What will you learn?
Data comprehension
Predictions through supervised learning and data classification
Decision-making through data analysis
Causal inference with Machine Learning
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Duration
Hours
Price
The more team members you enroll in your organization, the more benefits you can acquire. Depending on the number of members enrolled in our courses, you could obtain these benefits:
Push your organization forward with intelligent predictive analyses, methods, and toolkits.
Automate Decision Making in your organization to evade risks and make the best decisions possible.
Discover holistic data comprehension to infer relevant conclusions and drive strategy.
Data comprehension
Predictions through supervised learning and data classification
Decision-making through data analysis
Causal inference with Machine Learning
All the participants who successfully complete their program will receive an MIT Professional Education Certificate of Completion, as well as Continuing Education Units (CEUs)*.
To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours.
*The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.
To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.
Participants in our courses
Countries represented by our participants
Rate the experience as extraordinary
Module 1: Introduction to Machine Learning
We will start by addressing basic concepts such as black box, multidimensional data, prediction, and clustering, to begin to familiarize ourselves with the subject of study.
Module 2: Understanding Data
We will continue with the key characteristics of data sets and begin to identify statistical tools as well as effective visualization ways to extract information from the data.
Module 3: PREDICTIVE MODELS | Regression
In this module, for the first time we will deal with forecasting techniques, what are linear regressions and what are their limitations. We will develop complex skills to overcome obstacles that arise.
Module 4: PREDICTIVE MODELS | Classification
This would be the second block dedicated to prediction, we will learn to make classification models and we will study four methods to make them, so that we can choose what best suits the specific needs of each case.
Module 5: PREDICTIVE MODELS | Neural Networks
This will be the last block dedicated to prediction. We will deal with deep learning and its historical development, as well as the techniques and tools to train neural networks and specific applications.
Module 6: Basics of Decision-Making
At this point, we welcome you to the first block dedicated to decision making. We will analyze different frameworks and models that can be applied when making decisions to select the best option that adapts to different environments.
Module 7: Applications of Decision-Making
This is the second module applied to decision making. We already know the concepts and models, so we will address, as a reference, the processes that belong to the financial field. We will learn how to make recommendations to clients to boost business.
Module 8: Causal Inference
To finish the Machine Learning course, we will do experiments in order to analyze and understand the cause-and-effect relationships in the data sequences, which will allow us to make predictions of time series.
I firmly believe that applying a Machine Learning strategy in organizations is something very necessary in the present, as it allows us to make decisions in an optimized way, and therefore reduces possible strategy errors. Technology and data access give us an opportunity to be able to do this, so we should take maximum advantage of it. This program has assisted me in discovering Machine Learning, understanding data, exploring decision making, and evaluating its effectiveness, knowledge which I will apply in my professional development.
Sonsoles Catalá - Digital Marketing & Communications Specialist, Grupo Municipal Popular
