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Working in a hands-on learning environment, led by our Real-World Machine Learning expert instructor, students will learn about and explore: You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming.you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.It introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

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Unlimited Duration

Last Updated

February 4, 2021

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Certification

Course Description

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression. You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you’re done, you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.

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Course Curriculum

    • Using data to make decisions 00:00:00
    • Following the ML workflow: from data to deployment 00:00:00
    • Boosting model performance with advanced techniques 00:00:00
    • Getting started: data collection 00:00:00
    • Preprocessing the data for modeling 00:00:00
    • Using data visualization 00:00:00
    • Basic machine-learning modeling 00:00:00
    • Classification: predicting into buckets 00:00:00
    • Regression: predicting numerical values 00:00:00
    • Model generalization: assessing predictive accuracy for new data 00:00:00
    • Evaluation of classification models 00:00:00
    • Evaluation of regression models 00:00:00
    • Model optimization through parameter tuning 00:00:00
    • Motivation: why is feature engineering useful? 00:00:00
    • Basic feature-engineering processes 00:00:00
    • Feature selection 00:00:00
    • Data: NYC taxi trip and fare information 00:00:00
    • Modeling 00:00:00
    • Advanced text features 00:00:00
    • Image features 00:00:00
    • Time-series features 00:00:00
    • Exploring the data and use case 00:00:00
    • Extracting basic NLP features and building the initial model 00:00:00
    • Advanced algorithms and model deployment considerations 00:00:00
    • Before scaling up 00:00:00
    • Scaling ML modeling pipelines 00:00:00
    • Scaling predictions 00:00:00
    • Display advertising 00:00:00
    • Digital advertising data 00:00:00
    • Feature engineering and modeling strategy 00:00:00
    • Size and shape of the data 00:00:00
    • Singular value decomposition 00:00:00
    • Resource estimation and optimization 00:00:00
    • Modeling 00:00:00
    • K-nearest neighbors 00:00:00
    • Random forests 00:00:00
    • Other real-world considerations 00:00:00

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