2520 Evaluation courses in Coventry delivered Online

Who is this course suitable for? Required to undertake asbestos fibre counting as part of their work Considering a career in asbestos analysis Responsible for managing asbestos analysts Prior Knowledge and Understanding Candidates for this course are expected to be aware of HSG 248 Asbestos: The Analysts' Guide (July 2021), and in particular Appendix 1, Fibres in air: sampling and evaluation of by phase contrast microscopy. Candidates will preferably have prior experience of analysing fibre count samples and may already be participating in a quality control scheme. In addition, candidates are expected to have had training to cover the core competencies outlined within the foundation material detailed within Table A9.1 of HSG248 Asbestos: The Analysts' Guide (July 2021). This may be achieved by In -house learning or through the P400 foundation module.

About this training Mature fields differ from green field developments in that major infrastructure is in place, static reservoir data has accumulated from development drilling and a growing volume of production and processing performance data has become available. Decisions therefore relate to incremental projects, which may be small in scope and are often economically marginal. A firm understanding of the technical fundamentals associated with reservoir, wells and surface facilities is therefore required to make quality decisions in this environment, supported by realistic uncertainty ranges, and consistent application of incremental project economics and risk analysis. Various strategies may be considered to manage the mature asset, from harvest to divest, and the selected incremental activities should support a clear chosen strategy. Training Objectives Upon completion of this course, participants will be able to: Characterize the overall challenges associated with mature field developments Evaluate critical insights from subsurface data and apply this to modelling options and recovery methods Assess associated well data, typical late life issues and drilling and completion options for mature developments Manage the role of risk and uncertainty when making mature field development planning decisions Prepare a strategy and implementation plan Target Audience The course is intended for individuals who play a part in evaluating, screening and maturing oil and gas field development opportunities. The following personnel will benefit from the knowledge shared in this course: Petroleum engineers Geoscientist Facilities engineers Commercial staffs Reservoir engineer Production engineer Drilling engineer Project manager Asset manager Field engineer Exploration manager Course Level Basic or Foundation Trainer Your expert course leader, boasts nearly four decades of experience in the upstream oil & gas industry. He began his career in the back in 1982, spending 13 years with Shell International across several global locations. During his tenure, he served primarily as a reservoir engineer, contributing to exploration prospect evaluation, field development planning, corporate business planning, and drilling operations. Throughout his career, he has executed a diverse range of reservoir engineering projects for multiple UK and international firms, and has successfully led several PE study teams. Furthermore, he has continuously provided reservoir engineering and commercial training to oil company staff on a national and international scale. 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

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

About this Training Energy insurance is a type of insurance designed to protect businesses that work in the energy industry. This type of insurance covers a wide range of risks that are unique to the energy industry, such as damage to oil rigs, power plants, pipelines, or other energy infrastructure, as well as accidents, explosions, fires, and environmental damage. Energy insurance can also provide coverage for business interruption caused by unforeseen events that can disrupt energy production or supply, such as natural disasters, equipment breakdown, and cyber-attacks. It may also include coverage for liability and loss of income resulting from lawsuits and legal claims. Training Objectives Upon completion of this course, participants will be able to: Understand the risk sharing between oil companies and contractors Know how this is dealt within the insurance products available Understand insurer's perception of risk Create awareness of how market insurance products meet industry needs Be familiar with insurer's pricing methodologies Better understanding of the broker interface Understand technical evaluation of the coverage wordings Putting technical knowledge into practice with claims workshop Target Audience The course is intended for individuals who work in the energy industry, particularly those who are involved in managing risk or making decisions related to insurance coverage. The following personnel will benefit from the knowledge shared in this course: Insurers Brokers Adjusters Lawyers Risk Managers Treasury Contracts Legals Contract Adjustor Project Managers Course Level Basic or Foundation Trainer Your expert course leader has worked in the insurance sector for 59 years. He has worked as a broker for reputable firms, such as Marsh, where he served as the managing director of Energy Construction. He has also participated in peer review for different Lloyds Syndicates. He also served as a broker for Sedgwick, AAA, and Miller in the offshore energy sector. He has helped businesses including Shell, BP, Chevron, ConocoPhillips, Petrofina, Woodside, ENI, and Brunei Shell for their policy reviews during his career. 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

This Level 3 Award in Education and Training is accredited by NCFE and regulated by Ofqual. The National Council for Educational Awarding (NCFE) is a national educational awarding body that is well-known and respected throughout the world, which will improve your prospects of finding employment and showcase your professional growth. Course Curriculum Course Overview Course Overview - Level 3 Award in Education and Training 00:00:00 Lesson 1 - Roles and Responsibilities of Teachers Lesson 1 - Roles and Responsibilities of Teachers 00:05:00 Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching Lesson 2 - Legislation, Regulatory Requirements and Codes of Practice in Teaching 00:11:00 Lesson 3 - Factors Contributing to Effective Learning Lesson 3 - Factors Contributing to Effective Learning 00:13:00 Lesson 4 - Identifying Needs Lesson 4 - Identifying Needs 00:12:00 Lesson 5 - Planning in Teaching and Learning Lesson 5 - Planning in Teaching and Learning 00:11:00 Lesson 6 - Augmenting the Learning Process Lesson 6 - Augmenting the Learning Process 00:10:00 Lesson 7 - The Assessment Approach to Learning Lesson 7 - The Assessment Approach to Learning 00:13:00 Lesson 8 - The Evaluation Process in Learning Lesson 8 - The Evaluation Process in Learning 00:12:00 Lesson 9 - Learning Effective Teaching Microteaching Lesson 9 - Learning Effective Teaching Microteaching 00:10:00 Additional Resource Additional Resource - Level 3 Award in Education and Training 00:00:00 Assignment - Mandatory Units Assignment 1: Understanding Roles, Responsibilities and Relationships in Education and Training Assignment 1 - Understanding Roles, Responsibilities and Relationships in Education and Training 00:14:00 Assignment - Optional Units Assignment 2: Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training Assignment 2 - Understanding and Using Inclusive Teaching and Learning Approaches in Education and Training 00:12:00 Assignment 3: Understanding the Principles and Practices of Assessment Assignment 3 - Understanding the Principles and Practices of Assessment 00:07:00 Feedback Feedback 00:00:00

About this Virtual Instructor Led Training (VILT)  This 4 half-day Virtual Instructor Led Training (VILT) course will address a variety of contract and loan structuring issues associated with geothermal energy projects as well as comparison with solar, wind and battery storage. The course is designed to investigate how various project finance techniques and contract structures can be used to achieve a competitive power prices while maintaining a satisfactory equity return. Distinctive project finance features of power facilities that depend on geothermal, wind, hydro or solar resources will be evaluated with financial models. The course will cover economic analysis of exploration and development of geothermal facilities and how to incorporate probability of failure and success into an IRR framework. Subsequent sessions will address the theory underlying liquidated damages for delay, and performance as well as design of other incentives that is inherent in different contract structures. Nuanced project finance issues associated with structuring debt for renewable projects will be discussed including under what conditions the DSCR drives debt capacity and when the debt to capital ratio is instrumental. The course will be taught with a combination of theoretical discussions, term sheet review and focused financial models. Training Objectives Evaluation of the economic risks that arise from uncertainty associated with drilling exploration wells and development wells for geothermal projects. Analyse the theoretical issues with computing LCOE for geothermal projects compared to other renewable and non-renewable resources and the importance of cost of capital for renewable projects; Understand differences in contract structures for renewable projects and dispatchable projects and how a single price structure can distort incentives for efficient construction and operation; Understand components of financing that influence the bid price required to meet a required rate of return on equity and can result in relatively low prices with reasonable returns. Understand the importance of debt sizing constraints and what strategies are relevant when the debt to capital constraint applies relative to when the debt service coverage ratio drives the debt size; Understand how to compute P50, P90 and P99 for different projects driven by resource risk; Understand the difference between mean reverting resource variation and estimation mistakes that do not correct as the basis for 1-year P90 and 10-year P90. Understand under what conditions debt sculpting can affect returns and how synthetic sculpting can be used to increase returns when the DSCR constraint applies. Understand the theory of credit spreads, variable rate debt and interest rates in different currencies and compute the implied probability of default that in inherent in credit spreads. Understand how to evaluate the costs to equity investors and the benefits to lenders for various credit enhancements including DSRA accounts, cash flow sweeps and covenants. Course Level Basic or Foundation Training Methods The VILT will be delivered online in 4 sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day, including time for lectures, discussion, quizzes and short classroom exercises. Trainer Your expert course leader provides financial and economic consulting services to a variety of clients, he teaches professional development courses in an assortment of modelling topics (project finance, M&A, and energy). He is passionate about teaching in Africa, South America, Asia and Europe. Many of the unique analytical concepts and modelling techniques he has developed have arisen from discussion with participants in his courses. He has taught customized courses for MIT's Sloan Business School, Bank Paribas, Shell Oil, Society General, General Electric, HSBC, GDF Suez, Citibank, CIMB, Lind Lakers, Saudi Aramco and many other energy and industrial clients. His consulting activities include developing complex project finance, corporate and simulation models, providing expert testimony on financial and economic issues before energy regulatory agencies, and advisory services to support merger and acquisition projects. Our key course expert has written a textbook titled Corporate and Project Finance Modelling, Theory and Practice published by Wiley Finance. The book introduces unique modelling techniques that address many complex issues that are not typically used by even the most experienced financial analysts. For example, it describes how to build user-defined functions to solve circular logic without cumbersome copy and paste macros; how to write function that derives the ratio of EV/EBITDA accounting for asset life, historical growth, taxes, return on investment, and cost of capital; and how to efficiently solve many project finance issues related to debt structuring. He is in the process of writing a second book that describes a series of valuation and analytical mistakes made in finance. This book uses many case studies from Harvard Business School that were thought to represent effective business strategies and later turned into valuation nightmares. Over the course of his career our key course expert has been involved in formulating significant government policy related to electricity deregulation; he has prepared models and analyses for many clients around the world; he has evaluated energy purchasing decisions for many corporations; and, he has provided advice on corporate strategy. His projects include development of a biomass plant, analysis and advisory work for purchase of electricity generation, distribution and transmission assets by the City of Chicago, formulation of rate policy for major metro systems and street lighting networks, advocacy testimony on behalf of low income consumers, risk analysis for toll roads, and evaluation of solar and wind projects. He has constructed many advisory analyses for project finance and merger and acquisition transactions. Lastly, our key course expert was formerly Vice President at the First National Bank of Chicago where he directed analysis of energy loans and also created financial modelling techniques used in advisory projects. He received an MBA specializing in econometrics (with honours) from the University of Chicago and a BSc in Finance from the University of Illinois (with highest university honours). 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 about post training coaching support and fees applicable for this. Accreditions And Affliations

About this Training Course The 3-day hands-on petroleum economics training course provides a comprehensive overview of the practices of exploration and development petroleum economics and its application in valuing oil and gas assets to aid corporate decisions. Participants will gain a thorough understanding of the principles of economic analysis as well as practical instruction in analytical techniques used in the industry. The participants will learn how to construct economic models, to include basic fiscal terms, production and cost profiles and project timing. The resulting model will provide insights of how the various inputs affect value. Example exercises will be used throughout the course. Training Objectives Upon completion of this course, participants will be able to: Understand and construct petroleum industry cash flow projections Calculate, understand and know how to apply economic indicators Learn and apply risk analysis to exploration and production investments Evaluate and model fiscal/PSC terms of countries worldwide Target Audience The following oil & gas company personnel will benefit from the knowledge shared in this course: Geologists Explorationists Reservoir Engineers Project Accountants Contract Negotiators Financial Analysts New Venture Planners Economists Course Level Basic or Foundation Intermediate Trainer Your expert trainer has over 40 years' experience as a petroleum economist in the upstream oil and gas industry. He has presented over 230 oil and gas industry short courses worldwide on petroleum economics, risk, production sharing contracts (PSC) and fiscal analysis. In over 120 international oil industry consulting assignments, he has advised companies and governments in the Asia Pacific region on petroleum PSC and fiscal terms. He has prepared many independent valuations of petroleum properties and companies for acquisition and sale, as well as economics research reports on the oil and gas industry and including commercial support for oil field operations and investments worldwide. He has been involved in projects on petroleum royalties, design of petroleum fiscal terms, divestment of petroleum assets, and economic evaluation of assets and discoveries since the early 1990s to date. He has been working on training, consultancy, research and also advisory works in many countries including USA, UK, Denmark, Switzerland, Australia, New Zealand, Indonesia, India, Iran, Malaysia, Thailand, Vietnam, Brunei, Egypt, Libya, and South Africa. 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

Gain the clarity and confidence to take on inspections successfully with this actionable workshop.

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00