Subscribe to DSC Newsletter

Learn #MachineLearning Coding Basics in a weekend - Glossary and Mindmap

For background to this post, please see Learn #MachineLearning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book. 

The following entries (see below) are part of the glossary. The glossary is available as a PDF document. You can download it here.

Contents

Machine Learning concepts 4

  • Learning Algorithm 4
  • Predictive Model (Model) 4
  • Model, Classification 4
  • Model, Regression 4
  • Representation Learning 4
  • Supervised Learning 4
  • Unsupervised Learning 4
  • Semi-Supervised Learning 5
  • Parameter 5
  • Population 5

Algorithms 5

  • Linear Regression 5
  • Principal Component Analysis (PCA) 5
  • K-Means 6
  • Support Vector Machine (SVM) 7
  • Transfer Learning 7
  • Decision Tree 7
  • Dimensionality Reduction 8
  • Instance based learning 8
  • Instance-Based Learning 8
  • K Nearest Neighbors 8
  • Kernel 9


Training: Basics 9

  • Training 9
  • Training Example 9
  • Training Set 9
  • Iteration 9
  • Convergence 9

Training: Data 10

  • Standardization 10
  • Holdout Set 10
  • Normalization 10
  • One-Hot Encoding 10
  • Outlier 11
  • Embedding 11

Regression 12

  • Regression 12
  • Regression Algorithm 12
  • Regression Model 12

Classification 12

  • Classification 12
  • Class 12
  • Hyperplane 12
  • Decision Boundary 12
  • False Negative (FN) 13
  • False Positive (FP) 13
  • True Negative (TN) 13
  • True Positive (TP) 13
  • Precision 13
  • Recall 14
  • F1 Score 14
  • Few-Shot Learning 14
  • Hinge Loss 14
  • Log Loss 14

Ensemble 15

  • Ensemble 15
  • Ensemble Learning 15
  • Strong Classifier 15
  • Weak Classifier 15
  • Boosting 15

Evaluation 15

  • Validation Example 15
  • Validation Loss 15
  • Validation Set 16
  • Variance 16
  • Cost Function 16
  • Cross-Validation 16
  • Overfitting 16
  • Regularization 16
  • Underfitting 16
  • Evaluation Metrics 17
  • Evaluation Metric 17
  • Regression metrics 17
  • Mean Absolute Error. 17
  • Mean Squared Error. 17
  • R^2 17
  • Classification metrics 17
  • Accuracy. 17
  • Logarithmic Loss. 17
  • Area Under ROC Curve. 17
  • Confusion Matrix. 17
  • Hyperparameter 18
  • Hyperparameter 18
  • Hyperparameter Tuning 18
  • Grid Search 18
  • Random Search 18

Views: 7185

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Milan McGraw on February 16, 2019 at 2:39pm

Any updates on the schedule?

Comment by Reinaldo Nunez on February 15, 2019 at 8:41am

Hi Ajit: My understanding after reading your reply to Bria is that weekend training was on the first week of Feb 2019.

Today is Feb 15, 2019, which mean the weekend for that training already past.

I read the link for topics 1 and 2 below and I don't think there is off-line learning during the weekend. Am I right? do you plan to re-do this training? please advice

1.- The Beginner’s Guide to Deliberate Practice

2.- Primer for Learning Google Colab

Comment by Dashang Makwana on February 14, 2019 at 6:20pm

Hello, can i start now? can you share link. I have joined the group

Comment by ajit jaokar on February 12, 2019 at 11:01am

Hi Brian see comments in the original blog https://www.datasciencecentral.com/profiles/blogs/learn-machinelear... rgds ajit

Comment by Brian Estes on February 12, 2019 at 2:46am

Hi Ajit,

Thank you for sharing this. Can you advise which weekend you will be giving the training?

Thank you

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service