Linear SVM Classification(Soft Margin) -using Scikit Learn

R Programming Capstone

In this project-based course, we will build, train and test a machine learning model to detect diabetes with XG-boost and Artificial Neural Networks. The objective of this project is to predict whether a patient has diabetes or not based on their given features and diagnostic measurements such as number of pregnancies, insulin levels, Body mass index, age and blood pressure.

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process. • Apply machine learning techniques to explore and prepare data for modeling. • Identify the type of machine learning problem in order to apply the appropriate set of techniques. • Construct models that learn from data using widely available open source tools. • Analyze big data problems using scalable machine learning algorithms on Spark. Software Requirements: Cloudera VM, KNIME, Spark

The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior. 

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

In this 1-hour long project-based course, you will learn how to create a digit recognition web application using streamlit. This project is divided into two stages. In the first stage, you are going to write the training pipeline in which you will load MNIST Handwritten dataset. You will write the training and validation functions in order to train and validate the dataset. Lastly, in this stage you will do inference. In the second stage, you will use the best trained model from the training pipeline and you will use that in your web app. You will create the web user interface using streamlit python library. In this web app a user will draw a digit and given that drawn digit, the best trained model will output the probabilities.

This course continues your study of calculus by focusing on the applications of integration to vector valued functions, or vector fields. These are functions that assign vectors to points in space, allowing us to develop advanced theories to then apply to real-world problems. We define line integrals, which can be used to fund the work done by a vector field. We culminate this course with Green's Theorem, which describes the relationship between certain kinds of line integrals on closed paths and double integrals. In the discrete case, this theorem is called the Shoelace Theorem and allows us to measure the areas of polygons. We use this version of the theorem to develop more tools of data analysis through a peer reviewed project. Upon successful completion of this course, you have all the tools needed to master any advanced mathematics, computer science, or data science that builds off of the foundations of single or multivariable calculus.

In this 1-hour 30-mins long project-based course, you will learn the responsibilities of a Business Analyst such as Learn the basic concepts of data analysis and descriptive statistics. Learn how to manipulate, analyze, and visualize data in Google Sheets using functions, aggregation functions, and logical aggregation functions. and present data using different types of charts. This course works best for learners who wish to learn about Business Analysis and wish to learn about the role of a Business Analyst. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this course you will be introduced to two methods of writing SQL queries that retrieve data from two or more tables. Since one of the functions of a database is to store data in an organized format, many databases are made up of multiple tables. Often, the data output required from the database is made up of data from more than one table. For example, the data that populates a student transcript might come from the Student, Course, and Section tables. While the Student table may provide the student’s name, the name and number of the course might come from the Course table and the specific grade for that course may come from yet another table. While writing SQL queries in SQLiteStudio, you'll learn the SQL syntax required to join tables together as you develop an understanding of how the relationships among tables come into play. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.