Course Outline
Foundations of Applied Machine Learning
- Distinguishing between statistical and machine learning
- Cycles of iteration and performance evaluation
- Navigating the Bias-Variance trade-off
Supervised and Unsupervised Learning Paradigms
- Overview of ML language features, types, and applications
- Comparing supervised and unsupervised approaches
Core Supervised Learning Techniques
- Building and understanding Decision Trees
- Utilizing Random Forests
- Strategies for model evaluation
Implementing Machine Learning in Python
- Selecting appropriate libraries
- Integrating auxiliary tools
Regression Analysis
- Implementing linear regression
- Addressing generalizations and non-linear relationships
- Practical exercises
Classification Methods
- Review of Bayesian principles
- Applying Naive Bayes
- Understanding logistic regression
- Utilizing K-Nearest Neighbors
- Practical exercises
Validation via Cross-validation and Resampling
- Various cross-validation methodologies
- Applying the Bootstrap technique
- Practical exercises
Unsupervised Learning Strategies
- Implementing K-means clustering
- Real-world examples
- Exploring challenges and alternatives beyond K-means
Neural Networks
- Understanding layers and nodes
- Using Python libraries for neural networks
- Practical application with scikit-learn
- Practical application with PyBrain
- Introduction to Deep Learning
Requirements
Proficiency in the Python programming language is required. A foundational understanding of statistics and linear algebra is also 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
