Learn Terms used in Machine Learning in Python 312 285 6886
Learn the Basics of Model building without math or programming knowledge
Entry point to Data Science, Machine Learning Career in NYC New York
Requirements
- Python 101 (3-10 hours)
- Data Science 101 (3-10 hours)
- Career in Data Science (3-10 hours)
Description
Machine Learning 101 Class Bootcamp Course NYC
- Python Scikit-learn Library
- Supervised vs Unsupervised Learning
- Regression vs Classification models
- Categorical vs Continuous feature spaces
- Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
- Interpreting Results of Regression and Classification Models (Hands On)
- Parameters and Hyper Parameters
- SVM, K-Nearest Neighbor, Neural Networks
- Dimension Reduction
Hands on:
- Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
- Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
- Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
- Running Logistic Regression on Titanic Data Set
- 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
Course content
