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