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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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Our Team Building Programmes are 'simply excellent' (quote from Unilever). They always deliver much greater energy' motivation and efficiently accelerates to a galvanised, integrated team for their Manager/Team Leader. They're great fun and very commercially orientated - the best of both key elements to a successful and long-lasting high performance team. A successful company is always made up of successful teams. Teams that can work autonomously with a clearly defined set of goals, roles, vision, responsibility and culture will always reach for and achieve far greater success than a team that works just as a group of individuals. Our team building solutions are individually built and geared towards teams at any level within an organisation, providing an independent and objective perspective to promote a common purpose such as the creation of a 'high performance team'. Out With The Old Traditionally, team building events have been restricted to certain levels of management where they head off site for a bit of archery, quad biking and paintballing or something along those lines. Then over some coffee and cocktails, business plans and more efficient ways to work are casually discussed. Whilst being out having fun instead of being at work may improve an individual person's mood, the effect will only be short-term, and will not go far in creating permanent and cohesive teams who are able to overcome challenges together and drive the business forward when back in the workplace. In With The New Today's business thinking is more strategic and certainly has to look for returns on the investment. That is why Dickson Training Ltd's team building programmes are bespoke and built to your requirements through research, understanding your business and, most importantly, what results and achievements you are looking to get out of the programme. Once "what success looks like" has been established, we create tasks and activities that will test your leadership, problem solving, communication and team work skills. When the tasks have been completed, the learning - both practical and theory - is debriefed to the group as well as how it will translate back in your business. Not only are our events great fun, but they provide participants with learning points they can act upon to improve or enhance the working practices/environment. Team Building That Gets Results We have a highly innovative team who design team builds to suit all budgets and time or space restrictions. Large or small, we will develop the perfect event to meet your commercial objectives, keeping in line with your values and company culture. More recently we have combined team galvanising events with ways to engage the participants with and support their local communities. This solution has proved extremely popular with our clients and we are continuing to develop more and more programmes doing exactly this. " Phil did everything in a very professional and focused manner, without losing sight of the overall aims or having 'fun'. When I moved to Airbus UK and subsequently European Aeronautic Defense and Space Company (EADS), I had no hesitation in recommending Phil and the team to deliver the required training and team events.  Without doubt Phil and his team are excellent providers of training, to suit even bespoke requirements, and I would not hesitate in recommending the team to any business in the future. " Glenn Brown, Systems & Expertise Manager, Airbus Personnel Service Augmented Skills – an Essay by Phil Dickson All of you, who are reading this, and all the people you meet and work with will have – ‘Augmented Skills’. So – if you are an IT Engineer or a Pharmacist; perhaps you are, or know, a Departmental Leader and you work with a Logistics Project Manager; these roles will demand core skills, whether they be technical know-how or qualifications in the discipline. But to be that bit better; more reliable; more effective & productive and therefore more valuable and, frankly, marketable – capitalizing on ‘Augment Skills’ comes into play. The I T Engineer who was a Chess Champion at Uni, which would indicate that they possess some key ‘Augmented skills’ including how they plan 3 steps ahead and are always prepared for the unexpected. The Pharmacist, who is a keen sportsperson in their private life, will likely be tenacious, team-orientated and disciplined – again these are superb qualities to have in this – or any – role. Your colleagues, as well as yourself, will have ‘Augmented Skills’ that will be an asset if only they are explored and applied to their role and indeed, career. Everyone has their own 'Super-power' If they love gardening, they are probably strategic, patient and inclined to research; if they cook or bake, they are usually well organized and comfortable with multi-tasking. A big reader will tend to be considered and possess good critical thinking faculties, and an amateur mechanic or keen DIY person will often be practical, resourceful and very determined. I have observed that many new Parents discover they have ‘Augmented Skills’ they didn’t know they had... such as getting order out of chaos and displaying industrial amounts of patience and good grace when they really do not feel like it. They very often become far more compassionate and empathetic. Most people have their very own ‘Superpower’. Invite your team members to offer their ‘Augmented Skills’ to your work-place – and just watch as it elevates the motivation levels and improves results. It’ll be very rewarding for all concerned – and for meeting the Team’s objectives, to encourage the person who is a talented artist to be a sounding board on some of the marketing imagery and layouts; for the team member who is great at Maths or resolving crosswords to be asked for their input to solving a problem that is causing logistical or operational headaches. Never exploit a Team member’s unique special skills at their expense I would like to stress, however, that it must never be an area where a team member gets exploited by harvesting their unique special skills to coerce them into taking on greater responsibilities and tasks without providing them with the commensurate salary and status. To do so would be immoral and, ultimately, counter-productive as it would lead to resentment and disenfranchisement. This is about encouraging people’s capability and inviting their input to boost confidence and enhance the team’s capability. Often, we need to be more than what our Job Description says It is also important to highlight that whatever a person’s role or function is – they will definitely need to have additional capabilities to be effective. The best example of this is when we designed and delivered a range of ‘Advanced Customer Care skills’ training sessions for the Met Office a few years ago…we met so many remarkably super-bright Meteorologists, many of whom were having to make significant adjustments to answering questions from Customers that seemed to be illogical and often, obtuse. It wasn’t enough for these Meteorologists to be highly skilled at interpreting data and identifying patterns – they needed ‘Augmented skills’ to make that information accessible to members of the public (and Council workers and Air Traffic controllers and Shipping agents) and many other people, as to what that particular weather system was going to be like in their area and at what time. They have to know how to ‘de-jargonise’ the material and provide succinct, clear, and yet temperate, descriptions without ever appearing exasperated, impatient or judgmental in response to sometimes quite silly questions. For a highly trained scientist – that can be counter-intuitive. Being Philosophical... and a wee bit pretentious At the risk of being a little Philosophical (and probably a wee bit pretentious) – in my own role of Trainer – my core skills have to include – being a very good communicator, an active listener and have innovative and engaging ways to convert an idea, or a model, into practical application that my Delegates and Clients gain tangible benefits from. This is how it applies to me... I really enjoy composing short, light classical-style piano pieces. Now, to do this well, you need to be able to find a transition from one chord or melody to a different theme or key. It has to be worked out very carefully to have incremental transitions and pleasant-sounding developments as the piece unfolds. I think I have become better at this as I have honed my skills as a composer. But I have realized that these very same skills have ‘Augmented’ my ability to help a Manager, or a Team, move from a state of conflict; tension; disfunction; disenfranchisement; lack of confidence to a place that is more harmonious with far greater productivity. The very same process of careful listening, considering options, taking well-considered steps, having a creative, sometimes brave, move towards a resolution are at play in both Training and Piano Compositions! Scheduled Courses Unfortunately this course is not one that is currently scheduled as an open course, and is only available on an in-house basis. Please contact us for more information.

Does your child long to be Robin Hood, Merida, Katniss or Hawkeye? Now's their chance! At Kids' Archery Durham, children can learn all about the art of archery in a safe and fun way. They can work towards badges and certificates, develop the confidence that comes from learning a new skill, be active, make friends and most importantly - have fun!

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

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