72 Algorithms courses delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 5 Days 30 CPD hours This course is intended for This course is designed for people who are seeking to launch a career in cybersecurity. Overview Assess the security posture of an enterprise environment and recommend and implement appropriate security solutions; Monitor and secure hybrid environments, including cloud, mobile, and IoT; Operate with an awareness of applicable laws and policies, including principles of governance, risk, and compliance; Identify, analyze, and respond to security events and incidents. CompTIA Security+ is a global certification that validates the baseline skills necessary to perform core security functions and is the first security certification a candidate should earn. CompTIA Security+ establishes the core knowledge required of any cybersecurity role and provides a springboard to intermediate-level cybersecurity jobs. Lesson 1: Summarize Fundamental Security Concepts Security Concepts Security Controls Lesson 2: Compare Threat Types Threat Actors Attack Surfaces Social Engineering Lesson 3: Explain Cryptographic Solutions Cryptographic Algorithms Public Key Infrastructure Cryptographic Solutions Lesson 4: Implement Identity and Access Management Authentication Authorization Identity Management Lesson 5: Secure Enterprise Network Architecture Enterprise Network Architecture Network Security Appliances Secure Communications Lesson 6: Secure Cloud Network Architecture Cloud Infrastructure Embedded Systems and Zero Trust Architecture Lesson 7: Explain Resiliency and Site Security Concepts Asset Management Redundancy Strategies Physical Security Lesson 8: Explain Vulnerability Management Device and OS Vulnerabilities Application and Cloud Vulnerabilities Vulnerability Identification Methods Vulnerability Analysis and Remediation Lesson 9: Evaluate Network Security Capabilities Network Security Baselines Network Security Capability Enhancement Lesson 10: Assess Endpoint Security Capabilities Implement Endpoint Security Mobile Device Hardening Lesson 11: Enhance Application Security Capabilities Application Protocol Security Baselines Cloud and Web Application Security Concepts Lesson 12: Explain Incident Response and Monitoring Concepts Incident Response Digital Forensics Data Sources Alerting and Monitoring Tools Lesson 13: Analyze Indicators of Malicious Activity Malware Attack Indicators Physical and Network Attack Indicators Application Attack Indicators Lesson 14: Summarize Security Governance Concepts Policies, Standards, and Procedures Change Management Automation and Orchestration Lesson 15: Explain Risk Management Processes Risk Management Processes and Concepts Vendor Management Concepts Audits and Assessments Lesson 16: Summarize Data Protection and Compliance Concepts Data Classification and Compliance Personnel Policies Additional course details: Nexus Humans CompTIA Security Plus Certification (Exam SY0-601) 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 CompTIA Security Plus Certification (Exam SY0-601) 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 5 Days 30 CPD hours This course is intended for This course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of programming fundamentals and object-oriented programming concepts. They will typically be high school students, post-secondary school students, or career changers, with no prior programming experience. They might want to gain an understanding of the core programming fundamentals before moving on to more advanced courses such as Programming in C#. Overview Explain core programming fundamentals such as computer storage and processing. Explain computer number systems such as binary. Create and use variables and constants in programs. Explain how to create and use functions in a program. Create and use decisions structures in a computer program. Create and use repetition (loops) in a computer program. Explain pseudocode and its role in programming. Explain the basic computer data structures such as arrays, lists, stacks, and queues. Implement object-oriented programming concepts. Create and use classes in a computer program. Implement encapsulation, inheritance, and polymorphism. Describe the base class library (BCL) in the .NET Framework. Explain the application security concepts. Implement simple I/O in a computer program. Identify application errors and explain how to debug an application and handle errors. Identify the performance considerations for applications. In this 5-day course, students will learn the basics of computer programming through the use of Microsoft Visual Studio 2022 and the Visual C# and Visual Basic programming languages. The course assumes no prior programming experience and introduces the concepts needed to progress to the intermediate courses on programming, Programming in C#. The focus will be on core programming concepts such as computer storage, data types, decision structures, and repetition by using loops. The course also covers an introduction to object-oriented programming covering classes, encapsulation, inheritance, and polymorphism. Coverage is also included around exception handling, application security, performance, and memory management. 1 - Introduction to Core Programming Concepts Computer Data Storage and Processing Application Types Application Lifecycle Code Compilation 2 - Core Programming Language Concepts Syntax Data Types Variables and Constants 3 - Program Flow Introduction to Structured Programming Concepts Introduction to Branching Using Functions Using Decision Structures Introducing Repetition 4 - Algorithms and Data Structures Understand How to Write Pseudocode Algorithm Examples Introduction to Data Structures 5 - Error Handling and Debugging Introduction to Program Errors Introduction to Structured Error Handling Introduction to Debugging 6 - Introduction to Object-Oriented Programming Introduction to Complex Structures Introduction to Structs Introduction to Classes Introducing Encapsulation 7 - More Object-Oriented Programming Introduction to Inheritance Introduction to Polymorphism Introduction to .NET and the Base Class Library 8 - Introduction to Application Security Authentication and Authorization Code Permissions on Computers Introducing Code Signing 9 - Core I/O Programming Using Console I/O Using File I/O 10 - Application Performance and Memory Management Value Types vs Reference Types Converting Types The Garbage Collector Additional course details: Nexus Humans 55337 Introduction to Programming 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 55337 Introduction to Programming 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

GLA Tutors: Empowering Young Minds in Computer Coding At GLA Tutors, we are passionate about equipping children with the essential skills needed to thrive in today's digital world. Our tutoring website offers a comprehensive and engaging learning experience for children who are eager to explore the exciting world of computer coding. With our online tutoring services, we strive to make coding education accessible and convenient for children of all ages. Our team of expert tutors are highly skilled in teaching computer coding concepts in a fun and interactive manner. They have a deep understanding of various programming languages and frameworks, ensuring that students receive top-notch instruction tailored to their individual needs and skill levels. Our provision for tutoring computer coding to children is designed to foster creativity, problem-solving skills, and logical thinking. Through our carefully curated curriculum, we introduce young learners to the fundamentals of coding, including algorithms, variables, loops, conditionals, and more. We believe in a hands-on approach, allowing students to actively apply what they learn through practical coding exercises and projects. At GLA Tutors, we understand that each child has their own unique learning style and pace. That's why our tutors provide personalized attention to every student, offering guidance and support every step of the way. Whether your child is a beginner or has some coding experience, we have tailored programs to suit their specific needs and help them progress confidently.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

About this Training Course There are various kinds of geophysical data available. They are separated into seismic and non-seismic (multi-physics) data. Non-seismic or multi-physics data (which includes gravity, magnetics, electrical, electromagnetics, spectral etc - apart from providing complimentary information to seismic) is the main source of information for very shallow subsurface applications such as engineering, mapping pollution, archaeology, geothermal energy, and related areas. This 5 full-day blended course will focus specifically on seismic data which is the main method used in the Oil & Gas industry. In this blended course, participants will be equipped to understand that seismic data represents the movement of the surface, resulting from waves generated by a source, dynamite or vibrator which are reflected by changes in the subsurface rocks. The basic principles of acquisition and processing will be explained and insights into advanced methods, allowing a much more accurate interpretation of seismic data than previously considered possible, will also be provided. This blended course contains an introduction to Machine Learning and its important role in all aspects of seismic acquisition, processing, and interpretation. There is no need to know in detail how the algorithms work internally but it is necessary to know how to use them correctly to achieve optimum results. Training Objectives By attending this course, participants will be able to acquire the following: Obtain an understanding of the strengths and limitations of geophysical methods, specifically seismic, and the costs and risks involved, and how to reduce these. Be able to communicate more effectively with staff in other disciplines. Understand the potential applications of seismic data and know how to formulate the requirements needed for prospect and field evaluation. Gain an awareness of modern seismic technology. Apply the learning in a series of practical, illustrative exercises. Know what types of questions to ask to assess the necessary quality of a seismic project in its role in a sequence of E&P activities Target Audience The blended course is intended for non-geophysicists who have intensive interaction with geophysicists. But it may be of interest to those who want to know about the recent progress made in geophysics, leading to amazing imaging results, which could not be imagined a decade ago. The blended course will bring to the attention of the geologists, petrophysicists and reservoir/petroleum engineers an awareness of how the data they will work with is acquired and processed by the geophysicist. It will introduce the concepts that are of importance in geophysics and thus relevant for non-geophysicists to know and be able to communicate with geophysicists as well as formulate their requests. Course Level Intermediate Trainer Your expert course leader has degree in Geology (University of Leiden), a Master's degree in Theoretical Geophysics (University of Utrecht) and a PhD in Utrecht on 'Full wave theory and the structure of the lower mantle'. This involved forward modelling of P- and S-waves diffracted around the core-mantle boundary and comparison of the frequency-dependent attenuation of the signal with those obtained from major earthquakes observed at long offsets in the 'shadow zone' of the core. These observations were then translated into rock properties of the D' transition zone. After his PhD, he joined Shell Research in The Netherlands to develop methods to predict lithology and pore-fluid based on seismic, petrophysical and geological data. He subsequently worked for Shell in London to interpret seismic data from the Central North Sea Graben. As part of the Quantitative Interpretation assignment, he was also actively involved in managing, processing and interpreting Offshore Seismic Profiling experiments. After his return to The Netherlands, he headed a team for the development of 3D interpretation methods using multi-attribute statistical and pattern recognition analysis on workstations. After a period of Quality Assurance of 'Contractor' software for seismic processing, he became responsible for Geophysics in the Shell Learning Centre. During that period, he was also a part-time professor in Applied Geophysics at the University of Utrecht. From 2001 to 2005, he worked on the development of Potential Field Methods (Gravity, Magnetics) for detecting oil and gas. Finally, he became a champion on the use of EM methods and became involved in designing acquisition, processing and interpretation methods for Marine Controlled Source EM (CSEM) methods. After his retirement from Shell, he founded his own company, specialising in courses on acquisition, processing and interpretation of geophysical data (seismic, gravity, magnetic and electromagnetic data), providing courses to International and National energy companies. In the last couple of years, he became keenly interested in the use of Machine Learning in Geophysics. Apart from incorporating 'Artificial Intelligence' in his courses, he also developed a dedicated Machine Learning course for geophysics. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

1-2-1 bespoke training course

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.