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Machine Learning: Understanding Solutions and Developing Applications

List Tuition: $250.00

Course Overview

Duration: 20 Hours
Access: 1 Year
Format: Self-Paced Learning

Machine Learning is a discipline of Artificial Intelligence focused on teaching machines to gather and apply knowledge. We're already beginning to see the profound effects that educated intelligence systems are having on our world. The decade ahead promises to be one in which we will see an explosive growth in Machine Learning applications, techniques, solutions, and platforms.

In this training class we will focus on learning the core concepts of Machine Learning and getting hands-on with the latest technologies to learn how to create your own solutions!

Audience

This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasoned software developer who is comfortable and fluent in one or more modern development languages (preferably R, Python, or Spark).

Students are also expected to have a strong knowledge of the mathematical and statistical concepts which underlie Machine Learning as this course will NOT these concepts in-depth!

Course Outline

  • Chapter 1: What is Machine Learning?
    • How does ML relate to AI?
    • How does DL relate to ML/AI?
    • ML landscape
    • ML applications
    • ML algorithms & models (supervised and unsupervised)
  • Chapter 2: Machine Learning Tools
    • Introduction to Jupyter notebooks / R-Studio
    • Lab: Getting familiar with ML environment
  • Chapter 3: Machine Learning Concepts
    • Statistics Primer
    • Covariance, Correlation, Covariance Matrix
    • Errors, Residuals
    • Overfitting / Underfitting
    • Cross validation, bootstrapping
    • Confusion Matrix
    • ROC curve, Area Under Curve (AUC)
    • Lab: Basic stats
  • Chapter 4: Feature Engineering
    • Preparing data for ML
    • Extracting features, enhancing data
    • Data cleanup
    • Visualizing Data
    • Lab: data cleanup
    • Lab: visualizing data
  • Chapter 5: Linear regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Running LR
    • Evaluating LR model performance
    • Lab
    • Use case: House price estimates
  • Chapter 6: Logistic Regression
    • Understanding Logistic Regression
    • Calculating Logistic Regression
    • Evaluating model performance
    • Lab
    • Use case: credit card application, college admissions
  • Chapter 7: SVM (Supervised Vector Machines)
    • SVM concepts and theory
    • SVM with kernel
    • Lab
    • Use case: Customer churn data
  • Chapter 8: Decision Trees & Random Forests
    • Theory behind trees
    • Classification and Regression Trees (CART)
    • Random Forest concepts
    • Labs
    • Use case: predicting loan defaults, estimating election contributions
  • Chapter 9: Naive Bayes
    • Theory behind Naive Bayes
    • Running NB algorithm
    • Evaluating NB model
    • Lab
    • Use case: spam filtering
  • Chapter 10: Clustering (K-Means)
    • Theory behind K-Means
    • Running K-Means algorithm
    • Estimating the performance
    • Lab
    • Use case: grouping cars data, grouping shopping data
  • Chapter 11: Principal Component Analysis (PCA)
    • Understanding PCA concepts
    • PCA applications
    • Running a PCA algorithm
    • Evaluating results
    • Lab
    • Use case: analyzing retail shopping data
  • Chapter 12: Recommendation (Collaborative filtering)
    • Recommender systems overview
    • Collaborative Filtering concepts
    • Lab
    • Use case: movie recommendations, music recommendations

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Machine Learning: Understanding Solutions and Developing Applications

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