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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
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Course Description Pick up effective leadership and management skills from the Leadership and Management: Part 2 (Skills) course. This course will teach you valuable organisational skills. You will be able to elevate your communication abilities through the course. It will provide you with the key techniques of talent management. You will become competent in inspiring the employees. The Leadership and Management: Part 2 (Skills) course will show you the tips and tricks of negotiation. Join the course to master the skills of leadership and management. Learning outcome Gain effective organisational skills Build excellent skills for communication Learn the process of talent management Grasp valuable negotiation strategies How Much Do Leaders Earn? Senior - £44,000(Appx.) Average - £26,000(Appx.) Starting - £19,000(Appx.) Requirement Our Leadership and Management: Part 2 (Skills) is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Leadership and Management: Part 2 (Skills) Module 01: Organisational Skills 00:26:00 Module 02: Communication Skills 00:27:00 Module 03: Talent Management 00:33:00 Module 04: Motivating Employees 00:26:00 Module 05: Negotiation Techniques 00:15:00 Assignment Assignment - Leadership and Management: Part 2 (Skills) 00:00:00
About this Training Course Geomechanical evaluations are about the assessment of deformations and failure in the subsurface due to oil & gas production, geothermal operations, CO2 storage and other operations. All geomechanical evaluations include four types of modelling assumptions, which will be systematically addressed in this training, namely: 1. Geometrical modelling assumption: Impact of structural styles on initial stress and stress redistribution due to operations 2. Formation (or constitutive) behaviour: Linear elastic and non-linear behaviour, associated models and their parameters, and methods how to constrain these using 3. Initial stress: Relation with structural setting and methods to quantify the in-situ stress condition 4. Loading conditions: Changes in pore pressure and temperature on wellbore and field scale This 5 full-day course starts with the determination of the stresses in the earth, the impact of different structural styles, salt bodies, faulting and folding on the orientation of the three main principal stress components. Different (field) data sources will be discussed to constrain their magnitude, while exercises will be made to gain hands-on experience. Subsequently, the concepts of stress and strain will be discussed, linear elasticity, total and effective stress and poro-elasticity in 1D, 2D and 3D, as well as thermal expansion. Participants will be able to construct and interpret a Mohr-circles. Also, different failure mechanisms and associated models (plastic, viscous) will be discussed. All these concepts apply on a material point level. Next, geomechanics on the wellbore scale is addressed, starting with the stress distribution around the wellbore (Kirsch equations). The impact of mudweight on shear and tensile failure (fracturing) will be calculated, and participants will be able to determine the mudweight window stable drilling operations, while considering well deviation and the use of oil-based and water-based muds (pore pressure penetration). Fracturing conditions and fracture propagation will be addressed. Field-scale geomechanics is addressed on the fourth day, focussing on building a 3D geomechanical model that is fit-for-purpose (focussing on the risks that need evaluation). Here, geological interpretation (layering), initial stress and formation property estimation (from petrophysical logs and lab experiments) as well as determining the loading conditions come together. The course is concluded with interpretation of the field-wide geomechanical response to reservoir depletion with special attention to reservoir compaction & subsidence, well failure and fault reactivation & induced seismicity. Special attention is paid to uncertainties and formulating advice that impacts decision-making during development and production stages of a project. This course can also be offered through Virtual Instructor Led Training (VILT) format. Training Objectives Upon completing of this course, the participants will be able to: Identify potential project risks that may need a geomechanical evaluation Construct a pressure-depth plot based on available field data (density logs, (X)LOT, FIT, RFT) Employ log-based correlation function to estimate mechanical properties Produce a simplified, but appropriate geometrical (layered, upscaled) model that honours contrasts in initial stress, formation properties and loading conditions, including Construct and interpret a Mohr-circle for shear and tensile failure Calculate the mud weight that leads to shear and tensile failure (fracturing conditions) Identify potential lab experiments to measure required formation properties Describe the workflow and data to develop a field-wide fit-for-purpose geomechanical model Discuss the qualitative impact of pressure and temperature change on the risk related to compaction, well failure, top-seal integrity and fault reactivation Target Audience This course is intended for Drilling Engineers, Well Engineers, Production Technologists, Completion Engineers, Well Superintendents, Directional Drillers, Wellsite Supervisors and others, who wish to further their understanding of rock mechanics and its application to drilling and completion. There is no specific formal pre-requisite for this course. However, the participants are requested to have been exposed to drilling, completions and production operations in their positions and to have a recommended minimum of 3 years of field experience. Course Level Intermediate Trainer Your expert course leader has over 30 years of experience in the Oil & Gas industry, covering all geomechanical issues in the petroleum industry for Shell. Some of his projects included doing research and providing operational advice in wellbore stability, sand failure prediction, and oil-shale retortion among others. He guided multi-disciplinary teams in compaction & subsidence, top-seal integrity, fault reactivation, induced-seismicity and containment. He was also involved in projects related to Carbon Capture Storage (CCS). He is the founding father of various innovations and assessment tools, and developed new insights into the root causes seismicity induced by Oil & Gas production. Furthermore, he was the regional coordinator for technology deployment in Africa, and Smart Fields (DOFF, iField) design advisor for Shell globally. He was responsible for the Geomechanical competence framework, and associated virtual and classroom training programme in Shell for the last 10 years. He served as one of the Subject Matter Expert (SME) on geomechanics, provided Technical Assurance to many risk assessments, and is a co-author of Shell's global minimun standard on top-seal integry and containment. He has a MSc and PhD in Civil Engineering and computational mechanics from Delft University of Technology, The Netherlands. Training experience: Developed and delivered the following (between 2010 and 2020): The competence framework for the global geomechanical discipline in Shell Online Geomechanical training programs for petroleum engineers (post-doc level) The global minimum standard for top-seal integrity assessment in Shell Over 50 learning nuggets with Subject Matter Experts Various Shell virtual Geomechanical training courses covering all subjects Developed Advanced Geomechanical training program for experienced staff in Shell Coaching of KPC staff on Geomechanics and containment issues on an internship at Shell in The Netherlands, Q4 2014 Lectured at the Utrecht University summer school (The Netherlands, 2020) on induced seismicity among renowned earthquake experts (Prof. Mark Zoback, Prof. Jean-Philippe Avouac, Prof. Jean-Pierre Ampuero and Prof. Torsten Dahm) (https://www.nwo.nl/onderzoeksprogrammas/deepnl/bijeenkomsten/6-10-juli-2020-deepnl-webinar-series-induced-seismicity) Lectured at the Danish Technical University summer school (Copenhagen, 2021) summer school on Carbon Capture and Storage (https://www.oilgas.dtu.dk/english/Events/DHRTC-Summer-School) Virtual Carbon Capture and Storage (CCS): Project Risks & How to Manage Them training course (October and November 2021) 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