By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.

Algorithm used in everywhere. People Don't know how Complex Algorithm they are executing when doing there day to day task like: Riding a Bi-Cycle, Travelling from one place to another even Watering Garden. If you are Coder then the Knowledge of algorithm is Very much important for you. The knowledge of Algorithm teach you How to Think to solve a Problem? Algorithm is the concepts which differentiate one average software engineer and one better software engineer. In our daily life in the industry we used different kinds of algorithm to make the system faster, better and efficient. But the problem is 90% of the freshers and graduates don’t have the basic knowledge of algorithm. That is the reason we make this Design and Analysis of algorithm Masterclass. What you are Going to Learn? Asymptotic Notations, Recursion, Divide and Conquer, Dynamic Programming, Dijkstra's, Bellman Ford, Floyd Warshall Algorithm, Kruskal's Algorithm, Knapsack Problem, String Matching with Finite Automaton, Heap sort, Huffman Codes, n-Queens Algorithm, Rat in Maze, 0/1 Knapsack Problem, 15 Puzzle Problem, NP Completeness, Approximation Algorithms

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science, and in particular, we recommend that you have taken the first course in this specialization (which also requires some previous experience with Java). In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables. You will explain how these data structures make programs more efficient and flexible. You will apply asymptotic Big-O analysis to describe the performance of algorithms and evaluate which strategy to use for efficient data retrieval, addition of new data, deletion of elements, and/or memory usage. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. This is an intermediate Java course, and we will build on your prior knowledge. This course is designed around the same video series as in our first course in this specialization, including explanations of core content, learner videos, student and engineer testimonials, and support videos -- to better allow you to choose your own path through the course!

What you’ll achieve: In this project-centered course* you will build a modern computer system, from the ground up. We’ll divide this fascinating journey into six hands-on projects that will take you from constructing elementary logic gates all the way through creating a fully functioning general purpose computer. In the process, you will learn - in the most direct and constructive way - how computers work, and how they are designed. What you’ll need: This is a self-contained course: all the knowledge necessary to succeed in the course and build the computer system will be given as part of the learning experience. Therefore, we assume no previous computer science or engineering knowledge, and all learners are welcome aboard. You will need no physical materials, since you will build the computer on your own PC, using a software-based hardware simulator, just like real computers are designed by computer engineers in the field. The hardware simulator, as well as other software tools, will be supplied freely after you enroll in the course. Course format: The course consists of six modules, each comprising a series of video lectures, and a project. You will need about 2-3 hours to watch each module's lectures, and about 5-10 hours to complete each one of the six projects. The course can be completed in six weeks, but you are welcome to take it at your own pace. You can watch a TED talk about this course by Googling "nand2tetris TED talk". *About Project-Centered Courses: Project-centered courses are designed to help you complete a personally meaningful real-world project, with your instructor and a community of learners with similar goals providing guidance and suggestions along the way. By actively applying new concepts as you learn, you’ll master the course content more efficiently; you’ll also get a head start on using the skills you gain to make positive changes in your life and career. When you complete the course, you’ll have a finished project that you’ll be proud to use and share.

Updated in November 2018 with brand new section on Dynamic Programming! This course crams months of computer science and interview prep material into 20 hours of video. The content is based directly on last semester of my in-person coding bootcamps , where my students go on to land 6-figure developer jobs . I cover the exact same computer science content that has helped my students ace interviews at huge companies like Google, Tesla, Amazon , and Facebook . Nothing is watered down for an online audience; this is the real deal :)   We start with the basics and then eventually cover “advanced topics” that similar courses shy away from like Heaps, Graphs , and Dijkstra’s Shortest Path Algorithm . I start by teaching you how to analyze your code’s time and space complexity using Big O notation .  We cover the ins and outs of Recursion .  We learn a 5-step approach to solving any difficult coding problem. We cover common programming patterns. We implement popular searching algorithms . We write 6 different sorting algorithms : Bubble, Selection, Insertion, Quick, Merge, and Radix Sort.   Then, we switch gears and implement our own data structures from scratch, including linked lists, trees, heaps, hash tables , and graphs .  We learn to traverse trees and graphs, and cover Dijkstra's Shortest Path Algorithm .  The course also includes an entire section devoted to Dynamic Programming . Here's why this course is worth your time: It's interactive -  I give you a chance to try every problem before I show you my solution. Every single problem has a complete solution walkthrough video as well as accompanying solution file. I cover helpful "tips and tricks" to solve common problems, but we also focus on building an approach to ANY problem. It's full of animations and beautiful diagrams! Are you looking to level-up your developer skills? Sign up today!

"Excellent! Thank you for all your hard work." - Mammoth Interactive student Inderpal "Great! Well explained and the instructor provides clear examples" - Mark T. Dive into a world of data science and analysis with a wide range of examples including the CIFAR 100 image dataset, Xcode development for Apple, Swift coding, CoreML, image recognition, and structuring data with pandas. This Mammoth Interactive course was funded by a #1 project on Kickstarter Learn Android Studio, Java, app development, Pycharm, Python coding, Tensforflow and more with Mammoth Interactive. Build advanced projects using machine learning including advanced the MNIST database with neuron functions. Build a text summarizer and learn object localization, object recognition and Tensorboard. Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training then it has “learned”. Learn How Models Work Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely. There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating Data Gathering and Formatting You will learn to gather plenty of data for the model to learn from. All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets. Model Building You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve. Training, Testing and Evaluating The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network. In supervised learning, we show the model what the correct outputs are for a given set of inputs and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer. Enroll Now to Learn with Mammoth Interactive

Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact. In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.

Programming is an increasingly important skill, whether you aspire to a career in software development, or in other fields. This course is the first in the specialization Introduction to Programming in C, but its lessons extend to any language you might want to learn. This is because programming is fundamentally about figuring out how to solve a class of problems and writing the algorithm, a clear set of steps to solve any problem in its class. This course will introduce you to a powerful problem-solving process—the Seven Steps—which you can use to solve any programming problem. In this course, you will learn how to develop an algorithm, then progress to reading code and understanding how programming concepts relate to algorithms.

Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main `consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.

This course is for all those people who want to learn data structure and Algorithm from absolute basic to Intermediate level. We don't expect you to have any prior knowledge on Data Structure or Algorithm, but a basic prior knowledge of any Programming Language(preferably C++) will be helpful. Thanks for stopping by !