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Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is as follows: Technical professionals who are responsible for installation, deployment, and maintenance of the Cisco ONS 15454 MSTP network Network operations, planners, and designers Network operations engineers Overview Upon completing this course, the learner will be able to meet these overall objectives: Connect to a Cisco ONS 15454 MSTP chassis using Cisco Transport Controller (CTC) Identify Node configurations according to card population Provision DWDM circuits using the Cisco Transport Controller (CTC) Conduct performance monitoring, alarm verification, and fault isolation Provision M12 WSS in linear and M6 SMR nodes in ring topologies Configuration options for the any rate muxponder and crossponder Perform Raman amplifier initialization Isolate optical network issues In this course, you will learn the skills necessary to deploy a Cisco Network Convergence System (NCS) 2000 Series network. You will also learn how to perform node turnup. The course covers three shelf types, the Cisco ONS 15454 M12 Multiservice Transport Platform (MSTP), NCS 2006, and NCS 2002. You will learn how to deploy linear and ring dense-wavelength-division-multiplexing (DWDM) topologies. The course covers multiplexer-demultiplexer cards, Erbium-doped-fiber amplifier cards, Raman amplifiers, transponder cards, and the newest Cisco Any Rate muxponder cards and crossponder cards. These cards are used in terminal, amplifier, and reconfigurable optical add-drop multiplexer (ROADM) node configurations. You configure wavelength-selective switch (WSS) linear and single-module ROADM (SMR) rings. This Optical Technical Training Intermediate course covers 10-gigabit unprotected circuits and 10-gigabit protection using Y-cable, optical channel transport unit-2 (OTU-2), and protection switch module (PSM) cards. Learn more about this NCS 2000 training course below. Course Outline Lesson 1: CTC Operations Lesson 2: MSTP Topologies Lesson 3: Shelf and Card Installation Lesson 4: Fiber jumper installation Lesson 5: Linear Configurations Lesson 6: Node Turn-Up Lesson 7: Optical Channel Network Connection Circuits Lesson 8: Transponder and Optical Channel Client Connection Circuits Lesson 9: Multishelf Lesson 10: MSTP M6 SMR-Based Rings Lesson 11: 10-Gigabit Muxponder and Transponder Cards Lesson 12: 10-Gigabit with Y-Cable Protection Lesson 13: Alternative 10-GB Protection (PSM and OTU-2) Lesson 14: Any Rate Muxponder and Crossponder Lesson 15: Raman Amplifier Lesson 16: 40- and 100-Gigabit Transponder and Muxponder Lesson 17: Troubleshooting Additional course details: Nexus Humans Cisco Optical Technology Intermediate (OPT200) 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 Cisco Optical Technology Intermediate (OPT200) 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.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

5G training course description This course is designed to give the delegate an understanding of the technologies and interworking requirements of the next generation of cellular communications. It is not a definitive set of descriptions but a possibility of the final deployment. During the course we will investigate the 10 pillars for 5G, which will include various Radio Access Technologies that are required to interwork smoothly. Hence we will look at the 4G Pro features and other RATs. What will you learn List the ten pillars of 5G deployment. Explain the 5G Internet and Software Distributed Networks (SDN). Explain carrier aggregation, the mobile cloud and RAT virtualisation. Explain an overall picture of 5G architecture. 5G training course details Who will benefit: Anyone who is looking to work with next generation networks. Prerequisites: Mobile communications demystified Duration 3 days 5G training course contents Drivers for 5G 5G Road Map, 10 Pillars of 5G, evolving RATs, small cell, o SON, MTCm, mm-wave, backhaul, EE, new spectrum, spectrum sharing, RAN virtualisation. 4G LTE advanced features *MIMO, Downlink & uplink MIMO R8, MIMO technology in LTE advanced, Downlink 8-layer SU-MIMO, Downlink MU-MIMO, Uplink MU-MIMO, Uplink transmit diversity, Coordinated multi-point operation (CoMP), Independent eNB & remote base station configurations, Downlink CoMP, * Uplink Multi-Cell Reception. ICIC & eICIC ICIC, Homogeneous to heterogeneous network, eICIC, Macro-pico scenario, Macro-femto scenario, Time orthogonal frequencies. Almost Blank Subframe (ABS). Carrier aggregation Component carriers (CC), * CC aggregation, Intra-band contiguous solutions, Intra-band non-contiguous solutions, Inter-band non-contiguous solutions, CA bandwidth classes, Aggregated transmission bandwidth configurations (ATBC), Possible carrier aggregation configurations (Rel 9, 10 & 12). Enhanced Interference Mitigation & Traffic Adaptation (eIMTA) TDD UL-DL reconfiguration for traffic adaptation, Reconfiguration mechanisms, Interference mitigation schemes, Dynamic & flexible resource allocation. 5G architectures 5G in Europe, horizon 2020 framework, 5G infrastructure PPP, METIS project, innovation centre, 5G in North America, research, company R & D, 5G specifications. The 5G internet Cloud services, IoT & context awareness, network reconfiguration & virtualization support, hypervisors, SDN, the controller, service-oriented API, OpenFlow switches, SDN operation, SDN control for traffic flow redirection, OpenFlow controllers, how SDN works, application, control and infrastructure layers, a programmable network, how SDN & NFV tie together, SDN's downside, SDN orchestration, Mobility, architectures for distributed mobility management, MEDIEVAL & MEDIVO projects, a clean slate approach, mobility first architecture, network virtualization (VNet), INM, NetInf, ForMux, MEEM, GP & AM, QoS support, network resource provisioning, IntServ, RSVP, DiffServ, CoS, aggregated resource provisioning, SICAP, MARA, Emerging approach for resource over-provisioning, example use case architecture for the 5G internet, integrating SDN/NFV for efficient resource control, control information repository, service admission control policies, network resource provisioning, control enforcement functions, network configurations, network operations. Small cells for 5G Average spectral efficiency evolution, What are small cells? WiFi & Femto cells as candidate small-cell technologies, Capacity limits & achievable gains with densifications, gains with multi-antenna techniques, gains with small cells, Mobile data demand, approach & methodology, subscriber density projections, traffic demand projections, global mobile data traffic increase modelling, country level backhaul traffic projections, 2020 average spectrum requirement, Small cell challenges, backhaul, spectrum, automation. Cooperation for next generation wireless networks Cooperative diversity & relaying strategies, Cooperative ARQ & MAC protocols, NCCARQ & PRCSMA packet exchange, Physical layer impact on MAC protocol, NCCARQ overview, PHY layer impact, Performance evaluation, simulation scenario and results. Mobile clouds; technology & services for future communications platforms Mobile cloud, software, hardware and networking resources, Mobile cloud enablers, mobile user domain, wireless technologies, WWAN WLAN and WPAN range, Bluetooth, IEEE.802.15.4, software stacks, infrared, near field communications (NFC), store & forward vs compute & forward, random/linear network coding. Security for 5G communications Potential 5G architectures, Security issues & challenges in 5G, user equipment, mobile malware attacks, 5G mobile botnets, attacks on 4G networks, C-RNTI & packet sequence numbers based UE location tracking, false buffer status reports attacks, message insertion attacks, HeNB attacks, physical attacks, attacks on mobile operator's network, user data & identity attacks, DDoS attacks, amplification, HSS saturation, external IP networks.

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.

The course is relevant to anyone requiring an understanding of the use of Agile or looking to adopt it. This includes, but is not limited to, organisational leaders and managers, marketing executives and managers, and/or all professionals working in an Agile environment, including software sesters, developers, business analysts, UX designers, project management office (PMO), project support and project coordinators.

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

Why Choose Character Animation in 3ds Max Training Course? Click here for more info. Top character animation course in 3ds Max, this course provides an accessible learning experience. Learning character animation enables you to create your own short films. It's not just a means of income; it evolves into a passion.  Duration: 20 hrs Method: 1-on-1, Personalized attention. Schedule: Tailor your own hours of your choice, available from Monday to Saturday between 9 am and 7 pm. Enroll in our exclusive "Character Animation Fundamentals in 3ds Max" course at London Design Training, guided by experienced tutors Sitwat Ali, Qasim Ali, and Jess. Gain in-depth insights into animating 3D characters, covering essential techniques like character rigging, pose creation, and seamless pose-to-pose animation. 3ds Max Character Animation Course Duration: 20 hours Course Overview: Master the art of character animation in 3ds Max with our comprehensive course. Ideal for beginners and those with some 3D modeling and animation experience, this course covers everything you need to know to bring characters to life. Course Outline: Introduction to Character Animation Explore animation principles Get familiar with 3ds Max animation tools Learn to create character rigs and manage the timeline Basic Animation Principles Understand keyframes and animation cycles Apply the 12 principles of animation Work with the graph editor and ease-in/out techniques Advanced Animation Techniques Utilize the reaction manager for complex animations Master non-linear animation methods Animate with inverse kinematics, custom controllers, expressions, and scripts Creating Characters Craft a character model with proper topology Create UV maps and apply textures Prepare characters for rigging Facial Animation Learn facial animation principles Create blend shapes and morph targets Master lip syncing techniques Body Animation Animate walk cycles and character motion Achieve believable character poses Implement character physics Advanced Character Animation Work with motion capture data Use CAT and Biped tools Understand motion blur and create special effects Render and output animations Character Animation Projects Bring all skills together in practical projects Create basic and complex character animations Course Requirements: Computer with 3ds Max installed Basic computer operations knowledge Passion for character animation Course Goals: Upon completion, you'll have a thorough grasp of character animation in 3ds Max, capable of creating realistic and sophisticated character animations using advanced techniques. You'll be equipped with the skills to continue honing your character animation abilities independently.

Who is this course for? 3ds Max Character Animation Training Course. Master character animation in our 3ds Max course. All levels welcome. Learn from certified tutors in flexible in-person or online sessions. Create unique 3D characters from scratch, gaining personalized techniques to fuel your creativity. Click here for more info: Website Duration: 20 hours Method: 1-on-1 personalized attention Schedule: Flexible 1-on-1 sessions. Schedule your sessions at your convenience, choosing any hour between 9 am and 7 pm from Mon to Sat. Course Title: 3ds Max Character Animation Workshop Duration: 20 Hours Course Overview: This workshop is meticulously crafted to instill the foundational principles of character animation utilizing 3ds Max. Whether you're a novice or possess some background in 3D modeling and animation, this course caters to your learning needs. You'll delve into the art of character rigging, grasp animation essentials, and employ advanced methods to breathe life into your characters. Course Outline: Module 1: Introduction to Character Animation Grasping animation principles Exploring 3ds Max animation tools Mastering character rig creation Understanding the intricacies of the timeline Module 2: Basic Animation Principles Embracing keyframe dynamics Crafting fundamental animation cycles Applying the 12 principles of animation Navigating the graph editor Utilizing ease-in and ease-out techniques Module 3: Advanced Animation Techniques Harnessing the power of the reaction manager Crafting non-linear animations Implementing inverse kinematics for dynamic movements Designing custom controllers Exploring expressions and scripts Module 4: Character Creation Sculpting a character model from scratch Grasping the nuances of topology Perfecting UV maps and texturing techniques Preparing characters for seamless rigging Module 5: Facial Animation Mastering facial animation principles Creating expressive blend shapes Utilizing morph targets for nuanced expressions Achieving flawless lip syncing Module 6: Body Animation Crafting seamless walk cycles Animating characters in motion Creating authentic and believable poses Employing character physics for lifelike movements Module 7: Advanced Character Animation Implementing motion capture data for realistic animations Leveraging CAT and Biped for intricate movements Understanding motion blur nuances Adding special effects for enhanced realism Fine-tuning rendering and outputting animations Module 8: Character Animation Projects Synthesizing knowledge into practical applications Creating a fundamental character animation Crafting a nuanced walk cycle Executing complex character animations Course Requirements: Access to a computer with 3ds Max installed Basic proficiency in computer operations Enthusiasm for delving into the world of character animation Course Goals: Upon completion, you will possess a profound understanding of character animation in 3ds Max. You'll be equipped with the expertise to create intricate, lifelike character animations using advanced techniques. Moreover, you'll gain the skills necessary to continue honing your craft, ensuring a solid foundation for your future endeavors in the realm of character animation. Upon successful completion of the 3ds Max Character Animation Workshop, participants will: Master Fundamental Principles: Understand the core principles of character animation, including keyframe dynamics, timing, and the 12 principles of animation, laying a strong foundation for advanced techniques. Proficient Software Usage: Navigate 3ds Max confidently, utilizing animation tools, character rigging techniques, and specialized editors for precise control over character movements. Advanced Animation Techniques: Apply advanced techniques such as non-linear animations, inverse kinematics, and custom controller design to create dynamic and realistic character movements. Facial Animation Mastery: Demonstrate expertise in facial animation by creating expressive blend shapes, morph targets, and achieving seamless lip syncing for realistic character emotions. Body Language Proficiency: Create fluid and natural body movements, including walk cycles, dynamic poses, and character motions, capturing the essence of lifelike animations. Special Effects Integration: Integrate special effects seamlessly into character animations, enhancing visual appeal and realism in the final output. Project Implementation: Apply acquired knowledge and skills in practical projects, including basic character animations, walk cycles, and complex character animations, demonstrating proficiency in real-world scenarios. Problem-Solving Skills: Develop problem-solving abilities related to character animation challenges, employing creative solutions to achieve desired results. Collaborative Skills: Engage in collaborative projects, demonstrating effective communication and teamwork while integrating animations into broader creative contexts. Portfolio Enhancement: Build a robust portfolio showcasing diverse character animations, reflecting both technical prowess and creative expression, essential for career advancement in the animation industry. Continued Learning: Acquire the skills and confidence necessary to pursue further learning and self-improvement in the field of character animation, enabling a continuous growth trajectory in the industry. Course Title: 3ds Max Character Animation Workshop Duration: 20 Hours Key Details: Course Focus: Comprehensive training in character animation using 3ds Max, covering fundamental principles, advanced techniques, facial animation, body language, special effects integration, and project-based learning. Audience: Ideal for beginners and individuals with some background in 3D modeling and animation, aiming to enhance their skills in character animation for industries such as animation studios, gaming, and film production. Instruction Method: Interactive, instructor-led sessions combining theoretical knowledge with hands-on practical exercises, fostering a dynamic learning environment. Flexible Learning Options: Participants can choose between in-person and live online sessions, accommodating diverse schedules and geographical locations. Certified Instructors: Experienced tutors and industry professionals with certification in 3ds Max and character animation, ensuring high-quality instruction and personalized guidance. Project-Based Learning: Engage in real-world projects, applying learned skills to create character animations, walk cycles, and intricate character movements, fostering practical expertise. Software Proficiency: Gain proficiency in 3ds Max, including animation tools, character rigging, and specialized editors, enabling participants to confidently navigate the software. Collaborative Learning: Opportunities for teamwork and collaborative projects, encouraging effective communication and networking within the class. Career Development: Build a diverse and impressive portfolio, receive guidance on industry best practices, and develop problem-solving skills crucial for a successful career in character animation. Post-Course Support: Access to resources, tutorials, and community forums, allowing participants to continue learning and stay updated with industry trends even after the course completion. Certification: Participants receive a certificate of completion, recognizing their proficiency in 3ds Max character animation, enhancing their professional credibility in the job market. By enrolling in this course, you'll enjoy the following advantages: Comprehensive Learning: Master the art of character animation in 3ds Max, covering fundamental concepts and advanced techniques. Certified Tutors and Industry Experts: Learn from experienced professionals with extensive knowledge of character animation, providing valuable insights. Personalized Instruction: Receive one-to-one training tailored to your specific learning needs, ensuring individual attention and effective progress. Flexible Learning Options: Choose between in-person or live online training, offering convenience and accessibility to suit your schedule. Recorded Lessons: Access recorded sessions to review content and reinforce your learning at your own pace and convenience. Lifetime Email Support: Benefit from ongoing assistance and guidance through email, even after completing the course. Free Career Advice: Tap into our industry expertise and receive valuable career guidance to excel in the field of character animation.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently