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Course Outline
Introduction to Neural Networks
Introduction to Applied Machine Learning
- Distinguishing statistical learning from machine learning
- Iteration and evaluation processes
- Understanding the bias-variance trade-off
Machine Learning with Python
- Selecting appropriate libraries
- Utilizing supplementary tools
Machine Learning Concepts and Applications
Regression
- Linear regression
- Generalizations and nonlinearity
- Practical use cases
Classification
- Review of Bayesian principles
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Use Cases
Cross-validation and Resampling
- Cross-validation methodologies
- Bootstrap methods
- Use Cases
Unsupervised Learning
- K-means clustering
- Examples
- Challenges in unsupervised learning and methods beyond K-means
Brief Overview of NLP Methods
- Word and sentence tokenization
- Text classification
- Sentiment analysis
- Spelling correction
- Information extraction
- Parsing
- Semantic extraction
- Question answering
Artificial Intelligence and Deep Learning
Technical Overview
- R versus Python
- Caffe versus TensorFlow
- Overview of various Machine Learning libraries
Industry Case Studies
Requirements
- Fundamental knowledge of business operations and technology
- A solid grasp of software and systems
- Basic proficiency in Statistics (at an Excel level)
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.
