In this three-day intensive Machine Learning training course, you will be given a comprehensive insight into the principles, developments and secrets of this powerful method for data analysis.
Join Louis Dorard, co-founder of PAPIs, to learn how to integrate Machine Learning into your applications through the use of cutting-edge industry techniques. In this Machine Learning workshop, you will gain an understanding of the possibilities and present boundaries of modern Machine Learning, and how to put your knowledge to work on real cases.
Learn best principles when preparing data and creating Machine Learning models so that you can best evaluate them in your domain of application, optimize them, and then deploy them. Finally, learn how to adopt a top-down, results-first and experimentation-driven approach, focusing on practical techniques applied to concrete examples to consolidate the knowledge you accrue along the course of the three days.
- Develop an expert knowledge of how to integrate Machine Learning into your applications -
Who you will be learning with
Programmers, DevOps Engineers and those with a basic knowledge of the Python syntax attend this Machine Learning course to bring back insights to their organizations as well as develop their understanding of using ML for data analysis. Basic knowledge of scientific calculus, linear algebra and statistics is beneficial for those wanting to get the most out of the theory (but don’t fret if you don’t have this background!).
How to apply these skills
Come away from this ML course with the knowledge and skills to leverage the power of Machine Learning to improve your applications.
Book early to receive a discount on the course price and in doing so you will not only commit to growing your own skillset, but help us grow our community of over 140,000 passionate techies.
Learn how to:
- Understand the possibilities and limitations of Machine Learning
- Build predictive models from data, with Decision Trees and Random Forests
- Analyze models' behavior, errors, performance, and optimize their parameters
- Transform text variables into useful numerical representations for ML
- Package and deploy models to production with APIs
Introduction to Machine Learning
- Key ML concepts and terminology
- Formalizing supervised learning problems: classification and regression
- Possibilities and example use cases (web applications, mobile, enterprise data science)
- Learning techniques: Nearest Neighbors and Decision Trees
- [Hands-on] Introduction to Jupyter notebooks
- [Exercise] Decision Trees in scikit-learn (open source ML library) and BigML (ML-as-a-Service tool)- Model creation on classification and regression datasets- Visualization and interpretation
- Performance criteria for ML models and evaluation procedure* Aggregate metrics for regression (MAE, MSE, R-squared, MAPE) and classification (accuracy, confusion and cost matrices, precision, recall, AUC)
- [Exercise] Evaluating models with Python, scikit-learn and BigML on previous datasets* [Hands-on] Procedure for individual error inspection and interpretation
- [Hands-on] Tuning model complexity: under-fitting vs. over-fitting
- Improving predictions with Ensembles; application to Decisions Trees: Random Forests
- [Exercise] Comparing multiple evaluations of Decision Trees and Random Forests on previous datasets* [Hands-on] Optimizing classifiers by tuning probability thresholds and trading off between competing metrics* Embracing randomness with cross-validation* [Exercise] Tuning all models' hyper-parameters with grid search and competing in a Kaggle challenge
ML on text — Natural Language Processing
- [Hands-on] Text pre-processing tips with the NLTK library
- [Hands-on] Feature extraction (bag of words and n-grams) and feature selection with scikit-learn
- [Exercise] Creating and optimizing a model to detect fake hotel reviews
- [Hands-on] Why and how to use REST APIs for ML use in production
- [Exercise] Deploying your own Python models as APIs with the Flask library* [Hands-on] Using your API with curl, Postman, and to fill in missing values in a spreadsheet program* Critical overview of open source and cloud ML products and deployment solutions
- Recap of key take-aways
- Other ML techniques
- Introduction to neural networks and usage of BigML's automated deep learning feature- Unsupervised learning: clustering and anomaly detection- Time series forecasting (by reduction to a regression problem)- Recommender systems (by reduction to a classification problem)* Resources to go further and customized suggestions
Attendees should have:
- Programming experience and basic knowledge of the Python syntax. Code samples will be provided throughout the course; the exercises in this course that involve programming can be done by combining and adapting these samples. Please consult Codeacademy's Learn Python and Robert Johansson's Introduction to Python programming (in particular the following sections: Python program files, Modules, Assignment, Fundamental types, Control Flow and Functions) to learn or revise Python's basics.
- Usage of a spreadsheet program (e.g. Microsoft Excel)
- Basic knowledge of scientific calculus, linear algebra and statistics (undergraduate level) will be useful to better understand some of the theory behind learning techniques, but it isn’t a hard requirement.
Bring your own hardware
To participate in this Machine Learning course you are required to bring your own laptop for practical work, with Python 3 and a recent version of Chrome.
If you are unable to bring your own laptop and you let us know at least 2 weeks prior to your attendance of this course, our team will be able to provide you with a laptop pre-installed with the above environment.