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Learn Regression Analysis, earn certificates with free online courses from Stanford, MIT, University of Pennsylvania, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
Master gradient boosting models through hands-on practice with Tesla stock predictions, covering model training, cross-validation, hyperparameter tuning, feature importance analysis, and early stopping techniques.
Master essential machine learning metrics for evaluating regression and classification models, from basic error measurements to advanced ROC curves and confusion matrices.
Master advanced Python techniques for data analysis, from lambda functions to web scraping, while building expertise in regression analysis, data visualization, and database integration.
Master machine learning fundamentals through hands-on Python programming, covering regression analysis, classification algorithms, and essential data science techniques for real-world problem-solving.
Master statistical concepts, data visualization, and predictive analytics while learning to use powerful tools for forecasting, regression analysis, probability modeling, and prescriptive analytics.
Explore probability, uncertainty, and statistical significance in data analysis. Learn to interpret findings, conduct hypothesis tests, and understand regression analysis for informed decision-making.
Explore causal decision-making in data science, focusing on optimizing policies, pricing, and actions using causal structures and advanced analytical techniques for business, medical, and technological applications.
Master advanced machine learning techniques for complex problem-solving. Explore ensemble learning, regression analysis, unsupervised learning, and reinforcement learning. Gain practical skills to implement and optimize sophisticated models.
Develop practical machine learning skills through hands-on application of supervised learning, ensemble methods, neural networks, and advanced techniques like CNNs and reinforcement learning.
Master advanced probability concepts through joint distributions, statistical testing, and Markov chains, building expertise in data analysis and statistical inference for real-world applications.
Demystify data science through key skills, techniques, and concepts. Explore analytics, statistical modeling, data engineering, big data manipulation, and data mining algorithms.
Explore predictive modeling techniques, including supervised and unsupervised approaches, model fitting, and regression analysis. Develop skills in data classification, decision trees, and linear regression through hands-on activities.
Learn data analysis techniques for social sciences, including probability, statistics, regression, experiments, and machine learning, with real-world applications and R programming instruction.
Develop an analytics mindset and master data analytics tools in accounting, from Excel to Python, covering visualization, analysis, and machine learning applications.
Explore data-driven marketing decisions through causal, survey, textual, and network analysis techniques. Learn to leverage customer satisfaction data for improved marketing strategies.
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