Description

Machine Learning 101 Class Bootcamp Course NYC

  1. Python Scikit-learn Library
  2. Supervised vs Unsupervised Learning
  3. Regression vs Classification models
  4. Categorical vs Continuous feature spaces
  5. Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
  6. Interpreting Results of Regression and  Classification Models (Hands On)
  7. Parameters and Hyper Parameters
  8. SVM, K-Nearest Neighbor, Neural Networks
  9. Dimension Reduction

Hands on:

  1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
  2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
  3. Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
  4. Running Logistic Regression on Titanic Data Set
  5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset

Who this course is for:

  • Python and Data Analytics
  • Programmers with no knowledge of Maths
  • New Entrants in Data Science Field

Requirements

  • Python 101 (3-10 hours)
  • Data Science 101 (3-10 hours)
  • Career in Data Science (3-10 hours)
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