Course
Preprocessing for Machine Learning in Python
- IntermediateSkill Level
- 4.6+
- 2.2K
Learn how to clean and prepare your data for machine learning!
Machine Learning
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Learn how to clean and prepare your data for machine learning!
Machine Learning
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Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
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Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
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Learn to retrieve and parse information from the internet using the Python library scrapy.
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Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
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Learn to process, transform, and manipulate images at your will.
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In this course youll learn the basics of working with time series data.
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Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
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In this course you will learn the details of linear classifiers like logistic regression and SVM.
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Learn how to manipulate and visualize categorical data using pandas and seaborn.
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In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
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Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
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Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
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Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
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In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
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Create new features to improve the performance of your Machine Learning models.
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Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
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Learn about string manipulation and become a master at using regular expressions.
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This course focuses on feature engineering and machine learning for time series data.
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Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
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Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
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Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
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Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!
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Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
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Learn to create your own Python packages to make your code easier to use and share with others.
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Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
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Create interactive data visualizations in Python using Plotly.
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Build the foundation you need to think statistically and to speak the language of your data.
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Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
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Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Applied Finance