Applied Machine Learning I

Track: Enterprise Data Scientist (EDS)
The objective of this course is twofold. First to understand the statistical concepts related to different machine learning algorithms. Second to learn how to use those algorithms effectively and when to use them.

Make Data-Based Predictions with Applied Machine Learning

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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:

Upon completion, participants should be able to demonstrate each of the following;
  1. Understand the landscape of machine learning possibilities
  2. Ability to train a model using simple supervised learning algorithms
  3. Ability to train and evaluate a model using training and/or test data

Who should attend:

Professionals that work with data

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.

Pre-requisite:
Python Programming II
Minimum Qualification:
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

  1. 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
  2. Supervised Machine Learning: regression – Regression consists in predicting a size, meaning a number that can be continuous
    • Linear regression
  3. 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
  4. 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
      • Metric
      • Cross validation
    *All algorithms will be taught in interactive mode on different kind of data (texts, images) giving the trainees experiences with different features.

Lead Instructor

Laleh Asadzadeh Esafahani
Laleh received her MSc. in Computer Science in 2016 from Southern Illinois University. Her research focused on the modelling and analysis of social network users’ activities. Laleh then was a data scientist at Potentia Analytics Inc. and was in charge of developing and implementing several research projects that enhance the quality of service in hospitals. Before that, she received her MSc. In Mathematics in 2001 from Sharif University of Technology. Her Masters thesis was on defining sets in combinatorial structures and their applications in cryptography. After graduation, Laleh was a Mathematics instructor, researcher, and research mentor at Isfahan Mathematics House. She specializes in Mathematical Modelling, Statistical Analysis, Machine Learning, and programming languages, such as Python and R.

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