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93 Algorithm courses in Liverpool delivered Live Online
Social Media Marketing Update - July 2023
By Avocado Social
Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!
Cisco Implementing and Operating Cisco Security Core Technologies v1.0 (SCOR)
By Nexus Human
Duration 5 Days 30 CPD hours This course is intended for Security engineer Network engineer Network designer Network administrator Systems engineer Consulting systems engineer Technical solutions architect Network manager Cisco integrators and partners Overview After taking this course, you should be able to: Describe information security concepts and strategies within the network Describe common TCP/IP, network application, and endpoint attacks Describe how various network security technologies work together to guard against attacks Implement access control on Cisco ASA appliance and Cisco Firepower Next-Generation Firewall Describe and implement basic email content security features and functions provided by Cisco Email Security Appliance Describe and implement web content security features and functions provided by Cisco Web Security Appliance Describe Cisco Umbrella security capabilities, deployment models, policy management, and Investigate console Introduce VPNs and describe cryptography solutions and algorithms Describe Cisco secure site-to-site connectivity solutions and explain how to deploy Cisco Internetwork Operating System (Cisco IOS) Virtual Tunnel Interface (VTI)-based point-to-point IPsec VPNs, and point-to-point IPsec VPN on the Cisco ASA and Cisco Firepower Next-Generation Firewall (NGFW) Describe and deploy Cisco secure remote access connectivity solutions and describe how to configure 802.1X and Extensible Authentication Protocol (EAP) authentication Provide basic understanding of endpoint security and describe Advanced Malware Protection (AMP) for Endpoints architecture and basic features Examine various defenses on Cisco devices that protect the control and management plane Configure and verify Cisco IOS software Layer 2 and Layer 3 data plane controls Describe Cisco Stealthwatch Enterprise and Stealthwatch Cloud solutions Describe basics of cloud computing and common cloud attacks and how to secure cloud environment The Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 course helps you prepare for the Cisco© CCNP© Security and CCIE© Security certifications and for senior-level security roles. In this course, you will master the skills and technologies you need to implement core Cisco security solutions to provide advanced threat protection against cybersecurity attacks. You will learn security for networks, cloud and content, endpoint protection, secure network access, visibility, and enforcements. You will get extensive hands-on experience deploying Cisco Firepower© Next-Generation Firewall and Cisco Adaptive Security Appliance (ASA) Firewall; configuring access control policies, mail policies, and 802.1X Authentication; and more. You will get introductory practice on Cisco Stealthwatch© Enterprise and Cisco Stealthwatch Cloud threat detection features. This course, including the self-paced material, helps prepare you to take the exam, Implementing and Operating Cisco Security Core Technologies (350-701 SCOR), which leads to the new CCNP Security, CCIE Security, and the Cisco Certified Specialist - Security Core certifications. Describing Information Security Concepts* Information Security Overview Assets, Vulnerabilities, and Countermeasures Managing Risk Vulnerability Assessment Understanding Common Vulnerability Scoring System (CVSS) Describing Common TCP/IP Attacks* Legacy TCP/IP Vulnerabilities IP Vulnerabilities Internet Control Message Protocol (ICMP) Vulnerabilities TCP Vulnerabilities User Datagram Protocol (UDP) Vulnerabilities Attack Surface and Attack Vectors Reconnaissance Attacks Access Attacks Man-in-the-Middle Attacks Denial of Service and Distributed Denial of Service Attacks Reflection and Amplification Attacks Spoofing Attacks Dynamic Host Configuration Protocol (DHCP) Attacks Describing Common Network Application Attacks* Password Attacks Domain Name System (DNS)-Based Attacks DNS Tunneling Web-Based Attacks HTTP 302 Cushioning Command Injections SQL Injections Cross-Site Scripting and Request Forgery Email-Based Attacks Describing Common Endpoint Attacks* Buffer Overflow Malware Reconnaissance Attack Gaining Access and Control Gaining Access via Social Engineering Gaining Access via Web-Based Attacks Exploit Kits and Rootkits Privilege Escalation Post-Exploitation Phase Angler Exploit Kit Describing Network Security Technologies Defense-in-Depth Strategy Defending Across the Attack Continuum Network Segmentation and Virtualization Overview Stateful Firewall Overview Security Intelligence Overview Threat Information Standardization Network-Based Malware Protection Overview Intrusion Prevention System (IPS) Overview Next Generation Firewall Overview Email Content Security Overview Web Content Security Overview Threat Analytic Systems Overview DNS Security Overview Authentication, Authorization, and Accounting Overview Identity and Access Management Overview Virtual Private Network Technology Overview Network Security Device Form Factors Overview Deploying Cisco ASA Firewall Cisco ASA Deployment Types Cisco ASA Interface Security Levels Cisco ASA Objects and Object Groups Network Address Translation Cisco ASA Interface Access Control Lists (ACLs) Cisco ASA Global ACLs Cisco ASA Advanced Access Policies Cisco ASA High Availability Overview Deploying Cisco Firepower Next-Generation Firewall Cisco Firepower NGFW Deployments Cisco Firepower NGFW Packet Processing and Policies Cisco Firepower NGFW Objects Cisco Firepower NGFW Network Address Translation (NAT) Cisco Firepower NGFW Prefilter Policies Cisco Firepower NGFW Access Control Policies Cisco Firepower NGFW Security Intelligence Cisco Firepower NGFW Discovery Policies Cisco Firepower NGFW IPS Policies Cisco Firepower NGFW Malware and File Policies Deploying Email Content Security Cisco Email Content Security Overview Simple Mail Transfer Protocol (SMTP) Overview Email Pipeline Overview Public and Private Listeners Host Access Table Overview Recipient Access Table Overview Mail Policies Overview Protection Against Spam and Graymail Anti-virus and Anti-malware Protection Outbreak Filters Content Filters Data Loss Prevention Email Encryption Deploying Web Content Security Cisco Web Security Appliance (WSA) Overview Deployment Options Network Users Authentication Secure HTTP (HTTPS) Traffic Decryption Access Policies and Identification Profiles Acceptable Use Controls Settings Anti-Malware Protection Deploying Cisco Umbrella* Cisco Umbrella Architecture Deploying Cisco Umbrella Cisco Umbrella Roaming Client Managing Cisco Umbrella Cisco Umbrella Investigate Overview and Concepts Explaining VPN Technologies and Cryptography VPN Definition VPN Types Secure Communication and Cryptographic Services Keys in Cryptography Public Key Infrastructure Introducing Cisco Secure Site-to-Site VPN Solutions Site-to-Site VPN Topologies IPsec VPN Overview IPsec Static Crypto Maps IPsec Static Virtual Tunnel Interface Dynamic Multipoint VPN Cisco IOS FlexVPN Deploying Cisco IOS VTI-Based Point-to-Point IPsec VPNs Cisco IOS VTIs Static VTI Point-to-Point IPsec Internet Key Exchange (IKE) v2 VPN Configuration Deploying Point-to-Point IPsec VPNs on the Cisco ASA and Cisco Firepower NGFW Point-to-Point VPNs on the Cisco ASA and Cisco Firepower NGFW Cisco ASA Point-to-Point VPN Configuration Cisco Firepower NGFW Point-to-Point VPN Configuration Introducing Cisco Secure Remote Access VPN Solutions Remote Access VPN Components Remote Access VPN Technologies Secure Sockets Layer (SSL) Overview Deploying Remote Access SSL VPNs on the Cisco ASA and Cisco Firepower NGFW Remote Access Configuration Concepts Connection Profiles Group Policies Cisco ASA Remote Access VPN Configuration Cisco Firepower NGFW Remote Access VPN Configuration Explaining Cisco Secure Network Access Solutions Cisco Secure Network Access Cisco Secure Network Access Components AAA Role in Cisco Secure Network Access Solution Cisco Identity Services Engine Cisco TrustSec Describing 802.1X Authentication 802.1X and Extensible Authentication Protocol (EAP) EAP Methods Role of Remote Authentication Dial-in User Service (RADIUS) in 802.1X Communications RADIUS Change of Authorization Configuring 802.1X Authentication Cisco Catalyst© Switch 802.1X Configuration Cisco Wireless LAN Controller (WLC) 802.1X Configuration Cisco Identity Services Engine (ISE) 802.1X Configuration Supplicant 802.1x Configuration Cisco Central Web Authentication Describing Endpoint Security Technologies* Host-Based Personal Firewall Host-Based Anti-Virus Host-Based Intrusion Prevention System Application Whitelists and Blacklists Host-Based Malware Protection Sandboxing Overview File Integrity Checking Deploying Cisco Advanced Malware Protection (AMP) for Endpoints* Cisco AMP for Endpoints Architecture Cisco AMP for Endpoints Engines Retrospective Security with Cisco AMP Cisco AMP Device and File Trajectory Managing Cisco AMP for Endpoints Introducing Network Infrastructure Protection* Identifying Network Device Planes Control Plane Security Controls Management Plane Security Controls Network Telemetry Layer 2 Data Plane Security Controls Layer 3 Data Plane Security Controls Deploying Control Plane Security Controls* Infrastructure ACLs Control Plane Policing Control Plane Protection Routing Protocol Security Deploying Layer 2 Data Plane Security Controls* Overview of Layer 2 Data Plane Security Controls Virtual LAN (VLAN)-Based Attacks Mitigation Sp
VMware NSX Advanced Load Balancer: Web Application Firewall Security [V22.x]
By Nexus Human
Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture, components, and main functions Explain the key features and benefits of NSX Advanced Load Balancer Explain and configure local load-balancing constructs such as virtual services, pools, health monitors, and related components Recognize web application breaches and threats Recognize multiple attack vectors such as web scraping, Layer 7 Denial of Service, brute force, and code injections Explain the components of NSX Advanced Load Balancer WAF that build a security pipeline to protect a web application from being attacked Describe how to configure the NSX Advanced Load Balancer WAF components Describe an NSX Advanced Load Balancer WAF operational task such as setting up an application with WAF, tuning the WAF Policy, and working with logs and analytics Explain the NSX Advanced Load Balancer WAF best practices for on-boarding a web application; configuring WAF settings for effective application security Explain how to size the NSX Advanced Load Balancer WAF data plane Explain the WAF Application learning feature, configuration of Application learning, Virtual Patching concepts, common caveats, and troubleshooting while deploying in any environment Recognize NSX Advanced Load Balancer Cloud Services that include threat Intelligence services Describe the Threat Intelligence service provided by NSX Advanced Load Balancer WAF and how the NSX Advanced Load Balancer WAF Threat Intelligence service receives live security threat feed for multiple attack vectors from Cloud Services (formerly Avi Pulse) Describe the NSX Advanced Load Balancer DataScript capabilities for detecting and defending against advance and zero-day attacks. Discuss the relevant NSX Advanced Load Balancer WAF logs and perform basic troubleshooting of applications that are protected by NSX Advanced Load Balancer WAF Explain the NSX Advanced Load Balancer WAF capability to protect Personally Identifiable Information (PII) This three-day course provides comprehensive training to install, configure, and manage a VMware NSX Advanced Load Balancer Web Application Firewall (WAF) solution. This course covers key NSX Advanced Load Balancer WAF features and functionality offered in the NSX Advanced Load Balancer 22.1.3 release for web security and application attack protection. Features include security pipeline, application learning, policy tuning, false positive mitigation, virtual patching, threat intelligence, troubleshooting, logs, analytics, and solution monitoring. Hands-on labs provide access to an NSX Advanced Load Balancer environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Illustrate NSX Advanced Load Balancer Explain NSX Advanced Load Balancer architecture and components Describe control plane clustering and high availability Describe data plane high availability mode Understand the common terminologies used with NSX Advanced Load Balancer Explain the NSX Advanced Load Balancer service elements Explain virtual service components and how to configure a virtual service Explain application profiles and network profiles Explain the pool configuration options and how to configure a pool Explain the available load-balancing algorithms Explain and configure SSL profiles and certificates Explain cloud connectors and cloud connector integration modes Explain multiple health monitor types Understand client logs Introduction to Application Security Understand web application security breaches and the implication of breaches Explain common terminologies related to Web Application Security Understand the different teams involved to secure applications Attacking Web Applications Understand the various web application security testing methodologies Understand the OWASP Top 10 vulnerabilities Understand the tools to generate a web application attack Describe a few types of web application attacks Types of Transport Understand different web traffic transport modes Describe web traffic and API traffic NSX Advanced Load Balancer WAF Components Understand the core design principles of NSX Advanced Load Balancer WAF Describe the NSX Advanced Load Balancer WAF components that build the WAF security pipeline Understand the NSX Advanced Load Balancer WAF configuration objects NSX Advanced Load Balancer WAF Operations Examine how to set up an application with WAF Describe considerations for the WAF policy Work with WAF logs and analytics Describe WAF policy tuning Describe the options available to remediate false positive mitigation NSX Advanced Load Balancer WAF Best Practices Describe technical and application considerations for onboarding an application front ended by WAF Describe best practices to remediate false positive mitigation. Describe how to manage a response from a back-end application server and client upload to the application server Describe the consideration for setting the rigidity of a WAF signature rule set Describe the options available to identify client traffic NSX Advanced Load Balancer WAF Sizing Understand how to do WAF data plane sizing in Greenfield and Brownfield deployments NSX Advanced Load Balancer WAF Custom Rules Understand WAF custom rules Describe the need and recommendation for custom rules Describe ModSecurity rules Understand the ModSecurity rule structure and explain how to construct the rule Analyze a sample custom rule for the use-case scenario for in-depth understanding of a custom rule NSX Advanced Load Balancer WAF Application Learning Understand the significance of Application Learning Explain the Positive Security Model architecture Describe the WAF multifaceted Application Learning technique to build an application model for creating positive security rules Describe how to view the data that is learned by the Application learning module Describe the WAF Virtual Patching technique to construct a WAF policy from Dynamic Application Security Testing (DAST) scanner results Understand the conditions for sharing WAF Learning Data and PSM Group in WAF Policy. Malware Protection Through ICAP in NSX Advanced Load Balancer Understand Malicious File Upload Protection and ICAP workflow Describe ICAP configuration and log analytics NSX Advanced Load Balancer IP Reputation Understand IP Reputation concepts and their integration with NSX Advanced Load Balancer Describe IP Reputation configuration, log analytics, and troubleshooting DataScript for Application Security Describe DataScript events and reference Describe application security using DataScript Explain how to troubleshoot DataScript issues Rate Limiting and DOS Describe and configure the NSX Advanced Load Balancer rate limiter technique Describe protection from denial of service (DoS) attacks and distributed DoS (DDoS) attacks in NSX Advanced Load Balancer Explain the Service Engine general advice and guidance for DDOS Bot Management Understand Bots Describe the Bot Management mechanism in NSX Advanced Load Balancer Describe how to configure NSX Advanced Load Balancer Bot Management Managing Personally Identifiable Information in NSX Advanced Load Balancer Understand Personally Identifiable Information (PII) Understand the scope of managing PII in NSX Advanced Load Balancer Describe how to configure the hidden PII in NSX Advanced Load Balancer logs using profiles and WAF rules. Threat Intelligence Introduce the Threat Intelligence service Describe the Threat Intelligence live security threat feed for multiple attack vectors Describe how to configure Threat Intelligence in NSX Advanced Load Balancer Application Programming Interface Security Define Application Programming Interface (API) Security Understand API authentication and authorization using virtual service authentication mechanisms used for a virtual service such as LDAP, SAML, JSON Web Token, and OAUTH Understand API Rate Limiting in NSX Advanced Load Balancer Understand the NSX Advanced Load Balancer WAF Protection for API Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware NSX Advanced Load Balancer: Web Application Firewall Security [V22.x] 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 VMware NSX Advanced Load Balancer: Web Application Firewall Security [V22.x] 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.
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Artificial intelligence & Javascript 2D Game Development - MinMax algorithm - "Computer vs You" Tic Tac Toe AI game
Machine Learning and Data Science with Python: A Complete Beginners Guide
By Packt
This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.
Quantum Algorithms for Computational Finance
By Qureca
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” 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 QuantFiQuantFi 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.
NTP and PTP training course description This course looks at timing and synchronization as provided by NTP and PTP. Hands on sessions primarily involve using Wireshark to analyse the protocols. What will you learn Recognise the importance of timing and synchronisation. Explain how NTP works. Explain how PTP works. NTP and PTP training course details Who will benefit: Anyone using NTP or PTP but particularly relevant for those in the broadcast industry. Prerequisites: TCP/IP foundation for engineers Duration 1 day NTP and PTP training course contents Introduction Clock drift. Timing and synchronization. Importance in computing. Importance in broadcasting. NTP NTP versions, v0 to v4. Architecture. The intersection algorithm. Accuracy. Clock strata, Stratum 0, 1, 2 and 3. Protocol modes. Hands on NTP configuration More NTP NTP packet header. Timestamps. Variables in the header. Clock synchronization algorithm. SNTP. The Windows Time service. Hands on Wireshark and NTP analysis. PTP PTP v2, IEEE 1588. Architecture. Accuracy. Synchronization with PTP. Offset and delay. Hands on Analysing PTP packet flows. More PTP Ordinary clocks, boundary clocks, masters and grandmasters. PTP specific switch types. Hardware time stamping. SMPTE ST2059-2. PTP packet header. PTP domains. Best master clock algorithm. Hands on More Wireshark and PTP.
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
Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.
Python 3: Project-based Python, Algorithms, Data Structures
By Packt
Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects
The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, 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 Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00
