10735 Concept courses

Deepen your knowledge and understanding of water chemistry with the Water Chemistry Level 2 course. This course will provide you with insights into the components and the process of water treatment. You will also receive a valuable certificate upon completing the course. This certificate will elevate your resume and boost your employability in the relevant job market. The Water Chemistry Level 2 course will introduce you to the basics of chemistry. From this step-by-step learning process, you will learn the Alkalinity of water. The course will cover lessons on the principles of hard water and basic water treatment. You will comprehend the process of chemical water treatment. The informative modules will increase your knowledge of inorganic chemicals, volatile and synthetic organic compounds. From this comprehensive course, you will also attain the skills to maintain the safety and security of the laboratory. It will educate you on the essentials of chemical hygiene measures. The Water Chemistry Level 2 course is the perfect package to build your skills in water chemistry. Enrol now and start learning. Learning Objectives Familiarise yourself with the basics of chemistry Enrich your understanding of Alkalinity of Water Learn the principles of hard water and basic water treatment Know the chemical process of water treatment Get introduced to inorganic chemicals, volatile and synthetic organic compounds Deepen your understanding of the laboratory safety measures Explore the essential aspects of the chemical hygiene plan Who is this Course for? This Water Chemistry Level 2 course is ideal for aspiring professionals of the relevant industries who wish to gain the relevant skills and knowledge to fast track their careers. It is for those who have little or no knowledge of the principles of water chemistry or those who are new to the field and want to test their skills and knowledge. There are no entry requirements for this course; however, an eye for detail and a creative mind is essential. Entry Requirement This course is available to all learners of all academic backgrounds. A good understanding of the English language, numeracy and ICT are required to attend this course. CPD Certificate from Course Gate At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £4.99 and the hard copy for £9.99. Also, you can order both PDF and hardcopy certificates for £12.99. Career path On successful completion of the Water Chemistry Level 2 course, learners can progress to a more advanced program from our course list. Career opportunities in this field include freelancing or working in-house, within a range of professional settings, with the opportunity to earn a high salary. Related professions in this industry include: Water Treatment Operator Environmental Scientist Project Engineer Environmental Engineer Water Operator Course Curriculum Module 01: Introduction to Water Chemistry Introduction to Water Chemistry 00:23:00 Module 02: Concepts of Basic Chemistry Concepts of Basic Chemistry 00:22:00 Module 03: Alkalinity of Water Alkalinity of Water 00:15:00 Module 04: Hard Water and Basic Water Treatment Principles Hard Water and Basic Water Treatment Principles 00:20:00 Module 05: Water Treatment and Purification Water Treatment and Purification 00:31:00 Module 06: Chemical Processes of Water Treatment Chemical Processes of Water Treatment 00:16:00 Module 07: Inorganic Chemicals Inorganic Chemicals 00:17:00 Module 08: Volatile and Synthetic Organic Compounds Volatile and Synthetic Organic Compounds 00:25:00 Module 09: Metalloid Section Metalloid Section 00:18:00 Module 10: Metals and Heavy Metal Section Metals and Heavy Metal Section 00:33:00 Module 11: Laboratory Safety Laboratory Safety 00:33:00 Module 12: Chemical Hygiene Plan Chemical Hygiene Plan 00:13:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

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Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.   Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method.: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00

Overview This comprehensive course on CompTIA Security+ (SY0-601) will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This CompTIA Security+ (SY0-601) 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 CompTIA Security+ (SY0-601). It is available to all students, of all academic backgrounds. Requirements Our CompTIA Security+ (SY0-601) 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 14 sections • 164 lectures • 20:35:00 total length •Introduction to CompTIA Security+ SY0-601: 00:03:00 •About the CompTIA Security+ SY0-601 Exam: 00:03:00 •Defining Risk: 00:08:00 •Threats and Vulnerabilities: 00:07:00 •Threat Intelligence: 00:11:00 •Risk Management Concepts: 00:07:00 •Security Controls: 00:09:00 •Risk Assessments and Treatments: 00:06:00 •Quantitative Risk Assessments: 00:07:00 •Qualitative Risk Assessments: 00:04:00 •Business Impact Analysis: 00:09:00 •Data Types and Roles: 00:11:00 •Security and the Information Life Cycle: 00:09:00 •Data Destruction: 00:06:00 •Personnel Risk and Policies: 00:10:00 •Third-Party Risk Management: 00:09:00 •Agreement Types: 00:07:00 •Exam Question Review: 00:02:00 •Wiping Disks with the dd Command Lab: 00:06:00 •Ask Me Anything (AMA): 00:02:00 •Cryptography Basics: 00:16:00 •Data Protection: 00:09:00 •Cryptographic Methods: 00:07:00 •Symmetric Cryptosystems: 00:13:00 •Symmetric Block Modes: 00:08:00 •Asymmetric Cryptosystems: 00:13:00 •Diffie-Hellman: 00:07:00 •Hashing: 00:09:00 •Understanding Digital Certificates: 00:08:00 •Trust Models: 00:05:00 •Public Key Infrastructure: 00:04:00 •Certificate Types: 00:14:00 •Touring Certificates: 00:09:00 •Cryptographic Attacks: 00:05:00 •Password Cracking: 00:10:00 •Password Cracking Demo: 00:06:00 •Exam Question Review: 00:02:00 •SSH Public Key Authentication Lab: 00:09:00 •Ask Me Anything (AMA): 00:02:00 •Identification, Authentication, and Authorization: 00:08:00 •Enabling Multifactor Authentication: 00:05:00 •Authorization: 00:05:00 •Accounting: 00:05:00 •Authentication Methods: 00:14:00 •Access Control Schemes: 00:07:00 •Account Management: 00:13:00 •Network Authentication: 00:09:00 •Identity Management Systems: 00:06:00 •Exam Question Review: 00:02:00 •Creating LInux Users and Groups Lab: 00:06:00 •Ask Me Anything (AMA): 00:01:00 •Touring the CLI: 00:16:00 •Shells: 00:06:00 •The Windows Command Line: 00:05:00 •Microsoft PowerShell: 00:13:00 •Linux Shells: 00:12:00 •Python Scripts: 00:07:00 •Windows Command-Line Tools: 00:16:00 •Linux Command-Line Tools: 00:10:00 •Network Scanners: 00:05:00 •Network Scanning with Nmap: 00:09:00 •Network Protocol Analyzers: 00:08:00 •Using Wireshark to Analyze Network Traffic: 00:09:00 •Using tcpdump to Analyze Network Traffic: 00:08:00 •Log Files: 00:09:00 •Centralized Logging: 00:09:00 •Configuring Linux Log Forwarding: 00:08:00 •Exam Question Review: 00:03:00 •Lunux Shell Script Lab: 00:07:00 •Nmap Lab: 00:05:00 •Ask Me Anything (AMA): 00:02:00 •Malware: 00:14:00 •Weak Configurations: 00:12:00 •Common Attacks: 00:09:00 •Driver and Overflow Attacks: 00:08:00 •Password Attacks: 00:08:00 •Bots and Botnets: 00:06:00 •Disk RAID Levels: 00:10:00 •Securing Hardware: 00:11:00 •Securing Endpoints: 00:09:00 •Exam Question Review: 00:02:00 •Linux Software RAID Lab: 00:08:00 •Ask Me Anything (AMA): 00:02:00 •The OSI Model: 00:13:00 •ARP Cache Poisoning: 00:09:00 •Other Layer 2 Attacks: 00:05:00 •Network Planning: 00:07:00 •Load Balancing: 00:06:00 •Securing Network Access: 00:06:00 •Honeypots: 00:06:00 •Firewalls: 00:11:00 •Proxy Servers: 00:06:00 •Network and Port Address Translation: 00:07:00 •IP Security (IPsec): 00:09:00 •Virtual Private Networks (VPNs): 00:10:00 •Intrusion Detection and Prevention Systems (IDS/IPS): 00:13:00 •Exam Question Review: 00:03:00 •Linux Snort IDS Lab: 00:07:00 •Ask Me Anything (AMA): 00:01:00 •Wi-Fi Encryption Standards: 00:10:00 •RFID, NFC, and Bluetooth: 00:07:00 •Wi-Fi Coverage and Performance: 00:08:00 •Wi-Fi Discovery and Attacks: 00:12:00 •Cracking WPA2: 00:10:00 •Wi-Fi Hardening: 00:11:00 •Exam Question Review: 00:02:00 •WPA2 Cracking Lab: 00:06:00 •Ask Me Anything (AMA): 00:01:00 •Defining a Public Server: 00:01:00 •Common Attacks and Mitigations: 00:10:00 •Containers and Software-Defined Networking: 00:11:00 •Hypervisors and Virtual Machines: 00:08:00 •Cloud Deployment Models: 00:09:00 •Cloud Service Models: 00:08:00 •Securing the Cloud: 00:10:00 •Exam Question Review: 00:02:00 •Docker Container Lab: 00:04:00 •Ask Me Anything (AMA): 00:02:00 •Embedded Systems: 00:13:00 •Industrial Control System (ICS): 00:07:00 •Internet of Things (IoT) Devices: 00:10:00 •Connecting to Dedicated and Mobile Systems: 00:11:00 •Security Constraints for Dedicated Systems: 00:05:00 •Mobile Device Deployment and Hardening: 00:11:00 •Exam Question Review: 00:03:00 •Smartphone Hardening Lab: 00:03:00 •Ask Me Anything (AMA): 00:02:00 •Physical Security Overview: 00:01:00 •Physical Security: 00:10:00 •Keylogger Demo: 00:05:00 •Environmental Controls: 00:05:00 •Exam Question Review: 00:02:00 •Physical Security Lab: 00:03:00 •Ask Me Anything (AMA): 00:03:00 •DNS Security: 00:05:00 •FTP Packet Capture: 00:03:00 •Secure Web and E-mail: 00:02:00 •Request Forgery Attacks: 00:05:00 •Cross-Site Scripting Attacks: 00:07:00 •Web Application Security: 01:20:00 •Web App Vulnerability Scanning: 00:06:00 •Exam Question Review: 00:03:00 •OWASP ZAP Web App Scan Lab: 00:04:00 •Ask Me Anything (AMA): 00:02:00 •Testing Infrastructure Overview: 00:05:00 •Social Engineering: 00:06:00 •Social Engineering Attacks: 00:11:00 •Vulnerability Assessments: 00:09:00 •Penetration Testing: 00:10:00 •Security Assessment Tools: 00:12:00 •The Metasploit Framework: 00:08:00 •Exam Question Review: 00:02:00 •Hping3 Forged Packet Lab: 00:06:00 •Ask Me Anything (AMA): 00:02:00 •Incident Response Overview: 00:03:00 •Incident Response Plans (IRPs): 00:06:00 •Threat Analysis and Mitigating Actions: 00:08:00 •Digital Forensics: 00:12:00 •Gathering Digital Evidence: 00:10:00 •Business Continuity and Alternate Sites: 00:06:00 •Data Backup: 00:10:00 •Exam Question Review: 00:01:00 •Autopsy Forensic Browser Lab: 00:05:00 •Ask Me Anything (AMA): 00:02:00

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

Be prepared to save lives with our Emergency First Aid Course. Learn essential skills to respond effectively to medical emergencies, including CPR, wound care, and managing choking incidents. Whether you're a healthcare professional, workplace safety officer, or concerned citizen, this course equips you with the knowledge and confidence to handle critical situations with competence and composure. Enroll now and become a certified first aider, ready to provide immediate assistance when it matters most.

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support