Apply MACHINE LEARNING ASSOCIATE 2 Course
Tools / Frameworks: Python
Duration: 2 Weeks
Cost: $100
MODULE 4
LOGISTIC REGRESSION MODEL
EVALUATION
- Metrics beyond accuracy: sensitivity
& specificity - Finding the optimal threshold using
ROC curve - Metrics beyond accuracy: precision &
recall
MODULE 5
SUPERVISED LEARNING: K NEAREST
NEIGHBOR (KNN) CLASSIFIER
- Introduction to KNN
- How it works: theory
- Pros and cons of KNN
- Applications of KNN
- Model building KNN in Python Sklearn
- Evaluation: KNN model
MODULE 6
CLUSTERING
STATISTICAL TESTS USING SPSS
- Introduction
- Understanding clustering
- Practical example of clustering –
customer segmentation
K MEANS CLUSTERING
- Introduction
- Steps of the algorithm
- K means algorithm
- K means as coordinate descent
- Visualizing the K means algorithm
- Practical consideration in K means
algorithm - Cluster tendency
K MEANS IN PYTHON CASE: IRIS
DATASET CLUSTERING
- Introduction
- Iris data preparation
- Making the clusters
MODULE 7
HIERARCHICAL CLUSTERING
- Introduction
- Hierarchical clustering
- algorithm
- Interpreting the dendrogram
MODULE 8
UNSUPERVISED LEARNING:
PRINCIPLE COMPONENT ANALYSIS
(PCA)
- The why’s and what’s of PCA
- Building blocks of PCA
- Illustration – finding principal
- components
- Comprehension – calculating
- the principal components
- Singular value decomposition
(SVD)