Thanks to this Relearning-based Postgraduate certificate, you will use the most innovative techniques of Machine Learning and Data Mining to solve real-world problems" 

Postgraduate certificate are two areas in constant growth, with a high demand in various industries. The scientific community frequently conducts research that leads to new techniques or tools to optimize intelligent algorithms. This enables advances in areas such as healthcare, where these mechanisms are used for the interpretation of medical images, early detection of pathologies and personalization of therapeutic treatments. In this sense, Artificial Intelligence professionals require constant updating of their knowledge in order to provide the best services to their clients. At the same time, they need to obtain practical skills for the correct data processing and model evaluation.   

In this context, TECH implements a Postgraduate certificate in Machine Learning and Data Mining for specialists who want to expand their career horizons. Designed by subject matter experts, the curriculum will delve into issues ranging from knowledge discovery processes to data preprocessing. This will equip students with advanced methods for classifying instances of information based on specific attributes. Likewise, the syllabus will examine the operation of Neural Networks given their importance in executing algorithms that carry out specific tasks from data. On the other hand, the didactic contents will focus on Natural Language Processing so that graduates will benefit from descriptive analysis and corpus creation.  

This program will equip students with robust skills so that they can apply them immediately to their daily practice and overcome the challenges they face in the course of their work.  All of this is supported by a first-rate teaching staff, as well as TECH's revolutionary methodology: Relearning.  This learning system is based on the repetition of key concepts to ensure that students acquire knowledge in an optimal, progressive and natural way.  

You will develop Backpropagation Algorithms through 150 hours of the best digital teaching" 

This Postgraduate certificate in Machine Learning and Data Mining contains the most complete and up-to-date program on the market. The most important features include: 

• Development of 100 simulated scenarios presented by experts in Machine Learning and Data Mining 
• Its graphic, schematic and practical contents, with which they are conceived, gather scientific and practical information on Machine Learning and Data Mining
• News on the latest developments in Machine Learning and Data Mining 
• It contains practical exercises where the self-assessment process can be carried out to improve learning 
• Interactive learning system based on the case method and its application to real practice 
• All this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will delve into Regression and Continuous Response Models to predict the behavior of continuous variables as a function of other explanatory variables"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.  

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will enrich your daily procedures with the most innovative techniques in knowledge discovery processes"

You will accomplish your goals with the help of TECH's didactic tools, including interactive summaries and specialized readings"

This Postgraduate certificate in Machine Learning and Data Mining will provide students with a solid education in these areas of data science, which are essential in today's information age. The curriculum will delve into the exploration and preprocessing of data, so that students can build efficient predictive models. Likewise, the academic materials will offer the most advanced techniques in Classifier Evaluation, so that graduates can measure the performance of the models and diagnose possible problems. The program will also address the Clustering strategy in order to achieve clustering of unlabeled objects. 

The university program contains case studies that will enhance your skills in Data Exploration and Preprocessing” 

Module 1. Machine Learning and Data Mining

1.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning 

1.1.1. Key Concepts of Knowledge Discovery Processes 
1.1.2. Historical Perspective of Knowledge Discovery Processes 
1.1.3. Stages of the Knowledge Discovery Processes 
1.1.4. Techniques Used in Knowledge Discovery Processes 
1.1.5. Characteristics of Good Machine Learning Models 
1.1.6. Types of Machine Learning Information 
1.1.7. Basic Learning Concepts 
1.1.8. Basic Concepts of Unsupervised Learning 

1.2. Data Exploration and Pre-processing 

1.2.1. Data Processing 
1.2.2. Data Processing in the Data Analysis Flow 
1.2.3. Types of Data 
1.2.4. Data Transformations 
1.2.5. Visualization and Exploration of Continuous Variables 
1.2.6. Visualization and Exploration of Categorical Variables 
1.2.7. Correlation Measures 
1.2.8. Most Common Graphic Representations 
1.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction 

1.3. Decision Trees 

1.3.1. ID3 Algorithm 
1.3.2. C4.5 Algorithm
1.3.3. Overtraining and Pruning 
1.3.4. Analysis of Results 

1.4. Evaluation of Classifiers 

1.4.1. Confusion Matrixes 
1.4.2. Numerical Evaluation Matrixes 
1.4.3. Kappa Statistic 
1.4.4. ROC Curves 

1.5. Classification Rules 

1.5.1. Rule Evaluation Measures 
1.5.2. Introduction to Graphic Representation 
1.5.3. Sequential Overlay Algorithm 

1.6. Neural Networks 

1.6.1. Basic Concepts 
1.6.2. Simple Neural Networks 
1.6.3. Backpropagation Algorithm 
1.6.4. Introduction to Recurrent Neural Networks 

1.7. Bayesian Methods 

1.7.1. Basic Probability Concepts 
1.7.2. Bayes' Theorem 
1.7.3. Naive Bayes 
1.7.4. Introduction to Bayesian Networks 

1.8. Regression and Continuous Response Models 

1.8.1. Simple Linear Regression 
1.8.2. Multiple Linear Regression 
1.8.3. Logistic Regression 
1.8.4. Regression Trees 
1.8.5. Introduction to Support Vector Machines (SVM) 
1.8.6. Goodness-of-Fit Measures 

1.9. Clustering 

1.9.1. Basic Concepts 
1.9.2. Hierarchical Clustering 
1.9.3. Probabilistic Methods 
1.9.4. EM Algorithm 
1.9.5. B-Cubed Method 
1.9.6. Implicit Methods 

1.10. Text Mining and Natural Language Processing (NLP) 

1.10.1. Basic Concepts 
1.10.2. Corpus Creation 
1.10.3. Descriptive Analysis 
1.10.4. Introduction to Feelings Analysis 

A unique training experience, key and decisive to boost your professional development"