Statistical Machine Learning

Statistical Machine Learning is a subset of artificial intelligence that leverages statistical techniques to enable machines to improve at tasks. Coursera's Statistical Machine Learning catalogue equips you with the ability to develop and apply predictive algorithms based on statistical concepts. You'll learn to build models, interpret results, and make predictions using machine learning techniques. Further, you'll gain an understanding of various statistical learning methods such as regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This skill set can be used in various fields like data science, finance and banking, healthcare, retail, and more where predictive analysis is crucial.

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Results for "Statistical Machine Learning"

  • Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Model Training, Random Forest Algorithm, Model Optimization, Predictive Modeling, Model Evaluation, Python Programming, Performance Tuning, Classification Algorithms

  • Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Probability, Statistical Inference, Statistics, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Correlation Analysis, Histogram, Statistical Visualization, Box Plots

  • Skills you'll gain: Anomaly Detection, Feature Engineering, Fraud detection, Unsupervised Learning, Continuous Monitoring, Autoencoders, MLOps (Machine Learning Operations), Machine Learning Methods, Statistical Machine Learning, Model Training, Time Series Analysis and Forecasting, System Monitoring, Applied Machine Learning, Model Deployment, Statistical Analysis, Taxonomy

  • Skills you'll gain: Investment Management, Portfolio Management, Text Mining, Portfolio Risk, Applied Machine Learning, Asset Management, Network Analysis, Investments, Data Visualization Software, Machine Learning Methods, Return On Investment, Statistical Machine Learning, Financial Statement Analysis, Financial Data, Market Data, Financial Management, Unstructured Data, Predictive Modeling, Web Scraping, Risk Management

  • Skills you'll gain: Supervised Learning, Unsupervised Learning, Classification Algorithms, Dimensionality Reduction, Anomaly Detection, Artificial Intelligence, Embedded Systems, Machine Learning, Regression Analysis, Probability & Statistics, Data Ethics, Applied Machine Learning, Image Analysis, Computer Vision, Responsible AI, Machine Learning Methods, Decision Tree Learning, Statistical Machine Learning, Linear Algebra, Bayesian Statistics

  • University of Washington

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning, Image Analysis, Machine Learning Algorithms, AI Personalization, Unsupervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Machine Learning, Model Training, Logistic Regression, Statistical Modeling, Data Mining

  • Skills you'll gain: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Convolutional Neural Networks, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Statistical Inference

  • Skills you'll gain: Supervised Learning, Model Optimization, PyTorch (Machine Learning Library), Fine-tuning, Generative Model Architectures, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Generative AI, Deep Learning, Model Training, Applied Machine Learning, Statistical Machine Learning, Classification And Regression Tree (CART), Autoencoders, Machine Learning Methods, Machine Learning Software, Machine Learning, MLOps (Machine Learning Operations), Machine Learning Algorithms, Artificial Intelligence and Machine Learning (AI/ML)

  • Alberta Machine Intelligence Institute

    Skills you'll gain: Supervised Learning, Data Preprocessing, Feature Engineering, Model Optimization, Responsible AI, Machine Learning Algorithms, Data Ethics, Applied Machine Learning, Model Evaluation, Data Quality, Machine Learning Methods, Classification Algorithms, Model Training, MLOps (Machine Learning Operations), Model Deployment, Jupyter, Statistical Machine Learning, Data Validation, Machine Learning, Project Management

  • Skills you'll gain: Natural Language Processing, Supervised Learning, Transfer Learning, Recurrent Neural Networks (RNNs), Markov Model, Embeddings, Dimensionality Reduction, Large Language Modeling, Machine Learning Methods, Text Mining, Statistical Machine Learning, Artificial Neural Networks, Classification Algorithms, Data Preprocessing, Deep Learning, Tensorflow, Logistic Regression, Feature Engineering, Applied Machine Learning, Keras (Neural Network Library)

  • Multiple educators

    Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning Methods, Model Evaluation, Machine Learning, Google Cloud Platform, Model Training, Machine Learning Algorithms, Financial Trading, Reinforcement Learning, Recurrent Neural Networks (RNNs), Supervised Learning, Data Pipelines, Machine Learning Software, Time Series Analysis and Forecasting, Applied Machine Learning, Statistical Machine Learning, Model Optimization, Deep Learning, Portfolio Management

  • Skills you'll gain: Statistical Machine Learning, Data Preprocessing, Model Evaluation, PyTorch (Machine Learning Library), Statistical Methods, Probability, Probability & Statistics, Sampling (Statistics), Logistic Regression, Deep Learning, Probability Distribution, Statistical Modeling, Python Programming, Supervised Learning, Machine Learning, Agentic systems, Artificial Intelligence, Model Optimization, Algorithms, AI literacy