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Duration 5 Days 30 CPD hours This course is intended for This course is targeted towards the information technology (IT) professional that has a minimum 1 year IT Security and Networking experience. This course would be ideal for Information System Owners, Security Officers, Ethical Hackers, Information Owners, Penetration Testers, System Owner and Managers as well as Cyber Security Engineers. Overview Upon completion, the Certified Professional Ethical Hacker candidate will be able to competently take the CPEH exam. The CPEH certification training enables students to understand the importance of vulnerability assessments and how to implement counter response along with preventative measures when it comes to a network hack. Security Fundamentals Overview The Growth of Environments and Security Our Motivation? The Goal: Protecting Information! CIA Triad in Detail Approach Security Holistically Security Definitions Definitions Relationships Method: Ping The TCP/IP Stack Which Services Use Which Ports? TCP 3-Way Handshake TCP Flags Malware Types of Malware Types of Malware Cont... Types of Viruses More Malware: Spyware Trojan Horses Back Doors DoS DDoS Packet Sniffers Passive Sniffing Active Sniffing Firewalls, IDS and IPS Firewall ? First Line of Defense IDS ? Second Line of Defense IPS ? Last Line of Defense? Firewalls Firewall Types: (1) Packet Filtering Firewall Types: (2) Proxy Firewalls Firewall Types ? Circuit-Level Proxy Firewall Type of Circuit- Level Proxy ? SOCKS Firewall Types ? Application-Layer Proxy Firewall Types: (3) Stateful Firewall Types: (4) Dynamic Packet-Filtering Firewall Types: (5) Kernel Proxies Firewall Placement Firewall Architecture Types ? Screened Host Multi- or Dual-Homed Screened Subnet Wi-Fi Network Types Wi-Fi Network Types Widely Deployed Standards Standards Comparison 802.11n - MIMO Overview of Database Server Review Access Controls Overview Role of Access Control Definitions More Definitions Categories of Access Controls Physical Controls Logical Controls ?Soft? Controls Security Roles Steps to Granting Access Access Criteria Physical Access Control Mechanisms Biometric System Types Synchronous Token Asynchronous Token Device Memory Cards Smart Card Cryptographic Keys Logical Access Controls OS Access Controls Linux Access Controls Accounts and Groups Password & Shadow File Formats Accounts and Groups Linux and UNIX Permissions Set UID Programs Trust Relationships Review Protocols Protocols Overview OSI ? Application Layer OSI ? Presentation Layer OSI ? Session Layer Transport Layer OSI ? Network Layer OSI ? Data Link OSI ? Physical Layer Protocols at Each OSI Model Layer TCP/IP Suite Port and Protocol Relationship Conceptual Use of Ports UDP versus TCP Protocols ? ARP Protocols ? ICMP Network Service ? DNS SSH Security Protocol SSH Protocols ? SNMP Protocols ? SMTP Packet Sniffers Example Packet Sniffers Review Cryptography Overview Introduction Encryption Cryptographic Definitions Encryption Algorithm Implementation Symmetric Encryption Symmetric Downfalls Symmetric Algorithms Crack Times Asymmetric Encryption Public Key Cryptography Advantages Asymmetric Algorithm Disadvantages Asymmetric Algorithm Examples Key Exchange Symmetric versus Asymmetric Using the Algorithm Types Together Instructor Demonstration Hashing Common Hash Algorithms Birthday Attack Example of a Birthday Attack Generic Hash Demo Instructor Demonstration Security Issues in Hashing Hash Collisions MD5 Collision Creates Rogue Certificate Authority Hybrid Encryption Digital Signatures SSL/TLS SSL Connection Setup SSL Hybrid Encryption SSH IPSec - Network Layer Protection IPSec IPSec Public Key Infrastructure Quantum Cryptography Attack Vectors Network Attacks More Attacks (Cryptanalysis) Review Why Vulnerability Assessments? Overview What is a Vulnerability Assessment? Vulnerability Assessment Benefits of a Vulnerability Assessment What are Vulnerabilities? Security Vulnerability Life Cycle Compliance and Project Scoping The Project Overview Statement Project Overview Statement Assessing Current Network Concerns Vulnerabilities in Networks More Concerns Network Vulnerability Assessment Methodology Network Vulnerability Assessment Methodology Phase I: Data Collection Phase II: Interviews, Information Reviews, and Hands-On Investigation Phase III: Analysis Analysis cont. Risk Management Why Is Risk Management Difficult? Risk Analysis Objectives Putting Together the Team and Components What Is the Value of an Asset? Examples of Some Vulnerabilities that Are Not Always Obvious Categorizing Risks Some Examples of Types of Losses Different Approaches to Analysis Who Uses What? Qualitative Analysis Steps Quantitative Analysis ALE Values Uses ALE Example ARO Values and Their Meaning ALE Calculation Can a Purely Quantitative Analysis Be Accomplished? Comparing Cost and Benefit Countermeasure Criteria Calculating Cost/Benefit Cost of a Countermeasure Can You Get Rid of All Risk? Management?s Response to Identified Risks Liability of Actions Policy Review (Top-Down) Methodology Definitions Policy Types Policies with Different Goals Industry Best Practice Standards Components that Support the Security Policy Policy Contents When Critiquing a Policy Technical (Bottom-Up) Methodology Review Vulnerability Tools of the Trade Vulnerability Scanners Nessus SAINT ? Sample Report Tool: Retina Qualys Guard http://www.qualys.com/products/overview/ Tool: LANguard Microsoft Baseline Analyzer MBSA Scan Report Dealing with Assessment Results Patch Management Options Review Output Analysis and Reports Overview Staying Abreast: Security Alerts Vulnerability Research Sites Nessus SAINT SAINT Reports GFI Languard GFI Reports MBSA MBSA Reports Review Reconnaissance, Enumeration & Scanning Reconnaissance Overview Step One in the Hacking ?Life-Cycle? What Information is Gathered by the Hacker? Passive vs. Active Reconnaissance Footprinting Defined Social Access Social Engineering Techniques Social Networking Sites People Search Engines Internet Archive: The WayBack Machine Footprinting Tools Overview Maltego GUI Johnny.Ihackstuff.com Google (cont.) Domain Name Registration WHOIS Output DNS Databases Using Nslookup Traceroute Operation Web Server Info Tool: Netcraft Introduction to Port Scanning Which Services use Which Ports? Port Scan Tips Port Scans Shou

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Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

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Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

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