AI Machine Learning Essentials: Supervised Models with Capstone Project

Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox

In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, spam filtering, etc.

In this training workshop, the participant learn how to use Python to perform supervised learning, an essential component of machine learning. You’ll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You’ll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

In supervised learning, models are trained using labelled dataset, where the model learns about each type of data. Once the training process is completed, the model is tested on the basis of test data (a subset of the training set), and then it predicts the output.

 

  • Module 1: Introduction to Machine learning
    • Artificial Intelligence
    • Types of Machine Learning
    • Overview of Supervised Machine Learning
    • Exploratory data analysis
  • Module 2: Classification
    • Overview of classification
    • The classification Challenge
    • Measuring model performance
    • Lab session:
      • K-Nearest Neighbors: Fit
      • K-Nearest Neighbors: Predict
      • The digits recognition dataset
      • Overfitting and underfitting
    • Module 3: Regression
      • Introduction to regression
      • The basics of linear regression
      • Cross-validation
      • Regularized regression
      • Lab-session:
        • Importing data for supervised learning
        • Fir & predict for regression
        • Train/test split for regression
        • 5-fold cross validation
        • K-Fold CV compression
        • Regularization
      • Module 4: Fine-tuning your Model
        • How good is your model?
        • Logistic regression and the ROC curve
        • Area under the ROC curve
        • Hyperparameter tuning
        • Hold-out set for final evaluation
        • Lab Session
          • Metrics for classification
          • Building a logistic regression model
          • Plotting an ROC curve
          • AUC computation
          • Hyper-parameter tuning
        • Module 5: Pre-processing and pipelines
          • Pre-processing data
          • Handling missing data
          • Centering and scalling
          • Lab session:
            • Exploring cateforixal features
            • Creating dummy variables
            • Regression with categorical features
          • Module 7: Case Study of Supervised Machine
          • Module 8: Capstone Project

 

  • Presentation
  • Ice-breaking
  • Brainstorm
  • Lab session
  • Group discussion
  • Quiz
  • Questionnaire
  • Use case
  • Case study
  • Q&A

 

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