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Machine Learning Essentials with Scala is an essentials-level, three-day hands-on course that teaches you core skills and concepts in modern ML practices.

PRIVATE
Course Access

Unlimited Duration

Last Updated

March 3, 2021

Students Enrolled

20

Total Reviews

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Certification

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:

•tMachine Learning (ML) Overview

•tMachine Learning Environment

•tMachine Learning Concepts

•tFeature Engineering (FE)

•tLinear Regression

•tLogistic Regression

•tClassification : SVM (Supervised Vector Machines)

•tClassification : Decision Trees & Random Forests

•tClassification : Naive Bayes

•tClustering (K-Means)

•tPrincipal Component Analysis (PCA)

•tRecommendation (Collaborative filtering)

•tTime Permitting: Capstone Project


Course Curriculum

    • Machine Learning landscape . 00:00:00
    • Machine Learning applications 00:00:00
    • Understanding ML algorithms & models (supervised and unsupervised) 00:00:00
    • Lab: Getting familiar with ML environment 00:00:00
    • Statistics Primer 00:00:00
    • Covariance, Correlation, Covariance Matrix 00:00:00
    • Errors, Residuals 00:00:00
    • Overfitting / Underfitting 00:00:00
    • Cross validation, bootstrapping 00:00:00
    • Confusion Matrix 00:00:00
    • ROC curve, Area Under Curve (AUC) 00:00:00
    • Exercise: Working with Basic Statistics 00:00:00
    • Preparing data for ML 00:00:00
    • Extracting features, enhancing data 00:00:00
    • Data cleanup 00:00:00
    • Visualizing Data 00:00:00
    • Exercise: data cleanup 00:00:00
    • Exercise: visualizing data 00:00:00
    • Multiple Linear Regression 00:00:00
    • Running LR 00:00:00
    • Evaluating LR model performance 00:00:00
    • Exercise / Use case: House price estimates 00:00:00
    • Understanding Logistic Regression 00:00:00
    • Calculating Logistic Regression 00:00:00
    • Evaluating model performance 00:00:00
    • Use case: credit card application, college admissions 00:00:00
    • SVM concepts and theory 00:00:00
    • SVM with kernel 00:00:00
    • Use case: Customer churn data 00:00:00
    • Theory behind trees 00:00:00
    • Classification and Regression Trees (CART) 00:00:00
    • Random Forest concepts 00:00:00
    • Exercise / Use case: predicting loan defaults, estimating election contributions 00:00:00
    • Theory behind Naive Bayes 00:00:00
    • Running NB algorithm 00:00:00
    • Evaluating NB model 00:00:00
    • Exercise / Use case: spam filtering 00:00:00
    • Theory behind K-Means 00:00:00
    • Running K-Means algorithm 00:00:00
    • Estimating the performance 00:00:00
    • Exercise / Use case: grouping cars data, grouping shopping data 00:00:00
    • Understanding PCA concepts 00:00:00
    • PCA applications 00:00:00
    • Running a PCA algorithm 00:00:00
    • Evaluating results 00:00:00
    • Exercise / Use case: analyzing retail shopping data 00:00:00
    • Recommender systems overview 00:00:00
    • Collaborative Filtering concepts 00:00:00
    • Use case: movie recommendations, music recommendations 00:00:00
    • Hands-on guided workshop utilizing skills learned throughout the course 00:00:00

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