Machine Learning Concepts for Entrepreneurs and Managers Training Course

Introduction to Neural Networks Introduction to Applied Machine Learning

• Statistical learning vs. Machine learning
• Iteration and evaluation
• Bias-Variance trade-off

• Choice of libraries
• Add-on tools

Regression

• Linear regression
• Generalizations and Nonlinearity
• Use cases

Classification

• Bayesian refresher
• Naive Bayes
• Logistic regression
• K-Nearest neighbors
• Use Cases

Cross-validation and Resampling

• Cross-validation approaches
• Bootstrap
• Use Cases

Unsupervised Learning

• K-means clustering
• Examples
• Challenges of unsupervised learning and beyond K-means

• word and sentence tokenization
• text classification
• sentiment analysis
• spelling correction
• information extraction
• parsing
• meaning extraction
• question answering

• R v/s Python
• Caffe v/s Tensor Flow
• Various Machine Learning Libraries