15Jun

Machine Learning using Python - Weekend Batch

8 weekend classroom session

Weekend Class | 8 Hours/Day | Duration : 1 Month
Start Date: Jun 15, 2019

Course Fee :  29999  INR

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Training Contents

Module I

  • Unit I: Machine Learning Introduction
    • Introduction to ML Problems
    • ML Terminologies
    • ML Project Workflow
    • ML Real Life Examples
  • Unit II: Jupyter Notebook Introduction
    • Working with Jupyter Notebook
    • Markdown and Code Blocks
    • Keyboard Shortcuts
  • Unit III: Python Basics
    • Python Syntax
    • Basic Datatypes
    • Basic Data Structures
  • Unit IV: Python Advanced
    • Numpy Arrays
    • Plotting using Matplotlib
    • Pandas Dataframes
    • Introduction to Scikit Learn Package

Module II

  • Unit I: Regression Modeling
    • Introduction
    • Modeling Concept
    • Example Problem - Housing Price
  • Unit II: Simple Linear Regression
    • Error Metric - SSE, MSE, R Squared
    • Least Square Algorithm
    • Gradient Descent Algorithm
    • Implementation using scikit-learn
  • Unit III: Multiple Linear Regression
    • Dummy Variables
    • Error Metric - SSE,MSE, R Squared
    • Gradient Descent Algorithm
    • Feature Selection (Incremental)
    • Implementation using Scikit-Learn
  • Unit IV: Polynomial Regression
    • Non-Linear Relationship
    • Higher Order Terms
    • Feature Selection
    • Modeling Concepts - Avoid Overfitting
    • Implementation using Scikit-Learn

Module III

  • Unit I: Classification Modeling
    • Introduction to Classification Models
    • Error Metrics: Accuracy Score
    • Confusion Matrix
    • Type 1 and Type 2 Errors
    • Decision Boundaries
  • Unit II: Logistic Regression
    • Discrete Outcomes
    • Logit Function
    • Probability Scores
    • Implementation using scikit-learn
  • Unit III: Support Vector Machines
    • Support Vectors
    • Decision Boundary
    • Kernel Trick
    • Hyperparameters and Model Tuning
    • Implementation using Scikit-Learn
  • Unit IV: Decision Trees
    • Entropy
    • Using Entropy in Classification
    • Information Gain
    • Tree Pruning
    • Implementation using Scikit-Learn
  • Unit V: Random Forests
    • Bias Variance Errors
    • Ensembling
    • Randomness in Random Forest
    • Hyperparameters
    • Implementation using Scikit-Learn

Module IV

  • Unit I: Cluster Modeling
    • Introduction to Clustering
    • Distance Measures
    • Error Metrics
    • Analyzing Cluster Outputs
  • Unit II: Hierarchical Clustering
    • Agglomerative Method
    • Divisive Method
    • Understanding Dendrogram for Obtaining Clusters
    • Implementation using Scikit-Learn
  • Unit III: K-Means Clustering
    • Distance Measures
    • Centroids and their importance
    • Steps Involved in K-Means
    • Local Optima Problem
    • Implementation using Scikit-Learn

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