Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
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)
Testimonials
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.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Related Courses
AdaBoost Python for Machine Learning
14 hours
AutoML with Auto-Keras
14 hours
AutoML
14 hours
Google Cloud AutoML
7 hours
AutoML with Auto-sklearn
14 hours
Pattern Recognition
21 hours
DataRobot
7 hours
Data Mining with Weka
14 hours
H2O AutoML
14 hours
Pattern Matching
14 hours
Machine Learning with Random Forest
14 hours
Apache SystemML for Machine Learning
14 hours