Course curriculum

  • 1
    Build Your First Predictive Model
    • Resources to be used in this course
    • Course Introduction
    • Introduction to Problem Statement
    • Reading Material - Understanding the Data
    • How do we Make Predictions?
    • Methodology of Evaluating Predictions
    • Introduction to Data Division
    • Building-Benchmark-Models-and-Evaluating-It
    • Introduction to Machine Learning
    • Applications-of-Machine-Learning
    • Types of ML
    • Quiz
  • 2
    Preparing the Dataset for Machine Learning Model
    • ML-workflow
    • Tasks to be Performed
    • Combining Product Attribute Data with POS Data
    • Combining all the tables in the Dataframe
    • Understanding the Combined Data
    • Treating Missing Values - Part 1
    • Treating Missing Values Part - 2
    • Outlier Detection and Treatment
    • Preparing the Dataset for Supervised and Unsupervised Models
    • Generative AI for Data Analysis
  • 3
    Introduction to KNN Algorithm
    • Introduction to KNN
    • Building a kNN model
    • Choosing the Optimal K
    • Different Ways to Calculate Distance
    • Problems with Distance Based Algorithm
  • 4
    Building Your first KNN model
    • Sklearn to build Optimal Process to Build an ML Model
    • Building a Knn classification model and evaluating it
    • Choosing the right K value
    • Overfitting and Underfitting
    • Quiz: KNN model
  • 5
    Evaluation Metrics
    • Understanding Confusion Matrix and Accuracy
    • A deep dive into Precision, Recall and F1 Score
    • Understanding the AU-ROC curve
    • Why do we calculate RMSE
    • Understanding R2 Score and Adjusted R2 Score
    • Train-Test split
    • Train-Test split ratio and limit
    • Cross validation
    • Implementing Cross validation
    • Benchmark Models
    • Quiz: How to Evaluate a Model
    • Course Conclusion
    • Project: Healthcare