Duration
21 hours (usually 3 days including breaks)
Requirements
- Should have basic knowledge of business operation, and technical knowledge as well
- Must have basic understanding of software and systems
- Basic understanding of Statistics (in Excel levels)
Overview
This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
Course Outline
Introduction to Neural Networks
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
Machine learning Concepts and Applications
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
Short Introduction to NLP methods
- word and sentence tokenization
- text classification
- sentiment analysis
- spelling correction
- information extraction
- parsing
- meaning extraction
- question answering
Artificial Intelligence & Deep Learning
Technical Overview
- R v/s Python
- Caffe v/s Tensor Flow
- Various Machine Learning Libraries