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.

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:

Upon completion, participants should be able to demonstrate each of the following;

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:

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.

Pre-requisite:
Python Programming II

HRDF Claimable

HDRF claimable. EPP option available for selected credit card.

CADS Certification

Earn certification upon completion.

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

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
- - 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.

Hear from Our Alumni

The training has benefited me a lot with the skills required to be a data analyst and has upskilled me in data analysis. What I like about the program is where real life problems in the office is used as the materials during the trainings which provides more understanding of the topics. Eventually, it has broaden up my market in the career development.

The program started by setting an expectation on Day 1, that it is not a Read-And-Pass syllabus. The beauty of this program is that it requires the trainees to get their hands dirty and do the actual work throughout the course. It is indeed very different from a lot of programs in the market which just produces a certificate in the end without the trainee actually able to do some real stuff. The course is definitely benefiting anyone who really want to do Machine Learning and Data Science, by creating talents who are able to connect human thought with the machine algorithm, being the bridge within the corporate, into the digital worlds.

The training was beneficial as it trains myself to think from another perspective - one that may not be new, but wasn't practiced as often as I'd like. It was fun and interactive thanks to the facilitator, Jay, too. He was very helpful to answer any questions the attendees have. Believe that this training gives the attendees an extra edge by giving them the knowledge and perspective on how to better utilize data to what they want/need to do - for the organization, the business, and even for personal use, of which I believe will bring myself further in my career.

The training has been an eye opener for me. As someone who is not of programming background, it introduces me to the possibilities of what data can do. It has also helped me bridge the difference in thinking between the Business Team and the Data Science Team. Had a great time learning!

This training enhance on my skill of analyzing of using another tools of programming instead of excel and how to model and visualized our data in a better way instead of our traditional way. Especially when involve with a large scale of data. If using excel, there will a lot of time consuming there instead of R. For me, in market nowadays of big data, this course will brings more value and create another point of view on how to visualize our data especially in making any decision. And of course will enhance our career development.

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Applied Machine Learning I

Applied Machine Learning I

Make Data-Based Predictions with Applied Machine Learning

Learning outcome: