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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 Curriculum: Dental Hygienist Module 01: Introduction to Dental Hygiene Module 02: Benefits of Personal Dental Care Module 03: Dental Anatomy Module 04: Methods of Oral Care Module 05: Dental and Oral Diseases Module 06: Diet and Dental Health Module 07: Paediatric Dental Care Module 08: Herbal Oral Care Module 09: Dental Check-Up Module 10: Dentures Life Coaching Module 01: Life Coaching: The Purpose and Fundamentals Module 02: Develop Your Self-Awareness Module 03: The Development of Core Beliefs and Managing Fears Module 04: Life Coaching Types and Key Attributes of a Life Coach Module 05: Understanding Emotional Intelligence Module 06: Body Language and Communication Techniques Module 07: A Structure for Client Work and Goal Setting Module 08: Basic Principles for Business Development and Growth Module 09: The Ethical Coach: Protect Yourself and Your Clients Accreditation All of our courses are fully accredited, including this Dental Hygienist Training Course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in Dental Hygienist . 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In recent years, there has been an increase in drug, alcohol, and substance misuse in the United Kingdom. Drugs may destroy people's lives, and some of the most commonly used drugs, such as cocaine, cannabis, LSD, and heroin, lead to addiction that users cannot overcome. Managers in any workplace are uniquely placed to detect early signs of drug or alcohol abuse and put structures in place to minimise the damaging health, social, and economic effects of substance misuse in the UK. Our Drug and Alcohol Awareness Training course teaches learners how to support employees and service users struggling with substance abuse. It also raises awareness of the size and scale of substance misuse in the UK, the most commonly abused drugs, and recognising warning signs of a problem. The Drug and Alcohol Awareness Training course also focuses on how managers and organisations can protect the interests of the business and the people for whom they have a duty of care. Why choose this course from the School of Health Care? Self-paced course, access available from anywhere in the world. High-quality study materials that are easy to understand. Course developed by industry experts. After each module, there will be an MCQ quiz to assess your learning. Assessment results are generated automatically and instantly. 24/7 support is available via live chat, phone call, or email. Free PDF certificate after completing the course. Learning Objectives After completing this Drug and Alcohol Awareness Training course, the learner will be able to: Understand the fundamentals of substance misuse. Demonstrate a thorough understanding of the different types of illegal drugs. Understand the legal, health, and social implications of drug and alcohol abuse. Understand how organisations and individuals are expected to handle substance misuse issues. Demonstrate an understanding of the statistics on drug and alcohol abuse in the UK. Describe the various types of drug treatment services available in the UK. Plan a drug intervention and understand when someone requires assistance. Course Curriculum Module 1: Introduction Module 2: Historical Overview Module 3: Nature of Addiction Module 4: Policy Initiatives and Strategy in Alcohol and Drug Use Module 5: Understanding Alcohol Abuse Module 6: Drug Addiction & Abuse Certificate of Completion You will receive a course completion certificate for free as soon as you complete the Drug and Alcohol Awareness Training course. Who is this course for? This Drug and Alcohol Awareness Training course is ideal for those working in high-risk industries like hospitality and construction and anyone who manages or works with vulnerable people like adolescents. Requirements There are no particular requirements for this Drug and Alcohol Awareness Training course because it doesn't call for any specialized training. To enroll in this course, students must fulfill the following requirements: Good Command over English language Be energetic and self-motivated Basic computer Skill You must be at least 15 years old Career path This Drug and Alcohol Awareness Training Course will lead to a variety of career opportunities, some of which are listed below: Cleaner: £9.00 per hour Custodian: £12.00 per hour Housekeeper: £12.00 per hour Janitor: £10.00 per hour

Understanding domestic violence and abuse goes far beyond statistics—it calls for clear insight, critical reflection, and meaningful awareness. This course offers a structured approach to exploring the many forms abuse can take, from physical and emotional harm to coercive control and economic manipulation. Designed for learners seeking a deeper understanding of this sensitive subject, the content is thorough yet accessible, making it suitable for both personal interest and professional development. We take a careful, respectful tone throughout, placing emphasis on the realities victims face and how different support systems operate. Whether you're involved in support services, working in education, or simply seeking to learn, this course provides the knowledge needed to recognise warning signs, respond appropriately, and gain confidence in your understanding. With expert-written materials, case reflections, and research-backed insights, this online learning experience addresses the heart of the issue—helping you gain clarity without the clichés. Still not convinced? Take a glimpse of the key benefits you'll get with - Lessons prepared in collaboration with Professionals User-friendly Learner Management System (LMS) Syllabus designed in line with the standards of the UK Education System Updated Study Materials focusing both on Knowledge and Skill based training Guidance to expand in 'a' territory and move forward with Domestic Violence Officer profession Free Assessment to test learners' knowledge and skills Accredited learning and widely recognised certificate from reputed CPD Accreditation Bodies Course Curriculum: Module 01: Introduction to Domestic Violence & Abuse Module 02: The Components of Domestic Violence Module 03: The Impact of Domestic Violence on Victims Module 04: The Impact of Domestic Violence and Abuse on Children Module 05: Assessing the Risks of Domestic Violence and Abuses on Children Module 06: Supporting the Victim and Reporting Incidents Module 07: Record Keeping Course Assessment You will immediately be given access to a specifically crafted MCQ test upon completing the Domestic Violence and Abuse Awareness Diploma - CPD Accredited course. For each test, the pass mark will be set to 60%. Accredited Certificate After successfully completing this Domestic Violence and Abuse Awareness Diploma - CPD Accredited course, you will qualify for the CPD Quality Standards (CPD QS) certified certificate from Training Express. CPD 10 CPD hours / points Accredited by The CPD Quality Standards Who is this course for? The Domestic Violence and Abuse Awareness Diploma - CPD Accredited training is ideal for highly motivated individuals or teams who want to enhance their skills and efficiently skilled employees. Requirements There are no formal entry requirements for the course, with enrollment open to anyone! Career path Learn the essential skills and knowledge you need to excel in your professional life with the help & guidance from our Domestic Violence and Abuse Awareness Diploma - CPD Accredited training.