273 Algorithms courses

Duration 5 Days 30 CPD hours This course is intended for This course is designed for people who are seeking to launch a career in cybersecurity. Overview Assess the security posture of an enterprise environment and recommend and implement appropriate security solutions; Monitor and secure hybrid environments, including cloud, mobile, and IoT; Operate with an awareness of applicable laws and policies, including principles of governance, risk, and compliance; Identify, analyze, and respond to security events and incidents. CompTIA Security+ is a global certification that validates the baseline skills necessary to perform core security functions and is the first security certification a candidate should earn. CompTIA Security+ establishes the core knowledge required of any cybersecurity role and provides a springboard to intermediate-level cybersecurity jobs. LESSON 1: SUMMARIZE FUNDAMENTAL SECURITY CONCEPTS * Security Concepts * Security Controls LESSON 2: COMPARE THREAT TYPES * Threat Actors * Attack Surfaces * Social Engineering LESSON 3: EXPLAIN CRYPTOGRAPHIC SOLUTIONS * Cryptographic Algorithms * Public Key Infrastructure * Cryptographic Solutions LESSON 4: IMPLEMENT IDENTITY AND ACCESS MANAGEMENT * Authentication * Authorization * Identity Management LESSON 5: SECURE ENTERPRISE NETWORK ARCHITECTURE * Enterprise Network Architecture * Network Security Appliances * Secure Communications LESSON 6: SECURE CLOUD NETWORK ARCHITECTURE * Cloud Infrastructure * Embedded Systems and Zero Trust Architecture LESSON 7: EXPLAIN RESILIENCY AND SITE SECURITY CONCEPTS * Asset Management * Redundancy Strategies * Physical Security LESSON 8: EXPLAIN VULNERABILITY MANAGEMENT * Device and OS Vulnerabilities * Application and Cloud Vulnerabilities * Vulnerability Identification Methods * Vulnerability Analysis and Remediation LESSON 9: EVALUATE NETWORK SECURITY CAPABILITIES * Network Security Baselines * Network Security Capability Enhancement LESSON 10: ASSESS ENDPOINT SECURITY CAPABILITIES * Implement Endpoint Security * Mobile Device Hardening LESSON 11: ENHANCE APPLICATION SECURITY CAPABILITIES * Application Protocol Security Baselines * Cloud and Web Application Security Concepts LESSON 12: EXPLAIN INCIDENT RESPONSE AND MONITORING CONCEPTS * Incident Response * Digital Forensics * Data Sources * Alerting and Monitoring Tools LESSON 13: ANALYZE INDICATORS OF MALICIOUS ACTIVITY * Malware Attack Indicators * Physical and Network Attack Indicators * Application Attack Indicators LESSON 14: SUMMARIZE SECURITY GOVERNANCE CONCEPTS * Policies, Standards, and Procedures * Change Management * Automation and Orchestration LESSON 15: EXPLAIN RISK MANAGEMENT PROCESSES * Risk Management Processes and Concepts * Vendor Management Concepts * Audits and Assessments LESSON 16: SUMMARIZE DATA PROTECTION AND COMPLIANCE CONCEPTS * Data Classification and Compliance * Personnel Policies ADDITIONAL COURSE DETAILS: Nexus Humans CompTIA Security Plus Certification (Exam SY0-601) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the CompTIA Security Plus Certification (Exam SY0-601) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Make your code and programs faster and more efficient by using algorithms

Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects

Demystify the power of graphs and unlock their potential with our Graph Theory Algorithms Course. Master fundamental algorithms like Depth-First Search and Breadth-First Search, explore pathfinding techniques, and delve into advanced graph concepts. Enrol now to sharpen your skills and become proficient in graph theory.

This course takes you through all the important topics of data structure and algorithms from scratch. You will learn how to solve real-world problems with linked lists, stacks, queues, sorting algorithms, and a lot more using Python.

Register on the Graph Theory Algorithms today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The Graph Theory Algorithms is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! WHAT YOU GET WITH THE GRAPH THEORY ALGORITHMS * Receive a e-certificate upon successful completion of the course * Get taught by experienced, professional instructors * Study at a time and pace that suits your learning style * Get instant feedback on assessments  * 24/7 help and advice via email or live chat * Get full tutor support on weekdays (Monday to Friday) COURSE DESIGN The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of * Video lessons * Online study materials CERTIFICATION After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. WHO IS THIS COURSE FOR: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. REQUIREMENTS: The online training is open to all students and has no formal entry requirements. To study the Graph Theory Algorithms, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. COURSE CONTENT Module 01: Introduction Introduction 00:14:00 Module 02: Common Problem Common Problem 00:10:00 Module 03: Depth First Search Depth First Search 00:11:00 Module 04: Breadth First Search Breadth First Search 00:08:00 Module 05: Breadth First Search Shortest Path on a Grid Breadth First Search Shortest Path on a Grid 00:17:00 Module 06: Trees Storage and Representation of Trees 00:10:00 Beginner Tree Algorithms 00:10:00 Rooting Tree 00:05:00 Center(s) of a Tree 00:06:00 Isomorphisms in Trees 00:11:00 Isomorphisms in Trees Source Code 00:10:00 Lowest Common Ancestor 00:17:00 Module 07: Topological Sort Topological Sort 00:14:00 Shortest and Longest Paths on DAGs 00:10:00 Khan's Algorithm 00:13:00 Module 08: Dijkstra Dijkstra's Shortest Path Algorithm Source Code 00:09:00 Dijkstra's Shortest Path Algorithm 00:25:00 Module 09: Bellman-Ford Algorithm Bellman-Ford Algorithm 00:15:00 Module 10: Floyd-Warshall Algorithm Floyd-Warshall Algorithm 00:16:00 Floyd-Warshall Algorithm Source Code 00:09:00 Module 11: Bridge and Algorithm Points Algorithm to Find Bridges and Articulation Points 00:20:00 Algorithm to Find Bridges and Articulation Points Source Code 00:09:00 Module 12: Tarjan Algorithm Tarjan's Algorithm for Finding Strongly Connected Components 00:17:00 Tarjan's Algorithm for Finding Strongly Connected Components Source Code 00:07:00 Module 13: Travelling Salesman Problem (TSP) Travelling Salesman Problem (TSP) with Dynamic Programming 00:21:00 Travelling Salesman Problem (TSP) with Dynamic Programming Source Code 00:14:00 Module 14: Eulerian Paths and Circuits Existence of Eulerian Paths and Circuit 00:10:00 Finding Eulerian Paths and Circuits 00:16:00 Eulerian Paths Source Code 00:08:00 Module 15: Prim's Minimum Spanning Tree Algorithm Prim's Minimum Spanning Tree Algorithm (Lazy Version) 00:15:00 Prim's Minimum Spanning Tree Algorithm ( Eager Version) 00:15:00 Prim's Minimum Spanning Tree Algorithm Source Code ( Eager Version) 00:09:00 Module 16: Network Flow Max Flow Ford-Fulkerson Method 00:13:00 Max Flow Ford-Fulkerson Method Source Code 00:17:00 Network Flow: Unweighted Bipartite Graph Matching 00:11:00 Network Flow: Mice and Owls 00:08:00 Network Flow: Elementary Math 00:11:00 Network Flow: Edmond-Karp Algorithm Source Code 00:06:00 Network Flow: Edmond-Karp Algorithm Source Code 00:10:00 Network Flow: Capacity Scaling 00:10:00 Network Flow: Capacity Scaling Source Code 00:06:00 Network Flow: Dinic's Algorithm 00:12:00 Network Flow: Dinic's Algorithm Source Code 00:09:00 FREQUENTLY ASKED QUESTIONS Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

OVERVIEW This comprehensive course on Computer Science: Graph Theory Algorithms will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Computer Science: Graph Theory Algorithms comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. HOW WILL I GET MY CERTIFICATE? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. WHO IS THIS COURSE FOR? There is no experience or previous qualifications required for enrolment on this Computer Science: Graph Theory Algorithms. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Computer Science: Graph Theory Algorithms is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. CAREER PATH Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. COURSE CURRICULUM 17 sections • 44 lectures • 08:37:00 total length •Promo: 00:03:00 •Introduction: 00:14:00 •Common Problem: 00:10:00 •Depth First Search: 00:11:00 •Breadth First Search: 00:08:00 •Breadth First Search Shortest Path on a Grid: 00:17:00 •Storage and Representation of Trees: 00:10:00 •Beginner Tree Algorithms: 00:10:00 •Rooting Tree: 00:05:00 •Center(s) of a Tree: 00:06:00 •Isomorphisms in Trees: 00:11:00 •Isomorphisms in Trees Source Code: 00:10:00 •Lowest Common Ancestor: 00:17:00 •Topological Sort: 00:14:00 •Shortest and Longest Paths on DAGs: 00:10:00 •Khan's Algorithm: 00:13:00 •Dijkstra's Shortest Path Algorithm: 00:25:00 •Dijkstra's Shortest Path Algorithm Source Code: 00:09:00 •Bellman-Ford Algorithm: 00:15:00 •Floyd-Warshall Algorithm: 00:16:00 •Floyd-Warshall Algorithm Source Code: 00:09:00 •Algorithm to Find Bridges and Articulation Points: 00:20:00 •Algorithm to Find Bridges and Articulation Points Source Code: 00:09:00 •Tarjan's Algorithm for Finding Strongly Connected Components: 00:17:00 •Tarjan's Algorithm for Finding Strongly Connected Components Source Code: 00:07:00 •Travelling Salesman Problem (TSP) with Dynamic Programming: 00:21:00 •Travelling Salesman Problem (TSP) with Dynamic Programming Source Code: 00:14:00 •Existence of Eulerian Paths and Circuit: 00:10:00 •Finding Eulerian Paths and Circuits: 00:16:00 •Eulerian Paths Source Code: 00:08:00 •Prim's Minimum Spanning Tree Algorithm (Lazy Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm ( Eager Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm Source Code ( Eager Version): 00:09:00 •Max Flow Ford-Fulkerson Method: 00:13:00 •Max Flow Ford-Fulkerson Method Source Code: 00:17:00 •Network Flow: Unweighted Bipartite Graph Matching: 00:11:00 •Network Flow: Mice and Owls: 00:08:00 •Network Flow: Elementary Math: 00:11:00 •Network Flow: Edmond-Karp Algorithm: 00:06:00 •Network Flow: Edmond-Karp Algorithm Source Code: 00:10:00 •Network Flow: Capacity Scaling: 00:10:00 •Network Flow: Capacity Scaling Source Code: 00:06:00 •Network Flow: Dinic's Algorithm: 00:12:00 •Network Flow: Dinic's Algorithm Source Code: 00:09:00

This video course takes you through the basic and advanced JavaScript methods, enabling you to understand and implement them in a correct way. The course is filled with tips and tricks that will help you tackle tough interview questions to get a job.

ABOUT THE COURSE “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Quantum Algorithms for Computational Finance” is an advanced course in the emerging field of quantum computing for finance. This technical course will develop an understanding in quantum algorithms for its implementation on quantum computers. Through this course, you will learn the basics of various quantum algorithms including: * Grover’s and Rudolf’s algorithm, * Quantum amplitude Estimation (QAE) algorithm envisioned as a quadratic speed-up over Classical Monte-Carlo simulations, * Combinatorial optimization algorithms namely Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE), and * Quantum-inspired optimization algorithms – Simulated Coherent Ising Machine (Sim-CIM), and Simulated Bifurcation Algorithm (SBA). This course is meant for all those learners who want to explore the long-term employability of quantum computing in finance, assuming that you are familiar with the concepts of quantitative and computational finance. In addition, the course contains several Python based programming exercises for learners to practice the algorithms explained throughout the course. This course is the second part of the specialised educational series: “Quantum Computing for Finance”. WHAT SKILLS YOU WILL LEARN * Ability to perform quantum arithmetic operations and simulations. * An understanding of the Quantum Amplitude Estimation algorithm and its variants. * The computational and modelling techniques for option pricing and portfolio optimization on a quantum computer. * The skills for a career in quantum finance including Quantum Algorithmic Research, Quantitative Asset Management and Trading, financial engineering, and risk management, using quantum computing technology. COURSE PREREQUISITES All potential learners must have prior knowledge or familiarity with basic quantum algorithms/basic quantum programming. Before enrolling this course, we recommend all learners to complete the first course “Introduction to Quantitative and Computational Finance [https://platform.qureca.com/courses/introduction-to-quantitative-and-computational-finance/]” of the series “Quantum Computing for Finance”, if they have no previous experience with the concepts of quantitative and computational finance. DURATION The estimated duration to complete this course is approximately 6 weeks (~4hrs/week). COURSE ASSESSMENT To complete the course and earn the certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more on each of them. INSTRUCTORS [https://platform.qureca.com/courses/quantum-algorithms-for-computational-finance/#course-section__instructors] https://platform.qureca.com/author/kevincallaghan/ QuantFi [https://platform.qureca.com/author/kevincallaghan/]QuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.