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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

The IOSH Managing Safely® E-Learning course contains learning materials for the whole IOSH Managing Safely® course

This course is perfect for L&D professionals keen to develop their skills and drive change within their organisation and those who want the chance to advance their profession and demonstrate their experience. Course Overview Ready to shape your organisation’s working environment? Discover how to measure your company’s performance and culture, boost people’s confidence and organise different forms of learning across your organisation. Learn how to organise different forms of learning across your organisation to boost people’s confidence and supercharge their development. This course is perfect for L&D professionals keen to develop their skills and drive change within their organisation and those who want the chance to advance their profession and demonstrate their experience. After completing this qualification, you will receive a CIPD Associate Membership. As a result, your professional credibility will be established, your profile will be raised, and new work chances will become available. Is this qualification right for me? This qualification is perfect for you if you enjoy helping employees become the best they can be. You’ll expand your knowledge of L&D with a combination of core units and specialist areas and get an individual plan for your self-development. What will I learn? You’ll study 3 core units + 3 specialist units + the choice of 1 optional unit: Core units: • Organisational performance and culture in practice • Evidence-based practice • Professional behaviours and valuing people Specialist units: • Supporting self-directed and social learning • Learning and development design to create value • Facilitate personalised and performance focused learning Optional units: • Specialist employment law • Advances in digital learning and development • People management in an international context • Diversity and inclusion • Leadership and management development • Well-being at work Typical job titles: • L&D Business Partner or Manager • L&D Consultant • Organisation Design Specialist • Organisational Development Business Partner • Employee Experience Manager • L&D Designer DURATION 12-16 Months WHATS INCLUDED Course Material Case Study Experienced Lecturer Refreshments Certificate

The complete guide to creating your own Pose Estimation apps: Learn the full workflow and get up to speed with developing 5 AI apps

Customer Service Essentials 1 Day Training in Luton

Customer Service Essentials 1 Day Training in Dunfermline

Customer Service Essentials 1 Day Training in Cambridge

Are challenging people getting in the way of your happiness, well-being and success? You may find them at work, in social situations, you may live them or they may be members of your family? Even worse when you see them getting away with things; getting their way even though there is no sense to it; getting advantages over everyone else; and in many cases being rewarded for their abusive behaviour with promotion and other advantages! You probably would never want to behave the way they do. You don’t have it in you or you just couldn’t live with yourself if you behaved like that. Maybe your attempts to confront them have failed and you’ve ended up with egg on your face, with them having more opportunity to have a “dig” at you. As a result, you may have come to the conclusion that it’s the nasty people that get ahead, and someone like you just has to put up with this behaviour from others.

Master the art of online teaching with our comprehensive course. Gain practical skills and strategies to engage students, create dynamic online lessons, and effectively utilize digital tools. Enroll now and become a confident and effective online educator!

Learning Coach Qualification – is it right for me? A Learning Coach plays a vital role in guiding and supporting young people in their educational journey, helping them develop a range of skills and attributes that contribute to successful learning, personal growth, and future success. Pursuing a qualification to become a qualified Learning Coach is beneficial for enhancing skills, credibility, and effectiveness in supporting learners’ development. It provides a foundation of knowledge and expertise that contributes to successful coaching experiences. This qualification consists of three units (two mandatory, 1 optional). Please see the Agored Cymru website for full course information, where you can choose the units to suit the needs of your staff. It will cover the following: Young People’s Development: Factors influencing learning, motivation, and barriers to learning. Learning Styles and Inclusive Planning: Learning styles vs. Universal Design for Learning (UDL), planning for inclusive learning. Reflective Practice: Comparison of reflection models, engaging in reflective practice. Coaching for Learning: Communication and coaching skills, coaching models, functions of Learning Coaching Assessing Young People: Interview skills, communication styles, assessing achievements and barriers to learning. Developing Learning Pathways: Coordinating individualised learning pathways, engaging young people in goal-setting Further Information This course spans six months and covers various aspects of Learning Coaching, including understanding learning processes, personal and emotional factors affecting learning, coaching techniques, reflective practice, communication skills, inclusive planning, assessment, and the coordination of individualised learning pathways. Students will engage in assignments, reflective journals, case studies, and portfolio building to demonstrate their understanding and application of the concepts and skills covered in the course. Learning Outcomes Professional Competence: A formal qualification ensures that Learning Coaches have a solid understanding of best practices, theories, and methodologies in the field of education and coaching. It provides them with the necessary knowledge and skills to effectively support learners in their development. Credibility: Having a recognised qualification enhances the Learning Coach’s credibility and reputation. It demonstrates their commitment to their role and their ability to provide high-quality guidance and support. Improved Effectiveness: Learning Coaches who have undergone formal training are better equipped to assess learners’ needs, design personalised learning plans, and provide targeted support. This leads to more effective coaching outcomes. Personal Growth: Going through a qualification process often involves self-reflection, skill development, and continuous learning. This contributes to the personal growth and professional development of the Learning Coach. Network and Community: Enrolling in a qualification program provides Learning Coaches with the opportunity to connect with other professionals in the field. This network can offer support, resources, and collaboration opportunities.