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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
Industry Case Studies
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)
21 Hours
Testimonials (1)
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
