Course Outline
Introduction to Applied Machine Learning
- Differences between statistical learning and machine learning
- Processes for iteration and evaluation
- Understanding the Bias-Variance trade-off
Supervised and Unsupervised Learning
- Overview of Machine Learning languages, types, and case studies
- Comparing supervised and unsupervised learning approaches
Supervised Learning
- Construction of Decision Trees
- Implementation of Random Forests
- Methods for model evaluation
Machine Learning with Python
- Selection of appropriate libraries
- Integration of auxiliary tools
Regression
- Foundations of linear regression
- Generalizations and handling nonlinearity
- Practical exercises
Classification
- Refresher on Bayesian concepts
- Application of Naive Bayes
- Logistic regression techniques
- K-Nearest neighbors algorithm
- Practical exercises
Cross-validation and Resampling
- Various cross-validation methodologies
- Bootstrap techniques
- Practical exercises
Unsupervised Learning
- K-means clustering methods
- Real-world examples
- Addressing challenges in unsupervised learning beyond K-means
Neural Networks
- Understanding layers and nodes
- Neural network libraries in Python
- Implementation using scikit-learn
- Implementation using PyBrain
- Introduction to Deep Learning
Requirements
Proficiency in the Python programming language is required. Additionally, a foundational understanding of statistics and linear algebra is strongly recommended.
Testimonials (7)
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
The trainer was a practitioner with a lot of experience and had a very good knowledge of the material.
Witold Iwaniec - City of Calgary
Course - Machine Learning with Python – 4 Days
The trainer because he could handle almost every subject and situation.
Florin Babes - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
The manner in which the trainer explained the concepts, his positive and welcoming attitude and the real-world examples provided for each exercise.
Ovidiu Calita - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
Very good training session with nice documentation and exercises and Kristian did it like a professional he is.
Adrian Boulescu - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
I like that he is very skilled and has lots of knowledge in his domain.
dan dumitriu - eMAG IT RESEARCH SRL
Course - Machine Learning with Python – 4 Days
rich documentation and many resources as course support, as well as resources for the post-course learning process
