508 Courses

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

Within the course, you have the chance to complete a questionnaire focused on determining your preferences, which will enable you to gain some insights into your Type. As you progress through the course, you are able to check how clear you are with each preference to give you a deeper understanding of your own type.

How to inspire others and build successful relationships. This course is designed to support professionals in business to develop communication skills through understanding and applying knowledge of personality and behaviour in the workplace. It is based upon the book "The Authority Guide to Behaviour in Business: How to Inspire Others and Build Successful Relationships" by Robin Hills (ISBN: 1912300087).

This video course introduces you to the world of Rust programming. You'll learn about the Rust ecosystem, tools, primitive types, and control flow, and gain knowledge of how real-world applications are actually developed in Rust.

The medication and record keeping course is a full day, instructor led course in a fully equipped training room. We use a mixture of training methods such as scenarios, activities, group discussion, games and the use of equipment / technology to cover the different learning styles of the individual. Learners will develop their knowledge, skills and understanding around the administering of medication and the importance of record keeping. Some of the key Learning Outcomes we cover are; Understanding of the legislation surrounding medication Understanding of role and responsibilities Understanding of medication policy and procedures Understanding of boundaries when assisting with medication Explain how and why medication errors occur Explain how to eliminate or reduce risks to individuals safety Aware of who and where to report concerns Understand the importance of thorough documentation Care providers are regulated by the Care Quality Commission (CQC) and as a result must meet the Health and Social Care Act 2014 (Regulation 18 Staffing). Evidence of training and understanding is provided to support providers in their evidence of compliance. Instructions Attendees to arrive on time at 9.30am and will leave around 16.30pm It is important that learners are fit and well to participate in group activities. Directions Complete Care West Yorkshire Ltd. Somerset House, Sandal Castle Centre Asdale Road Wakefield WF2 7JE All training is carried out at our office (Somerset House, map is attached). Please note that we do not provide lunch so you will have to bring your own. Please do not park in the office car park as not all spaces belong to us. There is available parking in Asda and Aldi next door, or the public car park at eth side of Square Pizza Amenities Toilets

If you have no intention of removing asbestos but work on buildings built or refurbished before the year 2000, asbestos could be present. You will need awareness training so you know how to avoid the risks. Asbestos awareness training should be given to employees whose work could foreseeably disturb the fabric of a building and expose them to asbestos or who supervise or influence the work. In particular, it should be given to those workers in the refurbishment, maintenance and allied trades where it is foreseeable that ACMs may become exposed during their work.

2 day course  for more information please email training@humansafteymatters.co.uk 

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 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: 00:08:00 •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: 00:07:00 •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

LEARN HOW IRIDOLOGY CAN MAKE A HUGE CONTRIBUTION TO ANY COMPLEMENTARY PRACTICE A MESSAGE FROM THE AUTHOR I want to welcome you most warmly to the study of Iridology. Students of our course have taken their knowledge out into the world of practice and they have been able to see more penetratingly into the health of their patients. They have seen many truths about causes and effects in health and disease - that is what allows you to understand those extra things that make you into an even better healer. I think you are going to find this the most intriguing and absorbing study and, certainly, that is my sincere hope. As you precede, much of what you learn will amaze you and inspire wonder at the ways of the human body and mind. As you tread this very special road, I pass on to you the words that Bernard Jensen gave me years ago when I was his student, inscribed upon the inside cover of his book: “Seek the Higher Values in Life”. DR. LAWRENCE PLASKETT WHAT IS IRIDOLOGY? Iridology is the art of iris analysis. The iris is connected to the brain via the hypothalamus and can give naturopathic read outs on tissue conditions in various parts of the body. With training and practice it is possible to read signs indicative of biochemical, emotional and environmental influences that are hard to determine by other means. We can thus interpret health (and even aspects of personality) by close examination of the eyes, using suitable illumination and a magnifying glass. The close relationship between naturopathic iridology as an assessment tool and nutritional therapy and other naturopathic disciplines continues and grows closer. Now Iridology can make a huge contribution to complementary therapeutic practice and enhanced by our wonderful digital collection of eye photographs, the learning process with the Plaskett International College is a profound and exciting one. We teach Iridology quite separately from other topics and anyone who possesses, or expects to possess, a practitioner's qualification in any therapeutic discipline, may join the course. Course Duration 12 months Study Hours 200 hours Course Content 9 sections Course Fee £495 How Can Iridology Help Practitioners? Examples of how iridology can help practitioners Did you know that some iris features are so very plain that you can see them with the naked eye in ordinary social contact? You can see from two or three feet away in many cases that the person has a toxic digestive system (a strong wide dark ring around the pupil margin). You can often tell that the person has an overactive stomach (a narrow bright white ring very close to the pupil). You can tell when the skin is overlaid with toxins so that the skin's function in excreting toxins from the body is jeopardised (very narrow dark ring around the iris margin). You can tell in some people (rather advanced cases) that they suffer badly from sodium and potassium imbalance and have placed themselves at potential risk from cholesterol accumulation (the so-called corneal arcus, a white or off-white cloudy deposit, usually fairly thick, around the iris margin).  Another example is the ring of spots or 'tophi' represented by the lymphatic rosary. Its mere presence tells one that there is sluggishness in the lymphatic system. When these tophi are darkly pigmented, the situation gives rise to concern for the possible generation of lymphatic illness. Using the precise positioning of iris reflex areas contained on the iris chart, one may distinguish many key points of analysis. Areas of stress and tension can be pinpointed by identifying 'contraction furrows’. Past injuries and adhesions show themselves as contortions of the normally regular and even iris fibres. You can answer questions like:- Is it the pancreas or the liver that is responsible for the trouble? Is the patient's hypertension caused by a defect of or toxic deposits in the particular brain area that is geared to control blood pressure? One of Jensen's rather dramatic illustrations is of the iris of a man who has just been shot. It shows the precise areas of tissue damage within the body and the response is very fast. The number of potential examples is almost without limit. The above may suffice to show the types of things that iridology can do for practitioners. We hope it will help you decide to study Iridology with the Plaskett International College. Course Overview The course covers the nature of iris observation, the nutritive zone, the iris chart, the chronic and acute, the intestinal and stomach zones and nerve collarette, the constitution type, respiratory system, lacunae, open lacuna, inherent weaknesses, the organs of elimination, other organs, special signs, complete diagnosis of a subject. The treatment of the topic follows the principles of Bernard Jensen in the USA. Once the basics have been learnt, the course teachings then extend considerably by bringing in the work of Dorothy Hall and of Dr Josef Deck, both of which are the subject of a special presentation during the course. The published insights of Farida Sharan and Harri Wolf, while not separately presented, also influence the presentation of the course material. Both the Australian School, (Dorothy Hall) and the German School, (Dr Deck/Harri Wolf), offer an added dimension to the study and interpretation of the constitution. PERSONALITY ASPECTS & CONSTITUTIONAL TYPES The study focuses upon the different personality aspects, which show up in different constitutional types. Dorothy Hall gives insights into what contributes to various different types of personality and their emotional and mental responses and their pre-dispositions to health or disease. Different sorts of people can have different priorities, preferences and imperatives built into their very nature from or before birth, sometimes determining the course of their entire lives and their attitudes to the world and to other people. AN EMPATHY BETWEEN PATIENT & PRACTITIONER The course teaches an understanding of these types and facilitates an empathy between patient and practitioner. It shows how people of the differing constitutional types are likely to go out of balance either mentally or emotionally and how their vulnerability to various physical ailments varies. The German School offers a very exciting and precise approach to the constitutional types, which is really quite different, but no less helpful. It highlights variations in the susceptibility to diseases of different organs and systems. THE 3 SCHOOLS OF THOUGHT It is a prime purpose of this course, not only to teach these differing positions, but also to demonstrate how it is that all three of these major schools of Iridology embody different aspects of the truth, how each is individually valuable and how a full and deep understanding of the meaning of 'constitution' can be gained through a sympathetic synthesis of the contributions from all three of these schools. BREAKDOWN OF THE COURSE SECTIONS In total there are 9 sections comprising of text, videos and iris images to study: SECTION 1 GENERAL PRACTICE AND AN ACCOUNT OF THE NUTRITIVE ZONE Areas Covered Iris colour Information that iridology can give us The structure of the eye and the iris Using the iris as an assessment tool The principle of reflex areas The Nutritive Zone Abnormality in the colon The Collarette (autonomic nerve wreath or anw) Diagnosis of the constitution based upon fibre structure Studies on images of real eyes SECTION 2 FEATURES OF THE FIBRES OUTSIDE THE COLLARETTE Areas Covered The general layout of fibres outside the collarette Inherent weaknesses First stage in further deterioration of an inherent weakness The meaning of darkness in the iris The development of discrete – open lacunae Lacunae Further notes about lightness and darkness amongst the fibres Healing lines Crypts Round the iris chart – the left iris Round the iris chart – the right iris Checking which structures and inside and which outside the collarette The organ systems The neural arc reflex SECTION 3 SPECIAL SIGNS Areas covered The corneal arcus (sodium ring, cholesterol ring, lipemic ring) The tophi (also lymphatic tophi or lymphatic rosary) Corneal Arcus The anaemia sign The catarrhal sign Acidity Grey background Scurf rim Circulatory ring Sphincter muscle (also called pupillary sphincter) Pigments (topastible or topolabile) Psoric spots Contrcation furrows Radial furrows SECTION 4 THE CONSTITUTIONS IN RELATION TO PERSONALITY TYPE AND DISEASE DISPOSITION Areas covered Very resilient Resilient average Moderately resilient Mildly resilient SECTION 5 MORE ABOUT WHITE SIGNS Areas covered Revision of distinctions between the different white signs Pictures of irises with white signs, with commentaries Further interpretation of the corneal arcus Further interpretation of the lytophi More general interpretation of the colour white SECTION 6 COLOURS IN THE IRIS AND OTHER SPECIAL SIGNS Areas Covered Yellow pigment in the iris Orange pigment Brown pigment Contraction furrows Radial furrows Psoric spots Pupillary border The “friendly fibrils” sign Summary of remedies SECTION 7 THE CONSTITUTION AND SIGNS ACCORDING TO THE GERMAN SCHOOL Areas Covered The German school of iridology Our approach to teaching the German school Introduction to the German constitutional types The lymphatic constitutions Mixed biliary constitution or biliary constitution Haematogenic (or haematogenous) constitution The way to use information on the German constitutions New signs that are specific to the German school Treatment recommendations for constitutional types SECTION 8  ADVANCED STUDIES OF THE IRIS Areas Covered Further details of the iris chart – its layout and its implications Neural arc reflex Deformation of pupil shape and position Advanced study of fibre separations, sinuosity, injuries & adhesions Lacunae of different shape and appearance The b3 bulge and the pterygium Working with genetically brown eyes SECTION 9  THE CONSULTATION & THE PRACTICALITIES Areas Covered Diagnosing pathology of individual critical organs Personality interpretations based upon the German school Conducting an iridology consultation Practical aspects of iris examination Making drawings of the iris and recording the data The uses, advantages and limitations of iris photography and its place in iridology practice Equipments and techniques of iris photography Using the computer to store and process digital images The interaction of signs Interpreting the whole iris in conjunction with the case study Pointers to treatment Carrying out case histories TESTIMONIALS Here's what students have to say about the course Emma Rubio, Health Coach Spain "As a Health Coach I decided to pursue my studies with the Plaskett College to become a Nutritional Therapist. For that, I am also studying Iridology. I am happy to have a tutor to answer my doubts and I like the flexibility that the College offers me. I love the subject of Iridology and the way it is explained, I also like having some videos of Dr Plaskett teaching Iridology as I admire him." Dr Ezequiel Lafontaine, Iridologist Puerto Rico "I LOVE IRIDOLOGY. I have over 30 iridology books, Italian, French, German, Spanish and English, plus over 4,000 photos from my own practice. I took this course for a refresher course and found the material was second to none." Mrs D. Moothy, Nutritional Therapist Mauritius “The distance learning courses have given me the opportunity to pursue my dreams through a program that was not only flexible and convenient for my schedule, but was also challenging and rewarding. I thoroughly enjoyed the readings and the assignments but most importantly, I enjoyed being able to do things at my pace. I must say that the most exciting and challenging course was the Iridology Diploma, and I am happy that I was able to do well in all the courses."

This course is perfect for the beginner but also delves into building a SAS Model and intermediate topics. Learn SAS Data Step, SQL Step, Macros, SAS Model Building, Predictive Analytics, SAS and ML. If you are using SAS Enterprise Guide and want to learn how to code/program instead of using the point-and-click interface, this course is ideal!