If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

In this 1-hour long project-based course, you will gain hands-on experience and learn how to create and maintain relational databases such as MySQL, Oracle and MS Access. Concepts covered include database and table creation, primary keys and foreign keys. If you have some experience with end-user level SQL and want to advance to a designer or database administrator role, this course is for you! Note: Taking my course "Introduction to Relational Database and SQL" before taking this course is highly recommended. Especially if you do not have any previous experience with relational database and SQL.

By the end of this project, you will Build a Node Server backend with Express that will fetch data from a MongoDB database. Often, a dynamic web application is connected to a database on the server side. Node.js serves as the web server used to access the database. Express is a framework for Node.js and acts as middleware to connect the backend Node server to the Client-facing web application. The Client facing web application can then make API (Application Programming Interface) calls to Express to gather data for the dynamic web application. 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.

This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields. The course has been updated. Updates in the new version are : 1) the whole course has moved from Python 2.x to Python 3.x 2) Added manual webpage fetching and parsing. Web API is also added. 3) Improve the content order and enrich details of some content especially for some practice projects. Note: videos are in Chinese (Simplified) with English subtitles. All other materials are in English.

This course is the first course in a series that aims to prepare you for a role working as a programmer. In this course, you will be introduced to the five main concepts in procedural programming: user input, console output, variable declaration and assignment, decision branching and iteration. Labs will allow you to apply the material in the lectures in simple computer programs designed to re-enforce the material in the lesson.

In this hands-on guided project, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the first of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.

Welcome to IBM COBOL – Data and File Management! By enrolling in this course, you are taking a big step in increasing your knowledge and hands on experience with IBM COBOL data and file management. In this course, you will learn the fundamental elements of COBOL code. You will learn the process of working with COBOL data. You will learn handling COBOL files. This course also relational databases in a mainframe, COBOL context. So let’s get started!

Interfaces are a staple of good programming. They are "blueprints" for functionality and allow your code to be much more versatile, portable and understandable. In this one-hour, project-based course, you'll learn how to implement interfaces in C# and, importantly, in the Unity Editor. You'll also create a Unity ScriptableObject that implements an interface and, on that basis, become interchangeable with GameObjects in exposed variables. We will also cover different attributes that can provide convenience in coding and guidelines for designers wiring together the scene. The guided project will work with the following Unity concepts: - Interfaces - Inheritance - ScriptableObjects - Attributes This is an intermediate-level course. As such a certain familiarity with Unity and C# is assumed. If you have never used interfaces or ScriptableObjects, please complete some beginner-level guided projects before attempting this project. This project makes use of the tropical island-themed Unity project created in Create Animation Transitions in Unity (Intro to Animation 2) and in Make an Action Bar in Unity Part 1 - Modular Action System. These compliment this guided project and are recommended for a more well-rounded understanding of the concepts presented herein.

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".

Programming of any kind, including in game-development, can get complicated as the size and scope of a project increase. Investigating bugs and expanding the code with new functionality can be almost impossible if the code-structure is not well-thought-out. In this one-hour, project-based course, we will cover the third principle of "SOLID" programming: Liskov's Substitution Principle ("LSP") states that in methods that refer to a superclass (or base class), any subclass that inherits from the superclass should be useable without error. A common example of this is that if a block of code expects a Mammal class type, then we should be able to send in a Dog or Whale class type and not produce any unexpected behaviour. In the project, we will create some "typical" code that is fairly common in game-development, and then restructure it to become compliant with LSP. This project is part three of a five-part series on SOLID Programming principles, which are: - Single Responsibility - Open-Closed - Liskov's Substitution (this guided project) - Interface Segregation - Dependency Inversion Each of these guided projects stands on its own, but for a full understanding of good coding practices, completing all five guided projects (in order) is recommended.