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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

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

Facilities management professionals are responsible for services that support business. Their roles can cover management of a wide range of areas including health and safety, risk management, business continuity, procurement, sustainability, space planning, energy, property and asset management. They are typically responsible for activities such as catering, cleaning, building maintenance, environmental services, security and reception. The Level 5 qualifications will provide the skills needed to complete complex tasks and take on responsibility for planning and delivering solutions while developing your specialised knowledge of the profession. It is for you if you are at a middle or senior management level and responsible for more complex functions, or if you are aspiring to these roles and looking to stretch and develop.

Sales Overview: Traffic Psychology and Human Factors Are you looking to delve into the intricate dynamics of traffic psychology and human factors to enhance road safety and optimize transportation systems? Look no further than our comprehensive module package titled 'Traffic Psychology and Human Factors.' Tailored for professionals, researchers, and enthusiasts alike, this series offers profound insights into the behavioral, cognitive, and environmental aspects shaping our roadways.   1. Introduction to Traffic Psychology and Human Factors: Begin your journey by unraveling the core principles and theories underpinning traffic psychology and human factors. Explore the intricate interplay between human behavior, cognition, and the built environment, setting a solid foundation for deeper exploration. 2. Driver Perception and Attention: Delve into the fascinating world of driver perception and attention. Uncover how individuals process and interpret visual information while navigating complex traffic scenarios. Learn strategies to optimize attentional resources and mitigate the risks associated with attentional lapses. 3. Driver Cognition and Decision Making: Embark on an exploration of the cognitive processes driving human decision-making behind the wheel. Analyze how drivers perceive risks, evaluate alternatives, and execute actions in real-time situations. Gain insights into cognitive biases and heuristics influencing driver behavior. 4. Driver Behaviour and Performance: Dive into the realm of driver behavior and performance, where psychology meets practical application. Examine factors influencing driver performance, including stress, fatigue, and emotions. Discover techniques to enhance driver behavior through training, intervention, and design. 5. Road User Behaviour: Expand your understanding beyond drivers to encompass the diverse array of road users. Investigate the behaviors and interactions among pedestrians, cyclists, and motorists. Gain valuable perspectives on shared spaces, conflict resolution, and inclusive design principles. 6. Traffic Psychology and the Environment: Explore the symbiotic relationship between traffic psychology and the environment. Assess how environmental factors, such as infrastructure design and urban planning, shape human behavior and traffic patterns. Unearth strategies for creating safer, more sustainable transportation systems.   Unlock the potential to revolutionize road safety and transportation efficiency with 'Traffic Psychology and Human Factors.' Whether you're an aspiring researcher, transportation planner, or safety advocate, this comprehensive module package equips you with the knowledge and tools needed to navigate the complex landscape of human behavior on the road. Accelerate your understanding, transform your approach, and pave the way towards safer, smarter transportation solutions. Course Curriculum Module 1: Introduction to Traffic Psychology and Human Factors Introduction to Traffic Psychology and Human Factors 00:00 Module 2: Driver Perception and Attention Driver Perception and Attention 00:00 Module 3: Driver Cognition and Decision Making Driver Cognition and Decision Making 00:00 Module 4: Driver Behaviour and Performance Driver Behaviour and Performance 00:00 Module 5: Road User Behaviour Road User Behaviour 00:00 Module 6: Traffic Psychology and the Environment Traffic Psychology and the Environment 00:00

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14 in 1 "IT and Analytics Training Training" Bundle only at £ 79 Get Hard Copy + PDF Certificates + Transcript + Student ID Card + e-Learning App as a Gift - Enrol Now Tired of browsing and searching for a IT and Analytics Training course you are looking for? Can't find the complete Training package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this 14-in-1 extensive IT and Analytics Training bundle that has everything you need to succeed in IT and Analytics Training and other relevant fields! After surveying thousands of learners just like you and considering their valuable feedback, this all-in-one Training bundle has been designed by industry experts. We prioritised what learners were looking for in a complete IT and Analytics Training package and developed this in-demand Training course that will enhance your skills and prepare you for the competitive job market. Furthermore, to help you showcase your expertise in this Training, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These Training certificates will enhance your credibility and encourage possible employers to pick you over the rest. This Bundle Consists of the following Premium courses: Course 01: Introduction to Data Analysis Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step Course 12: IT For Recruiters Enrol now to advance your career, and use the premium study materials from Apex Learning. Benefits you'll get from choosing Apex Learning for this training: Pay once and get lifetime access to 14 CPD courses Free e-Learning App for engaging reading materials & helpful assistance Certificates, student ID included in a one-time fee Free up your time - don't waste time and money travelling for classes Accessible, informative modules designed by expert instructors Learn anytime, from anywhere Study from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (For The Title Course) Hard Copy Certificate: Free (For The Title Course) Curriculum Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding --------Other Courses Are-------- Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step Course 12: IT For Recruiters CPD 130 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this IT and Analytics Training bundle including- Students Graduates Job Seekers Job Holders Requirements Our Training is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. This Training course has been designed to be fully compatible with tablets and smartphones, so you can access your course on Wi-Fi, 3G or 4G. Career path Having this Training expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Prepare for the AWS Certified Solutions Architect - Associate (SAA-C03) exam. Learn about the AWS Management Console, S3 buckets, instances, database services, cloud security, costs associated with AWS, Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS).

Description Do you need to lead one-on-one meetings in your workplace? If you do, then you will know it is your responsibility to make them comfortable for both of you, and also an effective use of time. One-on-One Meetings Serve Many Purposes They may be for Goal setting Performance review - formal and informal Tasking and progress monitoring Briefing Problem-solving and addressing issues Care and support Guidance Coaching or mentoring Performance feedback ...Even reprimands   But often, people get promoted to a level where they need to lead these kinds of meeting, without getting any training. So, in this course, we'll cover: The different types of one-on-one meeting Planning and preparing your meeting Putting together an effective agenda How to build rapport How men and women each perceive a mixed meeting - with advice for each How to listen well Questioning skills How to adapt to the range of meeting types you'll need as a manager, project manager, or team leader.   As a result, you'll be able to: Select the most effective style for your one-on-one meeting Plan and prepare for an effective meeting that puts both of you at ease, and allows you to make the progress you need Lead a meeting that flows naturally Choose the most suitable room layout for your meeting Build and strengthen professional rapport with the other person Avoid the traps of the male-female one-on-one dynamic Listen deeply, and ask insightful questions Lead a range of different types of one-on-one meeting, for: appraisal, progress checking, coaching, mentoring and reprimands.   Prior Experience The only prior experience you need is some exposure to organizational life. However, the people who may find this most valuable are those who have tried leading their first one-on-one meetings and so know what challenges you want to work on..   Who is this course for? Supervisors who need to guide and support colleagues Managers who need to set expectations, and monitor performance Professionals who need to meet with other professionals and workplace contacts Leaders who want to coach, mentor, and give feedback to colleagues ... anyone in the world of work who has to take the lead in a one-on-one meeting   Your Tutor: Dr Mike Clayton Your tutor is Dr. Mike Clayton. As a senior manager at international consulting firm, Deloitte, Mike had to deal with conflict among staff, and with clients, and even among clients. But more important, as a senior representative of his firm and a program leader, he needed to avoid escalation of conflict. Now, as a trainer and facilitator, he has been applying what he learned and teaching it to others. This course represents 15 years' experience of what tools students find most helpful, and 25 years experience in business, managing conflicts large and small. Mike is supported by Felicity Dwyer Felicity is an experienced management trainer and coach, whose Institute of Leadership and Management accredited courses have a justifiably high reputation across the UK. What Will I Learn? Select the most effective style for your one-on-one meeting Plan and prepare for an effective meeting that puts both of you at ease, and allows you to make the progress you need Lead a meeting that flows naturally Choose the most suitable room layout for your meeting Build and strengthen professional rapport with the other person Avoid the traps of the male-female one-on-one dynamic Listen deeply, and ask insightful questions Lead a range of different types of one-on-one meeting, for: appraisal, progress checking, coaching, mentoring and reprimands. Who is the target audience? Supervisors who need to guide and support colleagues Managers who need to set expectations, and monitor performance Professionals who need to meet with other professionals and workplace contacts Leaders who want to coach, mentor, and give feedback to colleagues ... anyone in the world of work who has to take the lead in a one-on-one meeting Requirements You should be a professional, manager, or supervisor who leads one-on-one workplace meetings ...or be expecting to lead them in the near future. Welcome and Introduction Welcome to the Course 00:03:00 Why One-on-One Meetings are so Important 00:03:00 Reasons to Meet 00:03:00 Meetings are Conversations: Types of Conversation 00:04:00 Preparation and Planning for Your One-onOne Meeting Planning your One-on-One Meetings 00:04:00 How to Prepare for Your Meeting 00:02:00 Preparing your Agenda 00:05:00 Running Your One-on-One Meeting The Five Stages of a Meeting 00:04:00 Meeting Room Layout 00:06:00 Rapport Building Skills 00:04:00 The Male-Female Dynamic 00:01:00 The Male-Female Dynamic - from His Perspective 00:05:00 The Male-Female Dynamic - from Her Perspective 00:02:00 Questioning Skills 00:08:00 Listening Skills 00:06:00 Particular Types of One-on-One Meeting Staff-focused One-on-One Meetings 00:05:00 Checkpoint Meetings 00:04:00 Informal Feedback Meeting 00:04:00 Formal Appraisal Meetings 00:06:00 Difficult Conversations 00:04:00 Coaching Meetings 00:06:00 Mentoring Meetings 00:04:00 Giving a Reprimand 00:03:00 Closing Last Thoughts 00:02:00

Level 5 QLS Endorsed | 150 CPD Points| +Gifts: QLS Certificate + PDF Certificate | Lifetime Access| 24/7 Learner Support