft to drug offenses. Using Python and the powerful Scikit-Learn library, we’ll train a classification model using … Read more