264 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. DESCRIPTION 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.

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.

There are quite a few issues with manual memory management. Therefore, to avoid memory leaks and optimally use your memory, automatic memory management is essential. In this course, we'll learn about garbage collection as a form of automatic memory management.

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.