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Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00

If data is the new oil, then coding is your refinery. Whether you're exploring the depths of machine learning or navigating databases with ease, this course sharpens your edge in the competitive world of data science. With a sharp focus on three industry-leading languages—Python, JavaScript, and Microsoft SQL—you’ll build the solid foundations needed to analyse, automate, and query data confidently. From writing clean scripts to crafting smart SQL queries, you’ll develop the mindset to speak fluently in the language of data. Delivered entirely online, the course keeps your learning agile and accessible. Python lays the groundwork for analysis and automation, JavaScript helps in data visualisation and interaction, and SQL ensures you can command databases without blinking. It's not about ticking boxes—it’s about building fluency in what matters. Whether you're upskilling or aiming for a sharper digital edge, this course speaks directly to future-focused learners ready to code with purpose. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: JavaScript Section 01: Introduction Section 02: Basics Section 03: Operators Section 04: Conditional Statements Section 05: Control Flow Statements Section 06: Functions Section 07: Error Handling Section 08: Client-Side Validations Python Section 09: Introduction Section 10: Basic Section 11: Strings Section 12: Operators Section 13: Data Structures Section 14: Conditional Statements Section 15: control flow statements Section 16: core games Section 17: functions Section 18: args, KW args for Data Science Section 19: project Section 20: Object oriented programming [OOPs] Section 21: Methods Section 22: Class and Objects Section 23: Inheritance and Polymorphism Section 24: Encapsulation and Abstraction Section 25: OOPs Games Section 26: Modules and Packages Section 27: Error Handling Microsoft SQL Section 28: Introduction Section 29: Statements Section 30: Filtering Data Section 31: Functions Section 32: Joins Section 33: Advanced commands Section 34: Structure and Keys Section 35: Queries Section 36: Structure queries Section 37: Constraints Section 38: Backup and Restore Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn Python, JavaScript, and Microsoft SQL for Data science course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn Python, JavaScript, and Microsoft SQL for Data science course is open to all, with no formal entry requirements. Career path Upon successful completion of the Learn Python, JavaScript, and Microsoft SQL for Data science Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

Overview This Deep Learning & Neural Networks Python - Keras: For Dummies course will unlock your full potential and will show you how to excel in a career in Deep Learning & Neural Networks Python - Keras: For Dummies. So upskill now and reach your full potential. Everything you need to get started in Deep Learning & Neural Networks Python - Keras: For Dummies is available in this course. Learning and progressing are the hallmarks of personal development. This Deep Learning & Neural Networks Python - Keras: For Dummies will quickly teach you the must-have skills needed to start in the relevant industry. In This Deep Learning & Neural Networks Python - Keras: For Dummies Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Deep Learning & Neural Networks Python - Keras: For Dummies skills to help you advance your career. Acquire a comprehensive understanding of various Deep Learning & Neural Networks Python - Keras: For Dummies topics and tips from industry experts. Learn in-demand Deep Learning & Neural Networks Python - Keras: For Dummies skills that are in high demand among UK employers, which will help you to kickstart your career. This Deep Learning & Neural Networks Python - Keras: For Dummies course covers everything you must know to stand against the tough competition in the Deep Learning & Neural Networks Python - Keras: For Dummies field.  The future is truly yours to seize with this Deep Learning & Neural Networks Python - Keras: For Dummies. Enrol today and complete the course to achieve a Deep Learning & Neural Networks Python - Keras: For Dummies certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Deep Learning & Neural Networks Python - Keras: For Dummies course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Deep Learning & Neural Networks Python - Keras: For Dummies course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Deep Learning & Neural Networks Python - Keras: For Dummies course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Deep Learning & Neural Networks Python - Keras: For Dummies is suitable for anyone aspiring to start a career in Deep Learning & Neural Networks Python - Keras: For Dummies; even if you are new to this and have no prior knowledge on Deep Learning & Neural Networks Python - Keras: For Dummies, this course is going to be very easy for you to understand.  And if you are already working in the Deep Learning & Neural Networks Python - Keras: For Dummies field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Deep Learning & Neural Networks Python - Keras: For Dummies course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Deep Learning & Neural Networks Python - Keras: For Dummies course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Deep Learning & Neural Networks Python - Keras: For Dummies course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Deep Learning & Neural Networks Python - Keras: For Dummies course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum Course Introduction and Table of Contents Course Introduction and Table of Contents 00:11:00 Deep Learning Overview Deep Learning Overview - Theory Session - Part 1 00:06:00 Deep Learning Overview - Theory Session - Part 2 00:07:00 Choosing Between ML or DL for the next AI project - Quick Theory Session Choosing Between ML or DL for the next AI project - Quick Theory Session 00:09:00 Preparing Your Computer Preparing Your Computer - Part 1 00:07:00 Preparing Your Computer - Part 2 00:06:00 Python Basics Python Basics - Assignment 00:09:00 Python Basics - Flow Control 00:09:00 Python Basics - Functions 00:04:00 Python Basics - Data Structures 00:12:00 Theano Library Installation and Sample Program to Test Theano Library Installation and Sample Program to Test 00:11:00 TensorFlow library Installation and Sample Program to Test TensorFlow library Installation and Sample Program to Test 00:09:00 Keras Installation and Switching Theano and TensorFlow Backends Keras Installation and Switching Theano and TensorFlow Backends 00:10:00 Explaining Multi-Layer Perceptron Concepts Explaining Multi-Layer Perceptron Concepts 00:03:00 Explaining Neural Networks Steps and Terminology Explaining Neural Networks Steps and Terminology 00:10:00 First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset 00:07:00 Explaining Training and Evaluation Concepts Explaining Training and Evaluation Concepts 00:11:00 Pima Indian Model - Steps Explained Pima Indian Model - Steps Explained - Part 1 00:09:00 Pima Indian Model - Steps Explained - Part 2 00:07:00 Coding the Pima Indian Model Coding the Pima Indian Model - Part 1 00:11:00 Coding the Pima Indian Model - Part 2 00:09:00 Pima Indian Model - Performance Evaluation Pima Indian Model - Performance Evaluation - Automatic Verification 00:06:00 Pima Indian Model - Performance Evaluation - Manual Verification 00:08:00 Pima Indian Model - Performance Evaluation - k-fold Validation - Keras Pima Indian Model - Performance Evaluation - k-fold Validation - Keras 00:10:00 Pima Indian Model - Performance Evaluation - Hyper Parameters Pima Indian Model - Performance Evaluation - Hyper Parameters 00:12:00 Understanding Iris Flower Multi-Class Dataset Understanding Iris Flower Multi-Class Dataset 00:08:00 Developing the Iris Flower Multi-Class Model Developing the Iris Flower Multi-Class Model - Part 1 00:09:00 Developing the Iris Flower Multi-Class Model - Part 2 00:06:00 Developing the Iris Flower Multi-Class Model - Part 3 00:09:00 Understanding the Sonar Returns Dataset Understanding the Sonar Returns Dataset 00:07:00 Developing the Sonar Returns Model Developing the Sonar Returns Model 00:10:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Data Preparation - Standardization 00:15:00 Sonar Performance Improvement - Layer Tuning for Smaller Network Sonar Performance Improvement - Layer Tuning for Smaller Network 00:07:00 Sonar Performance Improvement - Layer Tuning for Larger Network Sonar Performance Improvement - Layer Tuning for Larger Network 00:06:00 Understanding the Boston Housing Regression Dataset Understanding the Boston Housing Regression Dataset 00:07:00 Developing the Boston Housing Baseline Model Developing the Boston Housing Baseline Model 00:08:00 Boston Performance Improvement by Standardization Boston Performance Improvement by Standardization 00:07:00 Boston Performance Improvement by Deeper Network Tuning Boston Performance Improvement by Deeper Network Tuning 00:05:00 Boston Performance Improvement by Wider Network Tuning Boston Performance Improvement by Wider Network Tuning 00:04:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 00:09:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 00:08:00 Save and Load Model as YAML File - Pima Indian Dataset Save and Load Model as YAML File - Pima Indian Dataset 00:05:00 Load and Predict using the Pima Indian Diabetes Model Load and Predict using the Pima Indian Diabetes Model 00:09:00 Load and Predict using the Iris Flower Multi-Class Model Load and Predict using the Iris Flower Multi-Class Model 00:08:00 Load and Predict using the Sonar Returns Model Load and Predict using the Sonar Returns Model 00:10:00 Load and Predict using the Boston Housing Regression Model Load and Predict using the Boston Housing Regression Model 00:08:00 An Introduction to Checkpointing An Introduction to Checkpointing 00:06:00 Checkpoint Neural Network Model Improvements Checkpoint Neural Network Model Improvements 00:10:00 Checkpoint Neural Network Best Model Checkpoint Neural Network Best Model 00:04:00 Loading the Saved Checkpoint Loading the Saved Checkpoint 00:05:00 Plotting Model Behavior History Plotting Model Behavior History - Introduction 00:06:00 Plotting Model Behavior History - Coding 00:08:00 Dropout Regularization - Visible Layer Dropout Regularization - Visible Layer - Part 1 00:11:00 Dropout Regularization - Visible Layer - Part 2 00:06:00 Dropout Regularization - Hidden Layer Dropout Regularization - Hidden Layer 00:06:00 Learning Rate Schedule using Ionosphere Dataset - Intro Learning Rate Schedule using Ionosphere Dataset 00:06:00 Time Based Learning Rate Schedule Time Based Learning Rate Schedule - Part 1 00:07:00 Time Based Learning Rate Schedule - Part 2 00:12:00 Drop Based Learning Rate Schedule Drop Based Learning Rate Schedule - Part 1 00:07:00 Drop Based Learning Rate Schedule - Part 2 00:08:00 Convolutional Neural Networks - Introduction Convolutional Neural Networks - Part 1 00:11:00 Convolutional Neural Networks - Part 2 00:06:00 MNIST Handwritten Digit Recognition Dataset Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00 Downloading and Testing MNIST Handwritten Digit Recognition Dataset 00:10:00 MNIST Multi-Layer Perceptron Model Development MNIST Multi-Layer Perceptron Model Development - Part 1 00:11:00 MNIST Multi-Layer Perceptron Model Development - Part 2 00:06:00 Convolutional Neural Network Model using MNIST Convolutional Neural Network Model using MNIST - Part 1 00:13:00 Convolutional Neural Network Model using MNIST - Part 2 00:12:00 Large CNN using MNIST Large CNN using MNIST 00:09:00 Load and Predict using the MNIST CNN Model Load and Predict using the MNIST CNN Model 00:14:00 Introduction to Image Augmentation using Keras Introduction to Image Augmentation using Keras 00:11:00 Augmentation using Sample Wise Standardization Augmentation using Sample Wise Standardization 00:10:00 Augmentation using Feature Wise Standardization & ZCA Whitening Augmentation using Feature Wise Standardization & ZCA Whitening 00:04:00 Augmentation using Rotation and Flipping Augmentation using Rotation and Flipping 00:04:00 Saving Augmentation Saving Augmentation 00:05:00 CIFAR-10 Object Recognition Dataset - Understanding and Loading CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:00 Simple CNN using CIFAR-10 Dataset Simple CNN using CIFAR-10 Dataset - Part 1 00:09:00 Simple CNN using CIFAR-10 Dataset - Part 2 00:06:00 Simple CNN using CIFAR-10 Dataset - Part 3 00:08:00 Train and Save CIFAR-10 Model Train and Save CIFAR-10 Model 00:08:00 Load and Predict using CIFAR-10 CNN Model Load and Predict using CIFAR-10 CNN Model 00:16:00 RECOMENDED READINGS Recomended Readings 00:00:00

Become a Python developer and build a rewarding career in tech. Python is one of the most popular and in-demand programming languages in the world. Python is used by companies of all sizes, from startups to Fortune 500 companies, to develop a wide range of applications, including web applications, data science tools, and machine learning algorithms. The demand for Python developers is rising rapidly in the UK, with job postings for Python developers increasing by 22% in the past year. The average salary for a Python developer in the UK is £65,000, making it one of the highest-paid programming languages. Our Python Programming - Beginner to Advanced course will teach you everything you need to know to become a Python developer. You will learn the fundamentals of Python programming, as well as more advanced topics such as object-oriented programming, data structures, and algorithms. You will also learn how to use popular Python libraries and frameworks, such as Django and NumPy. Why would you choose the Python Programming course from Compliance Central: Lifetime access to Python Programming course materials Full tutor support is available from Monday to Friday with the Python Programming course Learn Python Programming skills at your own pace from the comfort of your home Gain a complete understanding of Python Programming course Accessible, informative Python Programming learning modules designed by experts Get 24/7 help or advice from our email and live chat teams with the Python Programming Study Python Programming in your own time through your computer, tablet or mobile device A 100% learning satisfaction guarantee with your Python Programming course Python Programming Curriculum Breakdown of the Python Programming Course Section 01: Introduction & Getting Started Section 02: Downloading and Installing Python Editor Section 03: Getting Started Section 04: Variables and Basic Data Types in Python Section 05: Comments Section 06: Input Section 07: Exercise - Build a Program to Say Hello Section 08: Exercise - Build a Simple Calculator Section 09: Conditional Statements Section 10: Loops - For Loop Section 11: Loops - While Loop Section 12: Exercise - Building a Username Password App. Python Programming - Beginner to Advanced Course Learning Outcomes: Familiarise with Python's core principles and setup. Understand fundamental data types and variable operations in Python. Recognise the significance and application of comments in Python. Master the art of obtaining and processing user input in Python. Employ conditional structures with proficiency. Navigate confidently within both "For" and "While" loops. Conceptualise and draft rudimentary Python applications. Certificate of Achievement After successfully completing this Python course, you can get a digital and a hardcopy certificate for free. The delivery charge of the hardcopy certificate inside the UK is £3.99 and international students need to pay £9.99 to get their hardcopy certificate. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Python Programming course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Python Programming. It is also great for professionals who are already working in Python Programming and want to get promoted at work. Requirements To enrol in this Python Programming course, all you need is a basic understanding of the English Language and an internet connection. Career path The Python Programming course will enhance your knowledge and improve your confidence in exploring opportunities in various sectors related to Python Programming. Python Developer: £35,000 to £70,000 per year Data Analyst (Python): £30,000 to £55,000 per year Software Engineer (Python): £40,000 to £75,000 per year Machine Learning Engineer: £45,000 to £80,000 per year Certificates CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited PDF Certificate CPD Accredited Hard Copy Certificate Hard copy certificate - £10.79 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99 each

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Module 01: Introduction to MySQL Server and Databases Module 02: Download and Install MySQL Server and MySQL Workbench Unit 02: Manipulating Tables and Data - CRUD Operations Learn to manage databases through creating, reading, updating, and deleting (CRUD) data in SQL and MySQL Workbench. Module 01: Connect and Create a Database Module 02: Drop or Remove Database Module 03: Create an SQL Database Table Module 04: Insert Data into the Table with SQL Script Module 05: Insert Data into the Table with Workbench Module 06: Select Data from the Table with SQL Script Module 07: Select Data with Filters Module 08: Update Data in the Table Module 09: Delete Data from the Table Module 10: Reverse Engineer Database into Model Module 11: Forward Engineer Data Model into Database Unit 03: Relationships and Foreign Keys Understand and implement relationships, foreign keys, and normalization in databases, enhancing data integrity and querying capabilities.ys Module 01: What are Relationships, Foreign Keys and Normalization? Module 02: Create Relationships with Data Modeling Module 03: Create Relationships with Workbench Table Design Tool Module 04: Insert Records in Related Tables Module 05: Run Queries on Related Tables (Inner Joins) Module 06: Left, Right and Cross-Joins Unit 04: Aggregate Functions Explore aggregate functions to perform calculations on data sets, including grouping, averaging, counting, and summing data. Module 01: Grouping Data using SQL GROUP BY Clause Module 02: SQL AVG Aggregate Function Module 03: SQL COUNT Aggregate Function Module 04: SQL MIN & MAX Aggregate Functions Module 05: SQL SUM Aggregate Function Module 06: Splitting Groups using HAVING Clause CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Database Administrator course is ideal for: Individuals with an interest in data management and database systems. IT professionals seeking to expand their skillset and transition into a Database Administrator role. Business analysts who want to gain a deeper understanding of data structures and manipulation techniques. Anyone passionate about building and maintaining robust and efficient databases. Those looking to enhance their career prospects in the ever-growing field of data. Requirements To enrol in this Database Administrator course, all you need is a basic understanding of the English Language and an internet connection. Career path A successful career as a Database Administrator can open doors to exciting opportunities. Here are some potential career paths to consider: Database Administrator Database Analyst Database Architect Data Warehouse Specialist Business Intelligence Analyst Data Scientist (with further specialization) Database Security Specialist Certificates CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited PDF Certificate CPD Accredited Hard Copy Certificate Hard copy certificate - £10.79 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99 each

Ever stared at a website and thought, “How did they build that?” This course is your no-nonsense route to understanding the hows, whats and whys of web development—without needing a computer science degree or a background in tech. Whether you’re just curious or keen to reshape your digital future, we’ll walk you through HTML, CSS, JavaScript, and more—step by step, with clarity, purpose and the occasional chuckle where appropriate. Designed for complete beginners, this online course delivers the technical foundations of building websites from the ground up—clearly and without any fluff. By the end, you’ll understand how web pages come to life, how to structure content, and how styling and scripts bring everything together. No over-the-top jargon, no pressure—just a solid, well-paced approach to the world behind the screen. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: Here is a curriculum breakdown of the Learn Web Development from Scratch course: Section 01: Introduction Introduction How to Get Course requirements Getting Started on Windows, Linux or Mac How to ask Great Questions FAQ's Section 02: HTML Introduction HTML Choosing Code Editor Installing Code Editor (Sublime Text) Overview of a Webpage Structure of a Full HTML Webpage First Hello World! Webpage Section 03: HTML Basic Heading tags Paragraph Formatting Text List Items Unordered List Items Ordered Classes IDs Comments Section 04: HTML Intermediate Images Forms Marquee Text area Tables Links Navbar - Menu HTML Entities Div tag Google Maps Section 05: HTML Advanced HTML Audio HTML Video Canvas Iframes Input Types Input Attributes Registration Form Contact Us Form Coding Exercise Solution for Coding Exercise Section 06: JavaScript Introduction What is JavaScript Hello World Program Getting Output Internal JavaScript External JavaScript Inline JavaScript Async and defer Section 07: JavaScript Basics Variables Data Types Numbers Strings String Formatting Section 08: JavaScript Operators Arithmetic operators Assignment operators Comparison operators Logical operators Section 09: JavaScript Conditional Statements If-else statement If-else-if statement Section 10: JavaScript Control Flow Statements While loop Do-while loop For loop Coding Exercise Solution for Coding Exercise Section 11: JavaScript Functions Creating a Function Function Call() Function with parameters Section 12: JavaScript Error Handling Try-catch Try-catch-finally Section 13: JavaScript Client-Side Validations On Submit Validation Input Numeric Validation Section 14: Python Introduction Introduction to Python Python vs Other Languages Why It's Popular Command Line Basics Python Installation (Step By Step) PyCharm IDE Installation Getting Start PyCharm IDE First Python Hello World Program Section 15: Python Basic Variables Data Types Type Casting User Inputs Comments Section 16: Python Strings Strings String Indexing String Slicing String Built-in Functions Formatting String (Dynamic Data) Section 17: Python Operators Arithmetic Operators Assignment Operators Comparison Operators Logical Operators AND Operator OR Operator NOT Operator Booleans Section 18: Python Data Structures Arrays in Earlier Lists Add List Items Remove List Items Sort Lists Join Lists Tuples Update tuples Join tuples Dictionaries Add Dictionary Items Remove Dictionary Items Nested Dictionaries Sets Add Set Items Remove Set Items Join Set Items Section 19: Python Conditional Statements If Statement If-else Statement If-elif-else Statement If Statement Coding Exercise Section 20: Python Control Flow Statements Flow Charts While Loops Statement For Loops Statement The range() Function Nested Loops 2D List using Nested Loop Section 21: Python Core Games Guessing Game Car Game Section 22: Python Functions Creating a Function Calling a Function Function with Arguments Section 23: Python args, KW args for Data Science args, Arbitary Arguments kwargs, Arbitary Keyword Arguments Section 24: Python Project Project Overview ATM Realtime Project Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn Web Development from Scratch course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn Web Development from Scratch course is open to all, with no formal entry requirements. Career path Upon successful completion of the Learn Web Development from Scratch Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Re-imaging the World´s Economic Data Remember the "Kodak Moment?' It was a term in photography popularized by Kodak to capture important moments. Well, right now there is a Kodak Moment going on in healthcare information sciences. It is associated with the attribute-based data structures that are the basis for the revolution in genetic diagnostics, clinical risk management, and personalized medicine. It is also the foundation and source of the advances in Big Data and Artificial Intelligence in healthcare. In this session, you will learn about a new innovation in business information management called the Locus Model and a new type of business information system called the Functional Information System (FIS). These important innovations have the potential to impact all data management in business, finance, and economics by introducing a universal standard that can unify the disparate systems in disparate countries that we currently use to classify and organize business, finance, products or job information. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.

Re-imaging the World´s Economic Data Remember the "Kodak Moment?' It was a term in photography popularized by Kodak to capture important moments. Well, right now there is a Kodak Moment going on in healthcare information sciences. It is associated with the attribute-based data structures that are the basis for the revolution in genetic diagnostics, clinical risk management, and personalized medicine. It is also the foundation and source of the advances in Big Data and Artificial Intelligence in healthcare. In this session, you will learn about a new innovation in business information management called the Locus Model and a new type of business information system called the Functional Information System (FIS). These important innovations have the potential to impact all data management in business, finance, and economics by introducing a universal standard that can unify the disparate systems in disparate countries that we currently use to classify and organize business, finance, products or job information. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.

About Course Master the Fundamentals of Programming with Python Course Description Embark on an exciting journey into the world of programming with this comprehensive Python course, designed to equip you with the essential skills and knowledge to become a proficient Python programmer. Whether you're a complete beginner or seeking to enhance your existing Python skills, this course caters to all levels of expertise. What will be discussed in detail? Introduction to Python: Delve into the basics of Python programming, including variables, data types, operators, and control flow statements. Working with Data Types: Explore the fundamental data types in Python, including numbers, strings, booleans, and lists. Python Strings: Master the art of manipulating strings, including slicing, concatenation, and string formatting techniques. Python Lists: Discover the power of lists, one of Python's most versatile data structures, and learn how to create, access, modify, and iterate over lists. Python Casting and Input: Understand the concept of type casting and learn how to take user input from the console. Python Dictionary: Uncover the usefulness of dictionaries, another essential data structure in Python, and learn how to store and retrieve data using key-value pairs. Python Date and Time: Learn how to handle date and time operations in Python, including creating, formatting, and manipulating date and time objects. Python Loop Part 1: Master the 'for' loop, a fundamental looping construct in Python, to iterate over sequences and perform repetitive tasks. Python Loop Part 2: Expand your understanding of loops by exploring the 'while' loop, used to execute a block of code repeatedly while a condition remains true. Creating a Function: Discover the power of functions, reusable blocks of code that perform specific tasks, and learn how to define, call, and pass arguments to functions. Python OOP Part 1: Delve into the world of Object-Oriented Programming (OOP) with Python, and learn the concepts of classes, objects, inheritance, and polymorphism. Python OOP Part 2: Enhance your OOP skills by exploring advanced concepts such as abstract classes, multiple inheritance, and operator overloading. Python Advanced OOP Part 1: Discover more advanced OOP techniques, including class methods, static methods, and decorators. Python Advanced OOP Part 2: Master the concept of exception handling, a crucial aspect of robust programming, and learn how to handle errors and exceptions effectively. Error Handling: Understand the importance of error handling in Python programming and learn how to identify, handle, and prevent errors. Python File Handling: Learn how to read, write, and manipulate files in Python, enabling you to store and retrieve data from external sources. Python Modules: Explore the concept of modules, reusable code libraries, and discover how to import, use, and create your own modules. Why should you enroll into it? Gain a comprehensive understanding of Python programming: Master the fundamentals of Python programming, from basic syntax to advanced OOP concepts. Develop practical coding skills: Apply your theoretical knowledge to hands-on coding exercises, solidifying your understanding and building your confidence. Prepare for a career in programming: Equip yourself with the essential skills required for entry-level programming positions. Enhance your problem-solving abilities: Learn to think algorithmically and develop effective problem-solving techniques using Python programming. Expand your skillset and knowledge: Whether you're a beginner or an experienced programmer, this course will broaden your understanding of Python and its capabilities. What will be taught? (Learning Outcomes/Learning Objectives) Understand the fundamental concepts of Python programming Work with different data types, including numbers, strings, lists, dictionaries, and Booleans Master control flow statements such as 'if', 'elif', and 'else' Create and manipulate Python functions Implement Object-Oriented Programming (OOP) concepts using classes, objects, inheritance, and polymorphism Handle errors and exceptions effectively Read, write, and manipulate files in Python Import, use, and create Python modules What Will You Learn? Understand the fundamental concepts of Python programming Work with different data types, including numbers, strings, lists, dictionaries, and Booleans Master control flow statements such as 'if', 'elif', and 'else' Create and manipulate Python functions Implement Object-Oriented Programming (OOP) concepts using classes, objects, inheritance, and polymorphism Handle errors and exceptions effectively Read, write, and manipulate files in Python Import, use, and create Python modules Course Content Introduction to Python Introduction to Python Working with Data Types Working with Data Types Python Strings Python Strings Python List Python List Python Casting and Input Python Casting and Input Python Dictionary Python Dictionary Python Date and Time Python Date and Time Python Loop (Part - 1) Python Loop (Part - 1) Python Loop (Part - 2) Python Loop (Part - 2) Python While Loop Python While Loop Creating a Function Creating a Function Python OOP (Part - 1) Python OOP (Part - 1) Python OOP (Part - 2) Python OOP (Part - 2) Python Advanced OOP (Part - 1) Python Advanced OOP (Part - 1) Python Advanced OOP (Part - 2) Python Advanced OOP (Part - 2) Error Handling Error Handling Python File Handling Python File Handling Python Modules Python Modules A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsA basic understanding of computers and operating systemsA willingness to learn and practice codingA computer with internet access and the ability to install Python Audience Beginners with no prior programming experience Programmers seeking to transition to Python Individuals looking to enhance their programming skills and knowledge Anyone interested in pursuing a career in programming Audience Beginners with no prior programming experience Programmers seeking to transition to Python Individuals looking to enhance their programming skills and knowledge Anyone interested in pursuing a career in programming