Exegetic Analytics is a Data Science consultancy specialising in data acquisition and augmentation, data preparation, predictive analytics and machine learning. Our services are used by a range of industries from Education to Security, Food Delivery to Politics. Our consultants are based in Durban and Cape Town and we engage with clients all over the world. Our products and services are used by a multitude of industries including Aerospace, Education, Finance, Food and Transport.
Exegetic Analytics also offers training, with experienced and knowledgeable facilitators. Our courses focus on practical applications, working through examples and exercises based on real-world datasets.
All of our training packages include access to:
- our online development environment and
- detailed course material which participants will have continued access to even once the training has concluded.
For more information about what we do, you can refer to our website.
These are some of the companies who have benefitted from our trainning:
Take a look at our full list of courses to see what other training we have on offer.
If this proposal is of interest to you or you would like to hear more about what we do you can get in touch on email@example.com or +27 (0)83 350 7699.
2. Course Description
|Who should attend?||The course is aimed at students, academics and professionals who want to use Machine Learning.|
|Objectives||Machine Learning is a big (and rather hot!) topic. In this course you’ll learn how to apply Machine Learning to two types of problems: Classification and Regression. Although Machine Learning models are often treated as black boxes, you’ll learn (in an unthreatening, low-math way) how these models work. You’ll also learn how to appropriately prepare your data, how to build and test a model, and how to generate predictions.|
|Outcomes||Participants will be able to build Classification and Regression models on real world data. They will understand how the models work and how to interpret model predictions.|
|Requirements||Participants are assumed to be familiar with working with data in Python using Numpy and Pandas.|
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3. Course Outline
- What is Machine Learning?
- An overview of scikit-learn
- Nearest Neighbours
- Binary and multiclass classifiers
- Performance measures
- Evaluation & Model Selection
- Model Tuning
- Grid Search
- Randomised Search
- Random Forest
- Gradient Boosting
- Model Interpretation