Applied Machine Learning I
- Duration: 5 days
- Fee: Request
- Start Date: Request

Make Data-Based Predictions with Applied Machine Learning
In Machine Learning or statistical learning, it is possible to train a computer to perform a specific task. This is extremely powerful specifically when a lot of data related to this task is available. Machine Learning is a must-have for a data scientist to go beyond data analytics. With Machine Learning, a data scientist can make predictions. It is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.
Learning outcome:
- Understand the landscape of machine learning possibilities
- Ability to train a model using simple supervised learning algorithms
- Ability to train and evaluate a model using training and/or test data
Who should attend:
5 days of in depth learning
Face to face with experienced Data Scientist.
Course Methodology
This course will utilize a combination of Presentations and Workshops.
CADS Certification
Earn certification upon completion.
Python Programming II
Undergraduate Degree
Training Track
Enterprise Data Scientist (EDS)
Applied Machine Learning I is one of the modules under our Enterprise Data Scientist (EDS) programme. EDS is a 42- day training program that provides participants with the tools to be key leaders and contributors of a data science team and be able to analyze data to drive informed business decisions.

Details of Subject
- Introduction to Machine Learning – Machine Learning is a broad field. This lecture gives an overview of all the different topics covered by Machine Learning
- Supervised versus unsupervised
- Classification versus regression
- Clustering
- Dimensionality reduction
- Supervised Machine Learning: regression – Regression consists in predicting a size, meaning a number that can be continuous
- Linear regression
- Supervised machine learning: classification – Classification is the sub-field of Machine Learning that consists in building a model from training data (data with correct class) in order to predict the class for other data. In classification, there is only a finite number of classes. Different algorithms can be used for this tasks. We’ll cover here the baseline ones: the one that are easy and yet effective
- K-nearest neighbours
- Decision trees
- Logistic regression
- Evaluation –
How to assess that a model is good enough, how to compare two different solutions? This is the goal of evaluation. We will see that there are multiple possible metrics. And that the choice of a metric depends on the business problemWe will also see how to split the dataset between test and training for a fair evaluation
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- Metric
- Cross validation
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Lead Instructor

CADS Certification
EDS CADS Certified Enterprise Data Scientist
Certification information for this module & track will be made available soon.

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Applied Machine Learning I