3805 Engineer courses in Westergate delivered Online

SNMP training course description A hands-on generic look at the technical operation of SNMP. The course starts with an overview of all the components, which make up SNMP. Hands on starts early with configuration of a managed network. The major versions of SNMP are then put into perspective followed by a look at the SNMP protocol. MIBs are then studied both from the perspective of reading MIBs and writing MIBs. The course finishes with a look at the security implications of SNMP. What will you learn Describe the SNMP architecture. Analyse SNMP packets. Recognise the MIB structure. Describe the SMI. Recognise the strengths and weaknesses of SNMPv2 and SNMPv3. SNMP training course details Who will benefit: Network administrators. Network operators. Programmers writing MIBs and agents. Prerequisites: TCP/IP Foundation for engineers Hands on experience of an SNMP management station would also be beneficial. Duration 3 days SNMP training course contents Network management What is network management? Benefits, issues. What is SNMP? SNMP architecture, SNMP MIBs, SMI, the SNMP protocol, polling security, alternatives to SNMP: CMIP, web based management. Configuring SNMP Auto discovery for management stations, NMS configuration, agent configuration, traps. Hands on Configuring agents and an NMS. SNMP background SNMP history, RFCs, standards, SNMP protocol versions, SNMPv1, SNMPv2, SNMPv3, SNMP SMI versions, which version should you use? Futures. SNMPv1 packets SNMP in the 7 layer model, port numbers, general packet format, BER, GET, GET-NEXT, tables, SET, TRAP, bandwidth issues, in band versus out of band management. Hands on Analysing SNMPv1 packets. SNMPv2 packets SNMPv2 improvements, error handling, GETBULK, v2traps, INFORM. Hands on Analysing SNMPv2 packets. SNMPv3 packets SNMPv3 packet format, use of SNMPv2 messages, REPORT PDU. MIB structure The internet MIB branch, standard mib-2, extra parts of mib-2, private enterprise MIBs, loading extra MIBs. Hands on MIB browsing. mib-2 The mib-2 groups, system group, interfaces group, IP group, ICMP group, TCP group, UDP group, transmission group, SNMP group, RMON. Hands on mib-2 browsing in detail. SMI The MIB layout, obtaining a private enterprise number, MIB definitions, IMPORT, Module identity, Textual conventions, object definitions, notifications, compliance statements, object groups, base SMI data types, application data types, scalars, instances, tables, table definition, writing agents, SMIng. SNMP security Community strings, SNMPv1 and SNMPv2c security practices, SNMPv3 security, SNMPv3 architecture, SNMP applications, the SNMP engine, the EngineID, security fields in SNMPv3 packets, USM, authentication, encryption, timeliness, VBAC, SNMPv3 configuration.

OSPF training course description A detailed hands on examination of OSPF. Hands on sessions are used to reinforce the theory rather than teach specific manufacturer equipment. The course starts with a recap of reading routing tables and then jumps straight in with simple OSPF configuration. OSPF features are then studied and configured before moving onto how OSPF works within an area. Multi area OSPF is then studied before looking at OSPF operation in detail by analysing OSPF packets. Finally areas are covered again in more detail followed by troubleshooting. What will you learn Design OSPF networks. Design IP addressing schemes suitable for route summarisation. Troubleshoot OSPF networks. Describe the operation of OSPF. OSPF training course details Who will benefit: Technical staff working with OSPF. Prerequisites: TCP/IP Foundation for engineers Duration 3 days OSPF training course contents Basic routing and OSPF Reading routing tables, routing protocols, What is OSPF? Process IDs, passive interfaces. Hands on Simple OSPF configuration. OSPF History of OSPF, metrics, costs, convergence, Distance Vector vs. Link state routing protocols, IGPs, classless, OSPF features, load sharing, per packet/destination, OSPF authentication. Hands on Configuring OSPF features. OSPF within an area How OSPF works, LSDB, LSDB benefits and disadvantages, LSA types, Type 1 and 2, LSA propagation, router IDs, hellos, configuring hellos, the exchange protocol. Hands on Investigating OSPF structures. Areas Scalability, why areas? Area IDs, area 0, ABRs, ABR resilience, areas & LSDBs, areas & LSAs, Type 3 LSAs, virtual links. Hands on Multi area OSPF. Redistribution Multiple routing protocols, common scenarios, routing distance, External LSAs, E1 and E2. Type 4 LSAs. OSPF and default routes. Hands on Configuring static route redistribution. Route aggregation Route summarisation. How to aggregate, ABR summarisation, ASBR summarisation. Hands on OSPF address summarisation. OSPF packet formats OSPF packets, protocol stack, OSPF stages, packet flows, packet types, the OSPF header, multicasts, Hello, DDB, LS request, LS update, LS ACK, LSA header, LSA formats, neighbours, neighbour states, DRs, adjacencies, BDRs, DR election. Hands on Analysing OSPF packets, troubleshooting. OSPF network types BMA, NBMA, Point to point links. Hands on Configuring OSPF over Frame Relay. OSPF stub areas LSA types, area types, area architecture, stub areas, default routes, benefits and disadvantages of stub areas, TSSAs, NSSAs, Type 7 LSAs. Hands on Stub and TSSA configuration. The OSPF MIB SNMP overview, MIB 2, the OSPF MIB, OSPF MIB groups, useful objects, OSPF traps. Hands on the OSPF MIB. troubleshooting. Summary RFCs, OSPF design guidelines. OSPF variants (appendix) OSPF on demand, MOSPF, multicast overview, Type 6 LSAs, OSPF for IPv6 (OSPFv3).

DNS training course description This three-day hands on DNS training course studies both the UNIX BIND and the Microsoft (MS DNS) implementations. The course starts with the big picture of how DNS works, then client configuration. Primary and secondary servers are then configured, progressing to DDNS, subdomains and security issues. Hands on sessions follow all sections ensuring that troubleshooting techniques are used throughout the course. Students choose whether to use Windows or UNIX for the hands on sessions. What will you learn Describe the architecture of DNS. Explain how DNS works. Install, configure, maintain and troubleshoot DNS DNS training course details Who will benefit: Technical staff wanting to learn DNS including: Network personnel System administrators. Prerequisites: UNIX Fundamentals (or Windows knowledge). TCP/IP foundation for engineers. Duration 3 days DNS training course contents What is DNS? Hostnames, Name resolution, host files, host file problems, What is DNS? The DNS namespace, TLDs, gTLDs, registering domains, Nameservers, how DNS works. Hands on Testing DNS servers on the Internet. DNS clients Ways to use DNS, dynamic and static configuration, multiple nameservers, domain name, searchlist, resolution issues, testing the configuration. Hands on Client configuration. DNS server software Implementations, Microsoft, BIND, daemons and services, installation, starting and stopping servers. Hands on Setting up a DNS server. DNS zone files What is a zone, Zone file overview, Forward zones, Reverse zones, Resource records, A records, PTR, CNAME, Root hints, local zone file. BIND and Microsoft configuration. Hands on Server configuration files. NS and applications MX records, Mail server load balancing, SPF, SRV records, VoIP and SRV, Microsoft and SRV, NAPTR. Hands on Testing records with dig and nslookup. DNS slaves and other servers DNS server types, Server resilience, Slaves, Zone transfers, SOA records, Serial numbers, recommendations, polling based zone transfers, NOTIFY, AD integration, DNS caching, Negative caching, TTL, Caching only servers. Hands on Masters, slaves and zone transfers. The DNS protocol The DNS stack, DNS port numbers, DNS queries, The DNS header, header section format, question format, other section format. Hands on Troubleshooting DNS with Wireshark. Dynamic DNS DHCP, DDNS, IXFR, WINS integration. Hands on Dynamic DNS. Subdomains Root servers, root server selection, Authority, delegation, NS records, subdomain with and without delegation, reverse delegation. Hands on Delegation, setting up a subdomain server. DNS security Restricting queries, DNS and firewalls, Split DNS, forwarders, internal root servers, the use of proxy servers, DNSSEC, TSIG. Hands on Hardening a DNS server. DNS and IPv6 What is IPv6, IPv6 addressing, IPv6 DNS issues, AAAA, IPv6 reverse delegation. Troubleshooting DNS Problem solving, DNS troubleshooting, Zone file checking, Some common errors, Log files, tools, nslookup, dig, host, DNS design, performance, load balancing. Hands on Putting it all together. Summary Useful books, Internet sites, RFCs. Appendix: ENUM What is ENUM, How ENUM works, NAPTR.

IPsec training course description This hands on course focuses on IPsec VPNs. Rather than focusing on one implementation this course concentrates on the technologies and protocols of IPsec. Starting with an overview of the complete IPsec architecture the course then moves onto ESP packet analysis along with encryption and authentication provided. IKEv1 and IKEv2 are both covered in detail. Having covered IPsec with pre shared keys the course then moves onto IPsec with certificates followed by IPsec issues. The course is vendor neutral with hands on with both Cisco and Microsoft implementations. What will you learn Explain how IPsec works. Explain the role of AH, ESP and IKE. Configure IPsec. Troubleshoot IPsec. IPsec training course details Who will benefit: Technical staff working with IPsec. Prerequisites: Definitive IP VPNs for engineers. Duration 3 days IPsec training course contents What is IPsec? How to spell IPsec, IPsec is IP security, confidentiality, integrity, authenticity, replay protection, what is a VPN? Network layer security, IPsec and IPv4, IPsec and IPv6, the suite of protocols, the standard, IPsec RFCs, IPsec history. Hands on Analysis of 'normal' IP packets. IPsec architecture The IPsec protocols, AH vs ESP, Why two headers? transport mode, tunnel mode, Remote access VPNs, site to site VPNs, security associations, SA database, Security Parameters Index, implementations: Host tack, Bump in the Stack, Bump in the Wire. Hands on Configuring IPsec. AH What AH does, the stack, The AH header, What is authenticated? Device authentication. AH in transport mode, AH in tunnel mode. Hands on AH packet analysis. ESP What ESP does, the ESP header, ESP in transport mode, ESP in tunnel mode, ESP and SA, ESP and SPI. Hands on ESP packet analysis, policy configuration. IPsec encryption IPsec is a framework, standard algorithms, ESP keys, the role of IKE, key lifetimes, how IKE generates the keys, DES, 3DES, AES, cipher block chaining, counter mode, other encryption. Hands on Encryption configuration. IPsec authentication Authentication types, IPsec authentication, Authentication algorithms: MD5, keyed SHA-1, HMAC-MD5, HMAC-SHA-1, HMAC-RIPEMD, other authentication algorithms. Hands on Authentication configuration. IKE Internet Key Exchange, IKE and the SAD, the two phase negotiation, ISAKMP, ISAKMP header, pre shared keys, digital signatures, public key encryption, Diffie Hellman, proposals, counter proposals, nonces, identities, phase 1 negotiation: main mode, aggressive mode, base mode. Phase 2 negotiation: quick mode, new group mode. Hands on IKE packet analysis. More IKE PFS, IKE and dynamic addresses, XAUTH, hybrid authentication, CRACK, ULA, PIC. User level authentication. IKE renegotiation, heartbeats. Hands on Troubleshooting IPsec. IKEv2 The IKEv2 exchange, IKE_SA_INIT, IKE_AUTH, CREATE_CHILD_SA, IKEv2 packets, the informational exchange. Comparing IKev1 vs IKE v2. Hands on IKEv2 configuration and analysis. PKI What is PKI?, Digital certificates, Certificate authorities, CA servers, RA, VA, certificates, CA hierarchy, CRLs, certificate formats. Hands on installing and configuring certificate servers. IPsec issues NAT, IPsec overhead and fragmentation. Summary IPsec strengths and weaknesses. Where to get further information.

Gain n-depth knowledge about Docker technology and the confidence to help your company or your own project to apply the right Docker deployment workflow. Learn all about Docker, Docker REST API, and Docker continuous integration to build Docker images.

Total IP multicast training course description This training course provides an advanced three day hands on study of IP multicast technology focusing on architectures, applications and protocols. All aspects of IP multicasting are covered including PC, server and switch implementations. Design, configuration, support and troubleshooting are all covered in the course. Hands on sessions are used to reinforce the theory rather than teach specific implementations. What will you learn Design multicast networks. Explain how multicast networks work. Compare and contrast the different multicast routing protocols, such as DVMRP, PIM, MBGP and SSM. Configure PCs, servers, switches and routers for multicasting. Configure multicast routing protocols including: PIM Dense Mode. PIM Sparse Mode BGP SSM Troubleshoot multicast networks. Total IP multicast training course details Who will benefit: Technical staff working with IP multicasts. Prerequisites: TCP/IP Foundation for engineers Duration 3 days Total IP multicast training course contents Introduction What is multicasting? Why multicast? Why not multicast? Multicasting vs. multiple unicasts, Multicasting vs. broadcasts, multicasting applications, the use of unicast addressing for setting up multicast applications, multicast use within standard protocols such as OSPF. hands on Example multicast applications. Addressing Layer two multicast addresses, Class D addresses, mapping layer 3 addresses onto layer 2 multicast addresses. Multicast addresses on NBMAs, scoping multicast traffic, Multicast address blocks, GLOP, IPv6 and multicasting, anycasting. hands on Multicast addressing. Multicast architectures Where the different protocols are used, PC to router, router to router, how switches can get involved. hands on Analysing multicast packets. PC to router Configuring Class D addresses, IGMP, packet formats, queries, reports, maintaining groups, enhancements to IGMP (v2 and v3), Leaving a group, querier elections, hands on Analysing IGMP packets. Switches and multicasting Controlling multicast traffic with switches, VLANS, static bridge table entries, IGMP snooping, CGMP. hands on Configuring switches for multicast environments. Router to router MOSPF, DVMRP, PIM Sparse Mode, PIM Dense Mode, MBGP. hands on Simple router configuration for multicasting. Theory behind multicast routing protocols Distribution trees, source distribution trees, shared trees, core based trees. Reverse path forwarding, Multicast routing protocol types. PIM DM: Flooding, pruning, PIM designated routers, hands on configuring PIM DM. PIM Sparse mode Rendevous points, discovering RPs, hands on Configuring PIM SM, using different protocols for different groups. PIM SM with one RP, using multiple RPs, Auto RP. MBGP Multiprotocol routing, how does MBGP work? How MBGP carries multiple protocol information, MBGP and multicasts, MBGP and IPv6. hands on Configuring MBGP for multicasts. Internet multicasting The internet, ISPs, the MBone, tunnelling, Inter domain multicasting, the role of MBGP, Inter domain problem, MSDP, MSDP operation SSM, PIM-SM and shared trees, SSM, PIM-SSM operation, SSM benefits. hands on MSDP configuration. SSM configuration.

SMTP training course description A hands on course focusing on the workings of email systems and the standard protocols that they use. The course is not specific to any particular implementation, but some vendor specifics are noted. Linux and Microsoft machines are used in hands on sessions to reinforce the theory of major sessions. The course concentrates on troubleshooting and interworking using network sniffing and protocol inspection rather than "which buttons to push". What will you learn Describe and explain SMTP MIME POP3 IMAP PGP, GPG, S/MIME SPF, DKIM, DMARC Configure mail routing Secure email systems SMTP training course details Who will benefit: Technical staff responsible for email systems. Prerequisites: TCP/IP foundation for engineers. Duration 3 days SMTP training course contents SMTP architecture What is SMTP, email before SMTP, SMTP history, the different protocols, clients, servers. Email composition, transmission, delivering emails, storing and reading emails. MUAs, MTAs, POP3, IMAP, SMTP, DNS, webmail. Hands on Setting up MTAs and MUAs and sending a simple email using telnet. The SMTP protocol SMTP protocol stack, SMTP headers, HELO, SMTP mail, MAIL FROM, RCPT TO, DATA, SMTPUTF8, 8BITMIME, TURN, EHLO, ETRN, 3 digit replies. Hands on Analysing SMTP packets on a network. DNS and SMTP SMTP forwarding, SMTP relays, interoperation, how SMTP uses DNS, MX records. Hands on Setting up mail relays. SMTP headers IMF data, From, to, cc, bcc, sender and recipient headers, message Ids, received trails, in-reply-to, received-SPF, mail list headers. Hands on Using clients to analyse details from mail headers, including true originators and path of emails. MIME Email attachments, MIME versions, content type headers, encoding, base 64, binary data, multi part headers, troubleshooting attachments. Hands on Analysing MIME headers and attachments. POP3 What is POP3, where to use POP3, authorisation, transactions, POP3 commands: USER, PASS, STAT, LIST, RETR, DELE. Hands on Setting up a POP3 server, analysing POP3 packets on a network. IMAP and IMAPS What is IMAP, where to use IMAP, authorisation, mailbox structure, IMAP commands: LOGIN, AUTHENTICATE, LIST, CREATE, Examine (message flags), SELECT, STORE. Hands on Setting up an IMAP server and analysing IMAP packets on a network. Interoperation Mail gateways, addressing, Exchange, sendmail. Email security Basics, Transport level: STARTTLS. Content: PGP/GPG, mail signing and encryption, S/MIME, digital certificates, secure email submission. Hands on Setting up and using a PGP key, configure MTAs to use TLS. Email authentication and spam prevention Mail relays, grey listing, block list & RBL, DNSBL (Real-time Black hole List), White list, SPF, Domain Keys Identified Mail (DKIM), Author Domain Signing Practices (ADSP), Abuse Report Format (ARF), Domain-based Message Authentication, Reporting and Conformance (DMARC). Hands on Relay spamming and the blocking spamming.

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement 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.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 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.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Looking to build an AI application from scratch? Look no further than this compact course with ChatGPT! Using the OpenAI API and the latest web development technologies, including React, Next.js, JavaScript, Node, and CSS, you will gain hands-on experience building an AI-powered application that generates pet names for users.