Dealing with Missing Data in Python
Learn how to identify, analyze, remove and impute missing data in Python.
Learn how to identify, analyze, remove and impute missing data in Python.
This course will show you how to integrate spatial data into your Python Data Science workflow.
Learn efficient techniques in pandas to optimize your Python code.
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Learn to design and run your own Monte Carlo simulations using Python!
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
In this course you'll learn to use and present logistic regression models for making predictions.
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn how to detect fraud using Python.
Discover the fundamental concepts of object-oriented programming (OOP), building custom classes and objects!
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn how to segment customers in Python.
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Learn to build recommendation engines in Python using machine learning techniques.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn to build pipelines that stand the test of time.
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn to manipulate and analyze flexibly structured data with MongoDB.
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Learn how to use Python to analyze customer churn and build a model to predict it.
Learn how to approach and win competitions on Kaggle.
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
Learn how to build a model to automatically classify items in a school budget.
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Python's SimPy package.
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
This course is for R users who want to get up to speed with Python!
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
In this course you'll learn how to apply machine learning in the HR domain.
Use survival analysis to work with time-to-event data and predict survival time.
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Conduct a supply chain analysis of the ingredients used in an avocado toast to gain an understanding of the complex supply chain involved.
Explore local and global fitness trends to identify product niches. Investigate online interest in gyms, workouts, digital services, and web apps.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Use web scraping and NLP to find the most frequent words in classic literature: Herman Melville's novel, Moby Dick.
Find the true Scala experts by exploring its development history in Git and GitHub.
Tidy a bank marketing campaign dataset by splitting it into subsets, updating values, converting data types, and storing it as multiple csv files.
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!
Apply your data manipulation skills to time series data on water levels of the River Thames.
Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!
Analyze product data for an online sports retail company to optimize revenue.
Apply your importing and cleaning data and data manipulation skills to explore New York City Airbnb data.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Use joining techniques to discover the oldest businesses in the world.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Use coding best practices and functions to improve a script!
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Use a variety of data manipulation techniques to explore different aspects of Lego's history!