Supervised Learning – Traditional Methods

Supervised Learning - Traditional Methods
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.
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 involvesClassification Models, Shallow Machine Learning Models, Ensemble Models, RegressionModels, and the Black Box technique. We have numerous divisions of each modelingtechnique.We thoroughly discuss Probability, Joint Probability, Bayes Rule, and Naive Bayes using ause case. Naive Bayes is not ideal for larger numeric features because numeric featuresmust be converted into categorical ones through discretization or bining. It allows thedeletion 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 calculationzero. To encounter this problem, we use Laplace Estimator. French MathematicianPierre-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, RoteLearning, etc. We understand the differences between the k-means algorithm and kNN. Wethen understand 1, 2, 3, and 7 Nearest Neighbors. The minimum k value equals 1, and themaximum equals the number of observations. k is a hyperparameter. We then understandwhat a baseline model is, where accuracy is equal to the majority class, and for predictionmodels, 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 decisiontree is, followed by learning how to build decision trees, then we dive into the greedyalgorithm, building the best decision tree and attribute selection. A decision tree is a tree-likestructure in which an internal node represents an attribute, each branch represents theoutcome of the best and each leaf node represents a class label. There are 3 types of nodesa root node, a branch node, and a leaf node. So how do we build a decision tree? First, weuse 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 recursivedivide-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.
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