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Petanux Academy

Petanux Artificial Intelligence Cources

Python programming for IoT and data science

Internet of Things (IoT) and Data Science are two emerging technologies that empower businesses to have better intuitions and enhance their decision-making process by bringing access to massive amounts of data and analyzing them. IoT technology enables interconnection between devices varying from a household tool to advanced industrial ones which are gathering tones of Data every second. With the help of Data Science, you can utilize and put these enormous and even streaming data in use. During this course, you will learn how to collect, clean, analyze, and store IoT data by executing effective Python programming, using Data Science approaches. Python is one of the most popular programming languages for Data Science. It is used widely for applying Data Science methods and running experiments over a large amount of Data. Also, it is equipped with powerful libraries to facilitate coding. This course will guide you to build Data Science models to detect patterns or anomalies over IoT data with the help of these libraries. In addition, to educate you practically we have designed many virtual laboratories and hands-on projects that concentrate on applications of Data Science on IoT datasets. Real-world problems are presented with given IoT datasets and you are asked to design a model for learning by applying Data Science techniques. Finally, you should analyze its results and evaluate your model. This course is planned for students of Computer Science, Mathematics, and any other related fields in bachelor or master degree or those who want to learn Python programming for IoT and Data Science on a self-learning basis. While to use this material you do not need to have any background knowledge, for implementing the hands-on project you need to have access to the Anaconda environment or Google Colaboratory. Students and participants of this course are expected to learn the basics of Python Programming, fundamentals of Data Science algorithms, how to collect and store IoT data, and get familiar with popular Python libraries (for e.g Pandas, Matplotlib, Numpy, Tensorflow, Sklearn). This leads them to be able to use Python Programming to work with data and manipulate it. Also, empowers them to build and initiate Data Science Models on IoT datasets using Python on their own after finishing the course. To reach the course objectives and ensure the proper learning, the course engages diverse activities including, watching videos, reading the materials, participating in self-assessment, and completing the hands-on project.

Fundamental Machine Learning

This course provides a broad introduction to modern, approved, and most favorable Machine Learning (ML) approaches, their obstacles, and ways of tackling them. It helps you empower computers to learn and discover relationships on data. During this course, you will learn how to create ML models and modify their parameters by applying ML Algorithms to some given data to reach an objective. Furthermore, you will not only learn what the theoretical ideas behind Machine Learning Algorithms are, but also gain hands-on experience by implementing these algorithms on real-world problems. For the theoretical part, we plan to address the Basics of ML, Supervised and Unsupervised learning algorithms and methods, ML challenges, and ways of dealing with them. Moreover, This course covers computational learning theory which gives you mathematical insights, enabling you to conduct novel and innovative approaches for evaluating ML Algorithms. To present these topics, PowerPoint slides mixed with some further video tutorials from the web are offered. In addition, to educate you practically we have designed many virtual laboratories and hands-on projects that concentrate on applications of ML. Real-world problems are presented with given datasets and you are asked to design a model for learning with ML techniques. Finally, you should analyze your results and outputs. This course is designed with a practical perspective for everyone with or without previous knowledge of Machine Learning. Whether you want to enhance your comprehension or escalate your critical thinking and problem-solving skills in the field of Machine Learning, this course would be an asset for you. Although you can use the course content if you are studying Computer Science, Mathematics, or any other related fields in bachelor or master degree or if you just want to learn ML technologies on a self-learning basis, you should be familiar with linear algebra, statistics, and Python Programming. Students and participants of this course are expected to learn the basics of ML, theoretical concepts and algorithms of ML, ML obstacles, and how to design ML models. This leads them to be able to apply these techniques to master ML problems and initiate novel models and strategies on their own after finishing the course. To escalate the outcome of the course, it evaluates and conducts a diverse range of materials and media including, video lectures, reading materials, quizzes, and Jupyter Notebooks..

Big data management

Big data management is one of the state-of-the-art technologies that refer to organizing, controlling, storing, and processing vast and complex data, whether the data is structured, unstructured, or semi-structured. Big Data brings insights to drive novel techniques and strategies in industrial and scientific works as well as establishing high data quality for big data analysis. This course provides modern, approved, and most favorable Big Data Management methods, challenges, and ways of addressing them. This course prepares you to utilize Big Data Management strategies and techniques to govern big data, locate valuable information, and analyze data and results. To this aim, this course covers both the theoretical and practical parts of Big Data Management. For the theoretical part, we plan to address the basics of Big Data Management, Data Mining, Data Stream Mining, finding frequent items, and the Map- reduce technique by using PowerPoint slides mixed with some further video tutorials from the web. In addition, to educate you practically we have designed a virtual laboratory that concentrates on applications of Big Data Management, using Apache Spark tool. This course is planned for students of Computer Science, Mathematics, and any other related fields in bachelor or master degree or those who want to learn Big Data Management technologies on a self-learning basis. To use this material you need to have a solid knowledge of Algorithm Design and be familiar with Linear Algebra and Statistics. Besides, for implementing the hands-on project while you are not expected to be an expert in coding, it is necessary to know some basics and have access to the Apache Spark environment. Students and participants of this course are expected to learn the basics of Big Data Management, concepts and algorithms of Big Data Management, get familiar with Data Mining/ Data Stream Mining, and learn how to work with Big Data Management tools and technologies. This leads them to be able to apply these techniques to organize data, drive information from data, maintain high data quality, and practice Big Data Management on their own after finishing the course. To reach the course objectives and ensure the proper learning, the course engages diverse activities including, watching videos, reading the materials, participating in quizzes, and completing the final project. This enables you to not only understand the basics of Big Data Management approaches but also apply them to develop a big data architecture, utilize big data management tools, and overcome real-world problems.