Machine Learning Specialist. Artificial Intelligence

Machine Learning is one of the branches of AI Artificial Intelligence

The idea is to develop algorithms capable of learning from data analysis and detecting patterns, allowing them to anticipate or even act accordingly if programmed to do so. Although this is not new—it's already present in many of the technologies we use today—the potential possibilities of Artificial Intelligence have barely been fully realized.

Demo Video

About the BD Analytics Specialist. Machine Learning course.

This is an intensive course where you'll work hard, but where you'll find the satisfaction of mastering a career with a future in just a few months.

In the Machine Learning section, you'll learn to reason like a data scientist, exploring the implications of each application of each algorithm. You'll discuss the suitability of different solutions and see how they can be interpreted to create knowledge and add value to different customer problems. All of this is done through programming in Rstudio, Google Colab, Knime, and H2O.

In the Python section, you'll learn all the programming concepts of today's fastest-growing language. You'll be able to apply it to machine learning, as well as the Internet of Things, blockchain, web development, and more.

MACHINE LEARNING. Predictive Data Analysis.

The mechanism by which Machine Learning performs analysis is the detection of patterns in millions of data points. This is a first important difference from traditional BI, to which we could add, in our understanding, the following three aspects:

Compared to using aggregate data, Machine Learning uses individual data. with defining characteristics of each instance. This way, thousands of variables can be used to detect patterns.
Instead of relying on descriptive analytics, Machine Learning offers predictive analytics. That is, it not only assesses what has happened and extrapolates general trends, but also makes individualized predictions in which details and nuances define future behaviors.
Visualization panels or dashboards are replaced by predictive applications. We are talking about one of the greatest potentials of Machine Learning: Predictive algorithms learn automatically from data and their models can be integrated into applications to provide them with predictive capabilities. The models are periodically retrained so they automatically learn from new data.

Duration:

+200 Hours / 3-5 months

Mode:

In person
Semi-presential
On-line

Prerequisites:

Basic knowledge of computers and the Internet

Certification

Students gain official registration as technicians in a specific technology, which certifies them to practice their profession internationally. We certify our students worldwide.

DP-100 Exam
Designing and Implementing a Data Science Solution on Azure

Job opportunities

Machine Learning employability data is very favorable. Data mining and Big Data experts are the third most employable profession, with a percentage of 89%. Therefore, training in Big Data and AI is one of the best options for professionals who want to expand their knowledge and profile in search of their dream job.

The leading statistics schools do not offer the comprehensive machine learning training required for data scientist positions in their undergraduate programs. Our program is a commitment to providing students with the knowledge needed to apply for jobs in just 3-5 months through intensive training at a much lower cost than a private university and in less time than official master's degrees offered by public universities.

Through Cloud Talent The school connects you with more than 10,000 companies and generates job interviews and internships tailored to your professional profile. Additionally, the Cloud Talent Program gives you access to other complementary SAP certifications at no cost to enhance your qualifications.

Syllabus

Technical Training

UNIT 1. Introduction to R

What is R?

Creating documents with Jupyter Notebook.
Introduction to graph generation using Pandas and Numpy.

Tidyverse

Data import and analysis.
Tidy data.
Data Frame Manipulation.
Groups

Ggplott2

Data visualization and graphics.
Basic diagrams, trellis diagrams, scatter plots, and how to view data distributions.
Customizing diagrams

Other libraries and functions

Additional libraries for computing and data mining.

UNIT 2. Introduction to visualization tools

What is Data Viz?

Principles of data visualization.
Multidimensional data visualization.
Relationships between data sets

Introduction to Power BI

Installing Power BI Desktop.
Data capture and report generation.

Other Power BI concepts

POWER BI Panels

Other visualization tools

Review of other data visualization applications.

UNIT 3. Introduction to other tools

Python Language Fundamentals

Using a development environment.
Variables, data and operators.
Conditional structures.
Loops.
Functions.

Data processing with Python

Composite data.
Lists, tuples and dictionaries.
Compression queries.

H2O

Tool for Machine Learning and statistical learning.
H2O installation.
Load data, training and predictions.

Specific Training

Level I: Statistical fundamentals and introduction to ML

Goals:

Know the main statistical bases on which the ML is based
Understanding the fundamentals of ML
Handle the main data import and cleaning techniques
Use simple statistical models that can be quickly implemented
Getting closer to a model's performance KPIs: cross-validation and other metrics

Contents:

1. CRISP-DM Methodology and Introduction to Statistics

First approach to work methodologies
Basic notions of statistics
Multivariate statistics
Density and distribution functions
Inferential statistics and Bayesian statistics

2. Machine Learning Fundamentals

Using functions
Modules and packages
Application examples

3. Database processing

Examples in R
Examples in Python
Notebooks in Jupyter and other languages
Variable reduction techniques

4. First algorithms

ANOVA
Time series

Level II: Machine Learning Algorithms I. Supervised and unsupervised learning

Goals

Know the main ML algorithms
Differentiate between supervised and unsupervised learning algorithms
Differentiate between algorithms: regression, classification
Bring students closer to the main problems of ML: classification and regression

Contents

5. Clustering algorithms and association rules

Examples
Application

6. Data exploration techniques and Data Viz

Data visualization
Database Exploration

7. Naïve Bayes, KNN and decision trees

Algorithms
Applications

8. Natural language processing and text mining

Introduction
Examples
Applications

Level III: Machine Learning Algorithms II. Unsupervised Learning and Deep Learning

Goals

Know the main ML algorithms
Differentiate between supervised and unsupervised learning algorithms
Differentiate between algorithms: regression, classification
Bring the student closer to the main problems of ML: classification and regression

Contents

9. Basic regression

Linear regression
Logistic regression
Applications

10. Support vector machines and single-layer neural networks

Examples
Applications

11. Adaboosting and Xboosting

Adaboosting: Applications and Examples
Xboosting: Applications and Examples

12. Deep Learning

Fundamentals of Deep Learning
Applied Deep Learning
Deep Learning for texts and images

Microsoft DP100 Machine Learning

Creating Machine Learning Models

Machine learning is the foundation of artificial intelligence and predictive modeling. Learn the fundamental principles of machine learning and how to use common tools and frameworks to train, evaluate, and operate machine learning models.

Data Exploration and Analysis with Python: Introduction. Training and evaluating regression models. Training and evaluating classification models. Training and evaluating clustering models. Training and evaluating deep learning models.

Building predictive models without code with Azure Machine Learning

Machine learning is fundamental to artificial intelligence, and many modern applications and services rely on predictive machine learning models. Learn how to use Azure Machine Learning to build and publish models without writing code.

Using automated machine learning in Azure Machine LearningIntroduction. What is Machine Learning? Creating an Azure Machine Learning workspace. Creating compute resources. Exploring data. Training a machine learning model. Deploying a model as a service. Knowledge test.
Creating a regression model with Azure Machine Learning Designer
Creating a clustering model with Azure Machine Learning Designer

Building AI Solutions with Azure Machine Learning

Azure Machine Learning is a cloud platform designed for training, deploying, managing, and monitoring machine learning models. Learn how to use the Python SDK for Azure Machine Learning to build enterprise AI solutions.

Introduction to the Azure Machine Learning SDK: Getting Started. Azure Machine Learning workspaces. Exercise: Creating a workspace. Azure Machine Learning tools and interfaces. Azure Machine Learning experiments. Exercise: Running experiments. Knowledge check
Training a Machine Learning Model with Azure Machine Learning
Using data in Azure Machine Learning
Using Compute in Azure Machine Learning
Orchestrating Machine Learning with Pipelines
Deploying real-time machine learning services with Azure Machine Learning

Final Project

Course review.
Case study.

Official Certification Seminar

Preparation of exam-type questions.

Subsidized Training for Companies

Cloud Training as an entity registered with code 16753 in the State Registry of Training Entities, Manages and teaches courses within the Company-Programmed Training initiative, Vocational Training for Employment, in accordance with the provisions of Law 30/2015, of September 9.

Cloud Training helps you check your company's credit amount for this year, free of charge.