Applied Machine Learning

Applied Machine Learning is a multidisciplinary approach to constructing algorithms that can learn from and predict future data. Coursera's Applied Machine Learning catalogue provides you with the necessary knowledge and skills to effectively use machine learning in a range of practical applications. You'll learn how to process and analyze large-scale data, build predictive models using supervised and unsupervised learning techniques, and apply these models to real-world problems such as image and speech recognition, autonomous driving, and predictive analytics. Enhance your problem-solving abilities and gain a competitive edge in fields like data science, artificial intelligence, and software engineering by mastering machine learning techniques such as decision trees, neural networks, regression, and clustering.

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Results for "Applied Machine Learning"

  • University of Michigan

    Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning Methods, Machine Learning, Model Training, Model Optimization, Machine Learning Algorithms, Unsupervised Learning, Python Programming, Classification Algorithms, Artificial Neural Networks

    4.6 stars, 8.8K reviews, Intermediate, Course, 1 - 4 Weeks

  • Johns Hopkins University

    Skills you'll gain: Computer Vision, Model Evaluation, PyTorch (Machine Learning Library), Supervised Learning, Unsupervised Learning, Image Analysis, Applied Machine Learning, Data Preprocessing, Dimensionality Reduction, Machine Learning Methods, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Model Training, Machine Learning, Deep Learning, Model Optimization

    3.5 stars, 17 reviews, Intermediate, Specialization, 3 - 6 Months

  • Board Infinity

    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

    Beginner · Course · 1 - 4 Weeks

  • Multiple educators

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning

    4.9 stars, 39K reviews, Beginner, Specialization, 1 - 3 Months

  • New York University

    Skills you'll gain: Supervised Learning, Machine Learning Methods, Model Evaluation, Reinforcement Learning, Applied Machine Learning, Statistical Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Statistical Modeling, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Model Training, Machine Learning, Derivatives, Tensorflow

    3.7 stars, 825 reviews, Intermediate, Specialization, 3 - 6 Months

  • Google Cloud

    Skills you'll gain: Model Deployment, Google Cloud Platform, Natural Language Processing, Tensorflow, Model Optimization, Model Evaluation, MLOps (Machine Learning Operations), Computer Vision, Large Language Modeling, Reinforcement Learning, Convolutional Neural Networks, Model Training, Image Analysis, Transfer Learning, Keras (Neural Network Library), Systems Design, Applied Machine Learning, AI Personalization, Cloud Deployment, Machine Learning

    4.5 stars, 1.5K reviews, Advanced, Specialization, 3 - 6 Months

  • Packt

    Skills you'll gain: Supervised Learning, Model Optimization, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Algorithms, Model Training, Data Preprocessing, Classification Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Data Transformation, Fine-tuning

    Advanced · Course · 1 - 3 Months

  • EDUCBA

    Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Convolutional Neural Networks, Exploratory Data Analysis, Feature Engineering, Tensorflow, Model Training, Predictive Modeling, Transfer Learning, Applied Machine Learning, Machine Learning Methods, Application Development, Predictive Analytics, Model Evaluation, Machine Learning, Network Model, Analytics, Network Architecture, Design

    Beginner · Specialization · 1 - 3 Months

  • Coursera

    Skills you'll gain: Data Preprocessing, Supervised Learning, Model Optimization, Feature Engineering, Pandas (Python Package), Data Wrangling, Exploratory Data Analysis, Data Quality, Model Training, Applied Machine Learning, Data Processing, Data Manipulation, Statistical Machine Learning, Data Transformation, Classification And Regression Tree (CART), Data Cleansing, Data Pipelines, Machine Learning, Data Modeling, Data Architecture

    Intermediate · Specialization · 3 - 6 Months

  • EDUCBA

    Skills you'll gain: Model Evaluation, Sampling (Statistics), NumPy, Pandas (Python Package), Data Literacy, Feature Engineering, Statistical Visualization, Data Analysis, Predictive Modeling, Analytics, Regression Analysis, Predictive Analytics, Statistics, Analysis, Computational Thinking, Data Science, Business Intelligence, Applied Machine Learning, Data Quality, Machine Learning

    Beginner · Specialization · 3 - 6 Months

  • University of Glasgow

    Skills you'll gain: Generative AI, Data Preprocessing, Model Evaluation, Generative Model Architectures, Dimensionality Reduction, Applied Machine Learning, Feature Engineering, Image Analysis, Tensorflow, Generative Adversarial Networks (GANs), Computer Vision, LLM Application, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Convolutional Neural Networks, Keras (Neural Network Library), Recurrent Neural Networks (RNNs), Large Language Modeling, Data Cleansing, Autoencoders

    Intermediate · Course · 1 - 3 Months

  • IBM

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Feature Engineering, Dimensionality Reduction, Supervised Learning, Classification Algorithms, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Machine Learning Methods, Statistical Hypothesis Testing, Model Training, Data Processing, Machine Learning

    4.6 stars, 3.4K reviews, Intermediate, Specialization, 3 - 6 Months