202 Algorithms courses delivered On Demand

Building a parser is one of the early steps of designing a compiler. And to build a parser, it is important to learn about the different parsing techniques and how they work. In this course, we are going to learn just that.

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

For those interested in the field of genetics and computational biology, who want to gain a deep understanding of the tools and algorithms used in bioinformatics, we have this Bioinformatics & Computational Genomics: Algorithms & Tools course. With the explosion of data in the field of genomics, it can be overwhelming to keep up with the latest tools and techniques in bioinformatics. Without a strong foundation in this field, it can be difficult to navigate the complex algorithms and tools required to make meaningful contributions. Our Bioinformatics & Computational Genomics course provides a comprehensive introduction to the most important algorithms and tools in the field, as well as the latest research in computational genomics. Learning Outcomes: * Gain a thorough understanding of the fundamental principles and concepts in bioinformatics and computational genomics. * Develop the ability to analyse and interpret genomic data using advanced algorithms and computational tools. * Explore the structure and function of proteins, and understand their significance in biological systems. * Acquire knowledge of the latest advancements in computational genomics and their application in various research areas. * Develop critical thinking skills to tackle complex problems in bioinformatics and propose innovative solutions. Unlock the world of Bioinformatics & Computational Genomics with our comprehensive course. Delve into the fascinating realm of genetic data analysis, algorithms, and tools, and discover how they shape our understanding of complex biological systems. Throughout this immersive learning experience, you will explore diverse topics, including computational genomics, protein structure and function, and applied bioinformatics. With a strong focus on theoretical concepts, this course equips you with the essential knowledge to navigate the vast landscape of bioinformatics research. Embark on a transformative journey as you unravel the mysteries hidden within the genomes. Our carefully designed curriculum covers the key principles, algorithms, and tools that drive breakthroughs in bioinformatics. By delving into the intricacies of genomic data analysis, you will gain valuable insights into the structure and function of proteins, enabling you to decipher the language of life at a molecular level. Immerse yourself in the world of computational genomics and embrace the power of algorithms in unlocking the secrets of genetic information. This course is tailored for individuals passionate about genomics and computational biology. Whether you're an aspiring researcher, a healthcare professional, or a biology enthusiast, this course will ignite your curiosity and expand your horizons. No prior knowledge of bioinformatics is required, as we provide a solid foundation to help you navigate this captivating field. Enrol in the course right now! Certification Upon completion of the Bioinformatics & Computational Genomics: Algorithms & Tools course, learners can obtain a certificate as proof of their achievement. You can receive a £4.99 PDF Certificate sent via email, a £9.99 Printed Hardcopy Certificate for delivery in the UK, or a £19.99 Printed Hardcopy Certificate for international delivery. Each option depends on individual preferences and locations. CPD 10 CPD hours / points Accredited by CPD Quality Standards WHO IS THIS COURSE FOR? The target audience for the course is: * Researchers looking to enhance their knowledge and expertise in bioinformatics and computational genomics. * Healthcare professionals interested in understanding the computational aspects of genomics and its applications. * Biology students seeking to explore the intersection of biology and computer science. * Enthusiasts eager to unravel the mysteries of genomics and gain a deeper understanding of the computational tools used in bioinformatics. CAREER PATH Some of the career paths related to this field are: * Bioinformatics Analyst: £30,000 - £50,000 per annum * Computational Genomics Researcher: £35,000 - £60,000 per annum * Data Scientist (Genomics) £40,000 - £70,000 per annum * Genomic Data Analyst: £35,000 - £55,000 per annum * Bioinformatics Software Developer: £40,000 - £65,000 per annum * Academic Researcher (Bioinformatics): £35,000 - £70,000 per annum

WHAT WILL I LEARN? * Choose a perfect automated trading strategy * Build a portfolio consisting of trading algorithms * Learn what criteria a trading algorithm should match * Learn when to exclude a strategy from portfolio * Learn advantages of using trading algorithms * Learn where to find automated trading strategies * Understand why it's better to trust an algorithm than a human * Learn what markets to trade using trading algorithms REQUIREMENTS * No programming skills required * Just an open mind * Basic Excel DESCRIPTION Updated in June'16! Learn how to make 95% of profit in just 14 months! Learn how to choose automated trading strategies and force them to earn money for you! In this course, I will show you several platforms where you can find a variety of trading robots. But, more importantly, I will give you a list of criteria to choose perfect strategies and teach you how to select them in practice. I will explain what to take into account when you have to choose between two similar strategies. You will also get a basic knowledge of trading algorithms, their advantages as compared to manual trading, which markets it's better to trade using automated trading strategies. I believe by the end of this course you will be able to build your own portfolio and make profits. Take this course now and learn from my 12+ years of experience. This course is for beginners as well as for advanced traders and algorithm developers! All you need is just your aspiration to learn! With this course you also get: * unlimited lifetime access at no extra costs * all future additional lectures, live trading examples * never any questions asked full 30-day money-back-in-full guarantee Do not hesitate to ask me any questions concerning this course or trading financial markets! Viktor WHO IS THE TARGET AUDIENCE? * Anyone interested in trading Financial Markets * Professional traders and algorithm developers * Anyone who wants to create a passive income on Financial Markets, but has no time to spare how to trade * Forex, stocks, options and especially futures traders * Those, who want to trade algorithms, but don't know where to start * And finally, anyone who has bad experience in trading Introduction What this course is about FREE 00:02:00 What do you need to start this course FREE 00:02:00 Live Results 00:03:00 Performance Update (November'15) 00:04:00 Performance Update (January'16) 00:05:00 Performance update (June'16) 00:06:00 Performance Update (October'16) 00:06:00 What instruments do you prefer to invest? What are Financial Markets? 00:06:00 What is algorithmic trading? 00:03:00 Risks from using trading algorithms 00:05:00 What markets to trade (Futures against Forex) 00:06:00 The external side of the robot What criteria to use to choose a strategy 00:09:00 How to create a portfolio 00:02:00 How to exclude a strategy from portfolio 00:03:00 Where to invest 00:05:00 iSystems in details 00:09:00 Learn from experience What strategies to use. Creating a portfolio (part 1) 00:08:00 What strategies to use. Creating a portfolio (part 2) 00:07:00

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

The Computer Science and Programming Diploma course covers the fundamental theories of Algorithm Analysis. If you want to explore the concepts and methods that make a good programmer, then the course is designed for you. Programming is all about how to solve a problem. Programming theory is not confined to a single language; rather it applies to all programming languages. By understanding the right programming theory, you will be able to analyse a problem and also able to find out the probable solution. The course teaches you these Programming theories covering Algorithm analysis, Binary Number System, Arrays and their Advantages, the process of analysing a problem, Nodes and their Importance, various sorting algorithms and their comparisons, and more. Upon completion, you will be able to understand the core theories of computer science. WHAT WILL I LEARN? * Understand the Fundamental Theories of Algorithm Analysis * Be able to Compare Various Algorithms * Understand When to use Different Data Structures and Algorithms * Understand the Fundamentals of Computer Science theory REQUIREMENTS * A Willingness to Learn New Topics! * No Prior Experience or Knowledge is Needed! Module: 01 Kurt Anderson - 1 Introduction FREE 00:01:00 Kurt Anderson - 2 Binary System FREE 00:11:00 Kurt Anderson - 3 Complexity Introduction 00:02:00 Kurt Anderson - 4 Math Refresher Logarithmic Functions 00:11:00 Kurt Anderson - 5 Math Refresher Factorial Functions.TS 007 00:03:00 Kurt Anderson - 6 Math Refresher Algebraic Expressions.TS 00:03:00 Kurt Anderson - 7 n-notation 00:19:00 Kurt Anderson - 8 Big O 00:13:00 Kurt Anderson - 9 Big O Real World Example 00:10:00 Module: 02 Kurt Anderson - 10 How is Data Stored 00:09:00 Kurt Anderson - 11 Fixed Arrays 00:20:00 Kurt Anderson - 12 Circular Arrays 00:08:00 Kurt Anderson - 13 Dynamic Arrays 00:16:00 Kurt Anderson - 14 Array Review 00:08:00 Kurt Anderson - 15 Array Real World Examples 00:06:00 Kurt Anderson - 16 Linked List 00:12:00 Kurt Anderson - 16 Nodes 00:04:00 Kurt Anderson - 17 Linked List Run Times 00:15:00 Kurt Anderson - 18 Doubly Linked Lists 00:08:00 Kurt Anderson - 19 Tail Pointer 00:05:00 Module: 03 Kurt Anderson - 20 Linked List Real World Examples 00:03:00 Kurt Anderson - 20 Stack Example 00:11:00 Kurt Anderson - 21 Linked List Review 00:04:00 Kurt Anderson - 22 Stacks 00:10:00 Kurt Anderson - 23 Queues 00:09:00 Kurt Anderson - 24 Queue Examples 00:10:00 Kurt Anderson - 25 Queue and Stack Run Times 00:06:00 Kurt Anderson - 26 Stack and Queues Real World Examples 00:07:00 Kurt Anderson - 27 Sorting Algorithm Introdcution 00:02:00 Kurt Anderson - 28 Bubble Sort 00:10:00 Kurt Anderson - 29 Selection Sort 00:10:00 Module: 04 Kurt Anderson - 30 Insertion Sort 00:09:00 Kurt Anderson - 31 Quick Sort 00:15:00 Kurt Anderson - 32 Quick Sort Run Times 00:10:00 Kurt Anderson - 33 Merge Sort 00:12:00 Kurt Anderson - 34 Merge Sort Run Times 00:08:00 Kurt Anderson - 35 Stable vs Nonstable 00:07:00 Kurt Anderson - 36 Sorting Algorithm Real World Examples 00:04:00 Kurt Anderson - 37 Basics of Trees 00:08:00 Kurt Anderson - 38 Binary Search Tree 00:09:00 Kurt Anderson - 39 BST Run Times 00:08:00 Module: 05 Kurt Anderson - 40 Tree Traversals 00:13:00 Kurt Anderson - 41 Tree Real World Examples 00:05:00 Kurt Anderson - 42 Heap Introduction 00:04:00 Kurt Anderson - 43 Heap Step by Step 00:12:00 Kurt Anderson - 44 Heap Real World Examples 00:07:00 Kurt Anderson - 45 Thank You 00:01:00

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Are you familiar with JavaScript and programming, perhaps considering a coding interview soon, but looking to train, sharpen, and master your JavaScript skills? Are you looking to improve your data structures and algorithms with an anthology of some of the most important practice examples and a journey that can smoothly get you there? Then this course is specifically designed for you!

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.