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Supervised Learning - Traditional Methods
Supervised Learning - Traditional Methods
Created byAISPRY TUTOR
Last updated 7/2023
English
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What you'll learn
- Students will get in-depth knowledge of Supervised Learning.
- We delve deeply into Predictive Modeling.
- We learn about Classification Models, Shallow Machine Learning Models, Ensemble Models, Regression Models, and the Black Box technique.
- We also recognize the divisions of each modeling method.
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Course content
17 sections • 62 lectures • 12h 6m total length
Requirements
- It is recommended that learners have a prior grasp of the CRISP-ML(Q) Methodology.
- Understanding the remaining procedures in the CRISP-ML(Q) data preparation section.
- Recognize the role of Python programming in EDA.
Description
Data Mining Supervised Learning - Traditional ML Models.
Supervised Learning is a sub-division of the Model Building step of CRISP-ML(Q)
Methodology. Supervised learning is a type of Predictive Modeling that involves
Classification Models, Shallow Machine Learning Models, Ensemble Models, Regression
Models, and the Black Box technique. We have numerous divisions of each modeling
technique.
We thoroughly discuss Probability, Joint Probability, Bayes Rule, and Naive Bayes using a
use case. Naive Bayes is not ideal for larger numeric features because numeric features
must be converted into categorical ones through discretization or bining. It allows the
deletion of missing value entries. This algorithm assumes class-conditional independence.
The probability is zero for new words not seen in training data, making the entire calculation
zero. To encounter this problem, we use Laplace Estimator. French Mathematician
Pierre-Simon Laplace created this algorithm. The default value of the Laplace estimator is 1.
Any value can be used for the Laplace estimator.
K-Nearest Neighbor Classifier is also called Lazy Learner, Memory-Based Reasoning,
Example-Based Reasoning, Instance-Based Learning, Case-Based Reasoning, Rote
Learning, etc. We understand the differences between the k-means algorithm and kNN. We
then understand 1, 2, 3, and 7 Nearest Neighbors. The minimum k value equals 1, and the
maximum equals the number of observations. k is a hyperparameter. We then understand
what a baseline model is, where accuracy is equal to the majority class, and for prediction
models, accuracy is greater than 80%. We further understand the Bias-variance trade-off.
We jump into the applications and importance of k-NN at the end.
The Decision Tree algorithm is a Rules-based algorithm. We understand what a decision
tree is, followed by learning how to build decision trees, then we dive into the greedy
algorithm, building the best decision tree and attribute selection. A decision tree is a tree-like
structure in which an internal node represents an attribute, each branch represents the
outcome of the best and each leaf node represents a class label. There are 3 types of nodes
a root node, a branch node, and a leaf node. So how do we build a decision tree? First, we
use training data to build a model, and then the tree generator determines the following-
- Which variable has to be split at a node, and the value of the split
- The decision to stop or split again has to be made
- Assigning terminal nodes to a label
- A basic or Greedy algorithm is a tree constructed in a top-down recursive
divide-and-conquer manner.
- Further, we analyze Greedy Approach, Entropy, and Information gain.
Who this course is for:
- This course is designed for people who desire to advance their careers in data science.
- It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q).
- Students of all backgrounds are invited to enroll in this program.
- Students with engineering backgrounds are invited to use this program to supplement their education.