In this online Python course from O'Reilly Media, you will learn how to program with the popular development language. This tutorial is designed for the beginner, and you do not need to have any experience at all with programming or development in order to learn how to program with Python using this video tutorial. Some of the topics that this course covers throughout the ultimate Python for beginners training include installing Python, data types and creating variables, input and output, decision making and repetition, iterators, list comprehension and functions. He also covers variable scope, modules - creating and using pre-built ones, object oriented programming, inheritance, exception handling and using data structures. By the completion of this python for beginners video based training course on Python programming, you will be comfortable with Python and how to apply it to developing applications.

About this course: Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer? Do each of these objects meet the given requirements?

Welcome! "Machine Learning Prerequisites: Python Pandas & Matplotlib" is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field. Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. Machine Learning Prerequisites: Python Pandas & Matplotlib offers in-depth video tutorials in which we'll dive into tons of different datasets, short and long, broken and pristine. I'll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! .

Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15--85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.

This course provides both theoretical knowledge and practical skills in Big Geospatial Data Analysis with Google Earth Engine. In this course, you will be given hands on practical exercises to master analyzing big geospatial data on the cloud. You will learn to access, process and analyze satellite data including Landsat, MODIS, and Sentinel and others using an open source platform. You will also learn to classify satellite images using machine learning algorithms. You will also have access to the lab exercise scripts as part of this course.

Course is very good I learnt about a lot of things related to clustering. Actually it is a very good introductory course in clustering compared to the resources available online in general. Apart from these things I truly enjoyed and learned many new things.

CATIA (Computer Aided Three-Dimensional Interactive Application) is a professional CAD / CAM-based software produced by the French company Dassault Systèmes. Especially the automotive sector, aircraft production and other simulation sectors that can respond to the needs of the program is used more often and every sector is appealing to cutting. Almost all automotive industry in the world is using computer aided design and manufacturing. Catia ensures that the products that are to be produced can be processed in the virtual environment during the production process. After a product is designed by the designer in the Catia program, the ergonomist explores the ergonomics of the design.

About this course: Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models. In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future ML projects.

Have you always wanted to mine twitter data? Then this course is for you. This course presents example of text mining with R. Twitter text of @pycon and @udemy is used as the data to analyze. It starts by extracting text from Twitter. The extracted text is then transformed to a corpus and then a document-term matrix.

In this course we will talk about the past, present and the future of AI. This course covers all the introductory topics to AI to get you started on the path of becoming AI specialist. You will learn about main philosophy, history and approaches of AI as well as its applications.