Name: Microsoft Azure Cloud Concepts
Price: 12 GBP
Availability: InStock

<p>Have you ever wondered how machines are able to recognise handwritten digits with such precision? Have you ever been amazed by the power of neural networks and their ability to process complex information? If so, our Deep Learning Projects - Handwritten Digit Recognition Using Neural Network course is perfect for you.</p><p>In this course, you'll learn how to build your own neural network to recognise handwritten digits using the power of deep learning. Using Google Collab, you'll import data, preprocess it, and create your own neural network model. You'll learn how to train your model, make predictions, and even create your own convolutional neural network model for even more advanced recognition.</p><p>So if you're ready to delve into the world of deep learning and build your own handwritten digit recognition system, enroll in our Deep Learning Projects course today and unlock the power of neural networks.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a solid understanding of the principles of deep learning and neural networks.</li><li>Learn how to use Google Collab to import and preprocess data for deep learning projects.</li><li>Discover how to create your own neural network model to recognise handwritten digits.</li><li>Understand how to train your model, make predictions, and evaluate its performance.</li><li>Learn how to create your own convolutional neural network model for even more advanced recognition tasks.</li></ul><p>The Deep Learning Projects - Handwritten Digit Recognition Using Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network for handwritten digit recognition. Using Google Collab, you'll learn how to import and preprocess data for deep learning projects, create your own neural network model, and train it to recognize handwritten digits with high accuracy. You'll also discover how to evaluate the performance of your model and create a convolutional neural network model for even more advanced recognition tasks.</p><p><strong>â±â± Deep Learning Projects - Handwritten Digit Recognition Using Neural Network</strong><strong> Course Curriculum </strong></p><ul><li><strong>Lesson 01: </strong>Introduction to Project</li><li><strong>Lesson 02:</strong> Google Collab</li><li><strong>Lesson 03:</strong> Importing Packages and Data</li><li><strong>Lesson 04:</strong> Preprocessing and Model Creation</li><li><strong>Lesson 05: </strong>Training the Model and Prediction</li><li><strong>Lesson 06: </strong>Model Creation using CNN</li><li><strong>Lesson 07: </strong>CNN Model Prediction</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />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.<br /><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Data analysts who want to expand their skills in deep learning and neural networks.</li><li>Programmers who want to learn how to build their own neural network models for advanced recognition tasks.</li><li>Entrepreneurs who want to develop their own deep learning-based applications.</li><li>Students who want to enhance their skills in deep learning and prepare for a career in the field.</li><li>Anyone who wants to explore the world of deep learning and neural networks.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £24,000 - £45,000</li><li>Machine Learning Engineer: £28,000 - £65,000</li><li>Software Engineer: £28,000 - £60,000</li><li>Technical Lead: £40,000 - £90,000</li><li>Chief Technology Officer: £90,000 - £250,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Deep Learning Projects - Handwritten Digit Recognition Using Neural Network Course

General Admission

<h3><strong>Overview</strong></h3>
This comprehensive course on <strong>Python for Data Analysis</strong> will deepen your understanding on this topic.

After successful completion of this course you can acquire the required skills in this sector. This <strong>Python for Data Analysis</strong> comes with accredited certification, 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.
<h3><strong>How will I get my certificate?</strong></h3>
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.
<h3><strong>Who is this course for?</strong></h3>
There is no experience or previous qualifications required for enrolment on this <strong>Python for Data Analysis</strong>. It is available to all students, of all academic backgrounds.
<h3><strong>Requirements</strong></h3>
Our <strong>Python for Data Analysis</strong> 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.
<h3><strong>Career path</strong></h3>
Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc. <h3>Course Curriculum</h3><div><p>19 sections • 99 lectures • 00:08:00 total length</p></div><div><div><div><div><div><div><div><p>•Welcome &amp; Course Overview: 00:07:00</p></div><div><div><p>•Set-up the Environment for the Course (lecture 1): 00:09:00</p></div><div><div><p>•Set-up the Environment for the Course (lecture 2): 00:25:00</p></div><div><div><p>•Two other options to setup environment: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python data types Part 1: 00:21:00</p></div><div><div><p>•Python Data Types Part 2: 00:15:00</p></div><div><div><p>•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00</p></div><div><div><p>•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00</p></div><div><div><p>•Python Essentials Exercises Overview: 00:02:00</p></div><div><div><p>•Python Essentials Exercises Solutions: 00:22:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is Numpy? A brief introduction and installation instructions.: 00:03:00</p></div><div><div><p>•NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00</p></div><div><div><p>•NumPy Essentials - Indexing, slicing, broadcasting &amp; boolean masking: 00:26:00</p></div><div><div><p>•NumPy Essentials - Arithmetic Operations &amp; Universal Functions: 00:07:00</p></div><div><div><p>•NumPy Essentials Exercises Overview: 00:02:00</p></div><div><div><p>•NumPy Essentials Exercises Solutions: 00:25:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is pandas? A brief introduction and installation instructions.: 00:02:00</p></div><div><div><p>•Pandas Introduction: 00:02:00</p></div><div><div><p>•Pandas Essentials - Pandas Data Structures - Series: 00:20:00</p></div><div><div><p>•Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00</p></div><div><div><p>•Pandas Essentials - Handling Missing Data: 00:12:00</p></div><div><div><p>•Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00</p></div><div><div><p>•Pandas Essentials - Groupby: 00:10:00</p></div><div><div><p>•Pandas Essentials - Useful Methods and Operations: 00:26:00</p></div><div><div><p>•Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00</p></div><div><div><p>•Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00</p></div><div><div><p>•Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00</p></div><div><div><p>•Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Matplotlib Essentials (Part 1) - Basic Plotting &amp; Object Oriented Approach: 00:13:00</p></div><div><div><p>•Matplotlib Essentials (Part 2) - Basic Plotting &amp; Object Oriented Approach: 00:22:00</p></div><div><div><p>•Matplotlib Essentials (Part 3) - Basic Plotting &amp; Object Oriented Approach: 00:22:00</p></div><div><div><p>•Matplotlib Essentials - Exercises Overview: 00:06:00</p></div><div><div><p>•Matplotlib Essentials - Exercises Solutions: 00:21:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Seaborn - Introduction &amp; Installation: 00:04:00</p></div><div><div><p>•Seaborn - Distribution Plots: 00:25:00</p></div><div><div><p>•Seaborn - Categorical Plots (Part 1): 00:21:00</p></div><div><div><p>•Seaborn - Categorical Plots (Part 2): 00:16:00</p></div><div><div><p>•Seborn-Axis Grids: 00:25:00</p></div><div><div><p>•Seaborn - Matrix Plots: 00:13:00</p></div><div><div><p>•Seaborn - Regression Plots: 00:11:00</p></div><div><div><p>•Seaborn - Controlling Figure Aesthetics: 00:10:00</p></div><div><div><p>•Seaborn - Exercises Overview: 00:04:00</p></div><div><div><p>•Seaborn - Exercise Solutions: 00:19:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pandas Built-in Data Visualization: 00:34:00</p></div><div><div><p>•Pandas Data Visualization Exercises Overview: 00:03:00</p></div><div><div><p>•Panda Data Visualization Exercises Solutions: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting (Part 1): 00:19:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting (Part 2): 00:14:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting Exercises (Overview): 00:11:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting Exercises (Solutions): 00:37:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00</p></div><div><div><p>•Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to ML - What, Why and Types..: 00:15:00</p></div><div><div><p>•Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00</p></div><div><div><p>•scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00</p></div><div><div><p>•scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00</p></div><div><div><p>•Good to know! How to save and load your trained Machine Learning Model!: 00:01:00</p></div><div><div><p>•scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00</p></div><div><div><p>•scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00</p></div><div><div><p>•scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00</p></div><div><div><p>•scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00</p></div><div><div><p>•scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: D-Tree &amp; Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests - Hands-on  (Part 1): 00:19:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00</p></div><div><div><p>•scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: K Means Clustering, Elbow method ..: 00:11:00</p></div><div><div><p>•scikit-learn - K Means Clustering - Hands-on: 00:23:00</p></div><div><div><p>•scikit-learn - K Means Clustering (Project Overview): 00:07:00</p></div><div><div><p>•scikit-learn - K Means Clustering (Project Solutions): 00:22:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Principal Component Analysis (PCA): 00:09:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Recommender Systems their Types and Importance: 00:06:00</p></div><div><div><p>•Python for Recommender Systems - Hands-on (Part 1): 00:18:00</p></div><div><div><p>•Python for Recommender Systems - - Hands-on (Part 2): 00:19:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Natural Language Processing (NLP) - (Theory Lecture): 00:13:00</p></div><div><div><p>•NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00</p></div><div><div><p>•NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00</p></div><div><div><p>•NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00</p></div><div><div><p>•NLTK - BoW, TF-IDF, Machine Learning, Training &amp; Evaluation, Naive Bayes : 00:13:00</p></div><div><div><p>•NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources- Python for Data Analysis: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Online Course Creation

Data Science

Data Analysis

Trainers Course

Data Analyst

Python for Data Analysis

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			<h2>Overview
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					<p><span>This comprehensive course on <strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh </strong></span><span>will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></span><b> </b><span>comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.   </span></p><p><span>So enrol in this course today to fast-track your career ladder.</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>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.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></span><span>. It is available to all students, of all academic backgrounds.</span></p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our <strong>Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></span><span> 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.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>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-</span></p><ul><li><span>Open doors of opportunities</span></li><li><span>Increase your adaptability</span></li><li><span>Keep you relevant</span></li><li><span>Boost confidence</span></li></ul><p><span>And much more!</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>7 sections • 25 lectures • 05:04:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python Installation: 00:03:00</p></div><div><div><p>•Installing Third Party Libraries: 00:03:00</p></div><div><div><p>•Installing Apache Spark: 00:12:00</p></div><div><div><p>•Installing Java (Optional): 00:05:00</p></div><div><div><p>•Testing Apache Spark Installation: 00:06:00</p></div><div><div><p>•Installing MongoDB: 00:04:00</p></div><div><div><p>•Installing NoSQL Booster for MongoDB: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Integrating PySpark with Jupyter Notebook: 00:05:00</p></div><div><div><p>•Data Extraction: 00:19:00</p></div><div><div><p>•Data Transformation: 00:15:00</p></div><div><div><p>•Loading Data into MongoDB: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Pre-processing: 00:19:00</p></div><div><div><p>•Building the Predictive Model: 00:12:00</p></div><div><div><p>•Creating the Prediction Dataset: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Loading the Data Sources from MongoDB: 00:17:00</p></div><div><div><p>•Creating a Map Plot: 00:33:00</p></div><div><div><p>•Creating a Bar Chart: 00:09:00</p></div><div><div><p>•Creating a Magnitude Plot: 00:15:00</p></div><div><div><p>•Creating a Grid Plot: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing Visual Studio Code: 00:05:00</p></div><div><div><p>•Creating the PySpark ETL Script: 00:24:00</p></div><div><div><p>•Creating the Machine Learning Script: 00:30:00</p></div><div><div><p>•Creating the Dashboard Server: 00:21:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Source Code and Notebook: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Online Course Marketing

Data Engineering

Apache Spark

Professional Skills Course (PSC)

Data Skills

Building Big Data Pipelines with PySpark MongoDB and Bokeh

<p>Everywhere, from tiny businesses to major corporations, needs people skilled in SQL. In light of this, our online training course has been developed to help you succeed by equipping you with all the necessary skills.</p> <p>The importance of mastering SQL increases if you're looking for your first job in the data industry. You will learn about topics such as SQL fundamentals, data wrangling, SQL analysis, AB testing, distributed computing with Apache Spark, Delta Lake, and more through four increasingly more challenging SQL projects with data science applications. These subjects will equip you with the skills necessary to use SQL creatively for data analysis and exploration, write queries quickly, produce datasets for data analysis, conduct feature engineering, integrate SQL with other data analysis and machine learning toolsets, and work with unstructured data.</p> <p>This Specialisation is designed for a learner with<strong> little or no prior coding expertise</strong> who wants to become <strong>proficient</strong> with SQL queries. Experts have meticulously planned out the curriculum for the SQL Skills Training course with years of expertise. As a result, you will find it simple to learn the course material.</p> <p><strong>Learning outcome</strong></p> <p>After finishing the course, you'll</p> <ul> <li>Learn to utilise the tools for view creation</li><li>Become familiar with updating columns and indexed views</li><li>Be able to test and debug</li><li>Be able to search a database using SQL</li><li>Become more familiar with inline table-valued functions</li><li>Learn the fundamentals of transactions and multiple statements</li></ul> <p><strong>Why Prefer US? </strong></p> <ul> <li>Opportunity to earn a certificate accredited by <strong>CPD </strong>after completing this course</li><li><strong>Student ID card </strong>with amazing discounts - completely for <strong> FREE! </strong>(£10 postal charges will be applicable for international delivery)</li><li>Standards-aligned lesson planning</li><li>Innovative and engaging content and activities</li><li>Assessments that measure higher-level thinking and skills</li><li>Complete the program in your own time, at your own pace</li><li>Each of our students gets full 24/7 tutor support</li></ul> <p><strong>*** Course Curriculum ***</strong></p> <p><strong> SQL Programming Course </strong></p> <p><strong>Module 01: Course Introduction</strong></p> <ul> <li>Introduction</li><li>Course Curriculum Overview</li><li>Overview of Databases</li></ul> <p><strong>Module 02: SQL Environment Setup</strong></p> <ul> <li>MySQL Installation</li><li>MySQL Workbench Installation</li><li>Connecting to MySQL using Console</li></ul> <p><strong>Module 03: SQL Statement Basics</strong></p> <ul> <li>Overview of Challenges</li><li>SQL Statement Basic</li><li>SELECT Statement</li><li>SELECT DISTINCT</li><li>Column AS Statement</li><li>COUNT built-in Method usage</li><li>SELECT WHERE Clause - Part One</li><li>SELECT WHERE Clause - Part Two</li><li>Statement Basic</li><li>Limit Clause Statement</li><li>Using BETWEEN with Same Column Data</li><li>How to Apply IN Operator</li><li>Wildcard Characters with LIKE and ILIKE</li></ul> <p><strong>Module 04: GROUP BY Statements</strong></p> <ul> <li>Overview of GROUP BY</li><li>Aggregation function SUM()</li><li>Aggregation MIN() and MAX()</li><li>GROUP BY - One</li><li>GROUP BY - Two</li><li>HAVING Clause</li></ul> <p><strong>Module 05: JOINS</strong></p> <ul> <li>Overview of JOINS</li><li>Introduction to JOINS</li><li>AS Statement table</li><li>INNER Joins</li><li>FULL Outer Join</li><li>LEFT Outer JOIN</li><li>RIGHT JOIN</li><li>Union</li></ul> <p><strong>Module 06: Advanced SQL Commands / Statements</strong></p> <ul> <li>Timestamps</li><li>EXTRACT from timestamp</li><li>Mathematical Functions</li><li>String Functions</li><li>SUBQUERY</li></ul> <p><strong>Module 07: Creating Database and Tables</strong></p> <ul> <li>Basic of Database and Tables</li><li>Data Types</li><li>Primary key and Foreign key</li><li>Create Table in SQL Script</li><li>Insert</li><li>Update</li><li>Delete</li><li>Alter Table</li><li>Drop-Table</li><li>NOT NULL Constraint</li><li>UNIQUE Constraint</li></ul> <p><strong>Module 08: Databases and Tables</strong></p> <ul> <li>Creating a Database backup</li><li>10a Overview of Databases and Tables</li><li>10c Restoring a Database</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <p>The course can be helpful for anyone working in the SQL fields, whether self-employed or employed, regardless of their career level.</p> 
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p>You will not need any prior background or expertise to enrol in this course.</p> 
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p>The vocation of SQL Skills Training moves very quickly but pays well. This position provides unparalleled satisfaction. This is your opportunity to learn more and start changing things.</p> <ul> <li>Query Language Developer</li><li>Server Database Manager</li><li>Python Developer</li><li>Technical Consultant</li><li>Project Implementation Manager</li><li>Software Developer (SQL)</li></ul> 
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £10
        </p>

                        </div>

Academy for Health and Fitness

SQL Programming Course

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Dive deep into the world of ChatGPT with our ChatGPT Masterclass. From basic functionalities to advanced applications across various domains, this course equips you with the knowledge to leverage ChatGPT effectively. Enhance your professional skills, academic pursuits, and personal projects by mastering ChatGPT today.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;">Understand the mechanics behind ChatGPT&rsquo;s responses.</li><li fr-original-style="" style="box-sizing: border-box;">Create precise and effective prompts for ChatGPT.</li><li fr-original-style="" style="box-sizing: border-box;">Utilize ChatGPT for creative and technical writing.</li><li fr-original-style="" style="box-sizing: border-box;">Leverage ChatGPT for educational support and learning.</li><li fr-original-style="" style="box-sizing: border-box;">Integrate ChatGPT with Microsoft Excel for data management.</li><li fr-original-style="" style="box-sizing: border-box;">Employ ChatGPT to achieve professional excellence.</li><li fr-original-style="" style="box-sizing: border-box;">Explore practical and innovative ChatGPT prompts.</li><li fr-original-style="" style="box-sizing: border-box;">Implement ChatGPT strategies in social media marketing.</li><li fr-original-style="" style="box-sizing: border-box;">Understand the capabilities of ChatGPT Plus and New Bing.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Curriculum</strong></p><h4 id="isPasted" fr-original-style="" style="box-sizing: border-box;">Module 01: Getting Started with ChatGPT</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to ChatGPT:</strong> Basics of how to interact with and utilize ChatGPT effectively.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 02: Understanding ChatGPT</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How ChatGPT Works:</strong> Insights into the AI and machine learning principles powering ChatGPT.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 03: Crafting Effective ChatGPT Prompts: A Guide</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prompt Engineering:</strong> Techniques for developing precise prompts that generate desired outcomes.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 04: Writing with ChatGPT</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creative and Technical Writing:</strong> Using ChatGPT to assist with various writing tasks and projects.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 05: ChatGPT for Students</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Educational Applications:</strong> How students can use ChatGPT for studying, research, and homework assistance.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 06: ChatGPT for MS Excel</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Excel Integration:</strong> Harnessing ChatGPT for automating tasks and analyzing data in Microsoft Excel.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 07: ChatGPT for Professional Excellence</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Career Development:</strong> Applying ChatGPT in professional settings for communication, problem-solving, and innovation.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 08: Useful ChatGPT Prompts</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Practical Prompts:</strong> A collection of effective ChatGPT prompts for various uses.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 09: Social Media Marketing with ChatGPT</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Marketing Strategies:</strong> Leveraging ChatGPT for content creation, customer engagement, and campaign management.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 10: ChatGPT Plus and New Bing</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Advanced Features:</strong> Exploring the enhanced capabilities and applications of ChatGPT Plus and New Bing.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 11: ChatGPT in Personal Life</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Everyday Uses:</strong> Practical ways to incorporate ChatGPT into daily personal tasks and activities.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 12: The Future with ChatGPT</h4><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Looking Ahead:</strong> Discussing potential future developments in ChatGPT technology and its implications for various sectors.</li></ul>

RapidEDX

AI (Artificial Intelligence)

ChatGPT

ChatGPT Masterclass

<p>Jumpstart your Data Analytics Career with Tableau by solving Realistic Analytics Projects. Learn Data Visualisation!</p><p>Tableau is a powerful tool for analysing data and uncovering insights. It provides an easy-to-use interface that allows users to quickly create visualisations and dashboards to explore data. Tableau is a great tool for data exploration and discovery, as it allows users to quickly analyse large datasets and uncover patterns and trends.</p><p>In this course, You will construct six distinct dashboards in this course that each represent a<strong> real-world </strong>issue. You'll discover how to connect to a range of data sources, including <strong>cloud servers and Excel files</strong>. Additionally, you'll discover how to create interactive dashboards and upload them to Tableau Online to demonstrate your abilities to potential companies and link these in your resume.</p><p><strong>What you'll learn</strong></p><ul><li>You'll comprehend the inner workings of the data analytics pipeline by the end of this course, from merging, filtering, and extracting data to creating dashboards.</li><li>Create five interactive dashboards and display them online to build a strong work history for your resume.</li><li>Connect to several data sources, including servers, Google Sheets, and Excel!</li><li>Be able to use maps, line charts, donut charts, and bar charts to create in-depth analyses.</li><li>Gain a thorough knowledge of how Tableau's calculations operate.</li><li>Learning how to join data sources in Tableau and the many sorts of joins</li><li>Engage in set-based work and level-of-detail calculations</li><li>Recognise how Tableau's Groups and Hierarchies Operate.</li></ul><p>When you finish this course, you'll be an expert user of <strong>Tableau </strong> who can apply all you've learned to advance your career in data and analytics.</p><p><strong>Key Benefits</strong></p><ul><li>Learning materials of the Design course contain engaging voiceover and visual elements for your comfort.</li><li>Get 24/7 access to all content for a full year.</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li></ul><p>This course is designed with the following modules:</p><p><strong>***Introduction to Data Analytics with Tableau***</strong></p><ul><li>Module 01:Introduction to the Course</li><li>Module 02:Project 1: Discount Mart (Sales and Profit Analytics)</li><li>Module 03:Project 2: Green Destinations (HR Analytics)</li><li>Module 04:Project 3: Superstore (Sales Agent Tracker)</li><li>Module 05:Northwind Trade (Shipping Analytics)</li><li>Module 06:Project 5: Tesla (Stock Price Analytics)</li><li>Module 07:Bonus: Introduction to Database Concepts</li><li>Module 08:Tableau Stories</li></ul><p><strong>Course Assessment </strong></p><p>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%.</p><p>When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme.</p><p><strong>Certificate of Achievement</strong></p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After successfully completing the Diploma in Data Analytics with Tableau course, learners will be able to order an endorsed certificate as proof of their achievement. Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying</p><ul><li>Within the UK: £119</li><li>International: £119 + £10 (postal charge) = £129</li><li>CPD Accredited Certification from One Education</li></ul><p>After successfully completing this Diploma in Data Analytics with Tableau course, you will qualify for the CPD accredited certificate from One Education. Certification is available in both PDF &amp; hardcopy format, which can be received by paying -</p><ul><li>PDF Certificate: £9</li><li>Hardcopy Certificate (within the UK): £15</li><li>Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        150 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>This course is for anyone who wants to kickstart their career in Data Analytics.</li><li>This course is for anyone who wants to learn more about Tableau</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Diploma in Data Analytics with Tableau course is open to all, with no formal entry requirements.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After successfully completing this course, you can develop your confidence and pursue possibilities such as-</p><ul><li>Data analytics manager</li><li>Financial analyst</li><li>Scientist</li><li>Machine learning engineer</li></ul><p>The salary range for data analysts in the UK is £23,000 to £62,000 per year.</p>
                        </div>

Diploma in Data Analytics with Tableau Course

<p>In a world where artificial intelligence is becoming increasingly important, learning the skills needed to build your own neural networks is more crucial than ever. Imagine having the ability to create your own neural network and train it to perform complex tasks. With our Project on Deep Learning - Artificial Neural Network course, you can do just that.</p><p>In this course, you'll learn how to build your own artificial neural network from scratch, using Python. From installing the necessary packages to data preprocessing and encoding, you'll discover how to prepare your data for neural network training. You'll also gain a deeper understanding of the steps involved in building your own artificial neural network and learn how to make predictions using your model.</p><p>So if you're ready to unlock the power of deep learning and take your skills to the next level, enrol in our Project on Deep Learning - Artificial Neural Network course today and start building your own neural networks.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a solid understanding of artificial neural networks and their applications in deep learning.</li><li>Learn how to install the necessary packages and preprocess data for neural network training.</li><li>Discover how to encode data and build your own artificial neural network using Python.</li><li>Understand the steps involved in making predictions using your neural network model.</li><li>Learn how to deal with imbalanced data in your neural network training.</li></ul><p>The Project on Deep Learning - Artificial Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network and perform complex tasks using deep learning. You'll learn how to install the necessary packages, preprocess data, and encode data for neural network training. You'll also gain a deeper understanding of artificial neural networks and learn how to build your own model using Python. By the end of the course, you'll be able to make predictions using your neural network model and understand how to deal with imbalanced data in your training.</p><p><strong>â±â± Project on Deep Learning - Artificial Neural Network</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02:</strong> ANN Installation<strong><br /></strong></li><li><strong>Section 03: </strong>Data Preprocessing<strong><br /></strong></li><li><strong>Section 04: </strong>Data Encoding<strong><br /></strong></li><li><strong>Section 05:</strong> Steps to Build ANN<strong><br /></strong></li><li><strong>Section 06:</strong> Predictions and Imbalance-Learn</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />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.<br /><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Data analysts who want to expand their skills in deep learning and artificial neural networks.</li><li>Programmers who want to learn how to build their own neural network models for advanced tasks.</li><li>Entrepreneurs who want to develop their own deep learning-based applications.</li><li>Students who want to enhance their skills in deep learning and prepare for a career in the field.</li><li>Anyone who wants to explore the world of artificial neural networks and deep learning projects.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £24,000 - £45,000</li><li>Machine Learning Engineer: £28,000 - £65,000</li><li>Deep Learning Engineer: £30,000 - £75,000</li><li>Technical Lead: £40,000 - £90,000</li><li>Chief Technology Officer: £90,000 - £250,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Build Structures in Portuguese Course

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to<br />Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras<br />Apply modern solutions to a wide range of applications such as object detection and video analysis<br />Run your models on mobile devices and web pages and improve their performance.<br />Create your own neural networks from scratch<br />Classify images with modern architectures including Inception and ResNet<br />Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net<br />Tackle problems faced when developing self-driving cars and facial emotion recognition systems<br />Boost your application's performance with transfer learning, GANs, and domain adaptation<br />Use recurrent neural networks (RNNs) for video analysis<br />Optimize and deploy your networks on mobile devices and in the browser</p><p>Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.<br />This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.</p></div><div id="course-pills-2"><h4>Computer Vision and Neural Networks </h4><ul><li>Computer Vision and Neural Networks </li><li>Technical requirements </li><li>Computer vision in the wild </li><li>A brief history of computer vision </li><li>Getting started with neural networks </li></ul><h4>TensorFlow Basics and Training a Model </h4><ul><li>TensorFlow Basics and Training a Model </li><li>Technical requirements </li><li>Getting started with TensorFlow 2 and Keras </li><li>TensorFlow 2 and Keras in detail </li><li>The TensorFlow ecosystem </li></ul><h4>Modern Neural Networks </h4><ul><li>Modern Neural Networks </li><li>Technical requirements </li><li>Discovering convolutional neural networks </li><li>Refining the training process </li></ul><h4>Influential Classification Tools </h4><ul><li>Influential Classification Tools </li><li>Technical requirements </li><li>Understanding advanced CNN architectures </li><li>Leveraging transfer learning </li></ul><h4>Object Detection Models </h4><ul><li>Object Detection Models </li><li>Technical requirements </li><li>Introducing object detection </li><li>A fast object detection algorithm ? YOLO </li><li>Faster R-CNN ? a powerful object detection model </li></ul><h4>Enhancing and Segmenting Images </h4><ul><li>Enhancing and Segmenting Images </li><li>Technical requirements </li><li>Transforming images with encoders-decoders </li><li>Understanding semantic segmentation </li></ul><h4>Training on Complex and Scarce Datasets </h4><ul><li>Training on Complex and Scarce Datasets </li><li>Technical requirements </li><li>Efficient data serving </li><li>How to deal with data scarcity </li></ul><h4>Video and Recurrent Neural Networks </h4><ul><li>Video and Recurrent Neural Networks </li><li>Technical requirements </li><li>Introducing RNNs </li><li>Classifying videos </li></ul><h4>Optimizing Models and Deploying on Mobile Devices </h4><ul><li>Optimizing Models and Deploying on Mobile Devices </li><li>Technical requirements </li><li>Optimizing computational and disk footprints </li><li>On-device machine learning </li><li>Example app ? recognizing facial expressions </li></ul></div>

Nexus Human

Computing Skills

Computer Vision

TensorFlow

Deep Learning

Hands-On Computervision with TensorFlow 2 (TTML6900)

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			<h2>Overview
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					<p><span>This comprehensive course on <strong>Microsoft Azure Cloud Concepts </strong></span><span>will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <strong>Microsoft Azure Cloud Concepts</strong></span><b> </b><span>comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.   </span></p><p><span>So enrol in this course today to fast-track your career ladder.</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>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.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <strong>Microsoft Azure Cloud Concepts</strong></span><span>. It is available to all students, of all academic backgrounds.</span></p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our <strong>Microsoft Azure Cloud Concepts</strong></span><span> 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.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>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-</span></p><ul><li><span>Open doors of opportunities</span></li><li><span>Increase your adaptability</span></li><li><span>Keep you relevant</span></li><li><span>Boost confidence</span></li></ul><p><span>And much more!</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>1 sections • 29 lectures • 03:33:00 total length</p></div><div><div><div><div><div><div><div><p>•Unit 01: What will you learn and Cloud Concepts: 00:18:00</p></div><div><div><p>•Unit 02: Core Azure architectural components: 00:18:00</p></div><div><div><p>•Unit 03: LAB Create a Windows and Linux VM Computer: 00:10:00</p></div><div><div><p>•Unit 04: LAB Container creation: 00:04:00</p></div><div><div><p>•Unit 05: Storage with Azure: 00:04:00</p></div><div><div><p>•Unit 06: LAB Create a storage account: 00:07:00</p></div><div><div><p>•Unit 07: Network concepts: 00:03:00</p></div><div><div><p>•Unit 08: Lab Network Peering: 00:16:00</p></div><div><div><p>•Unit 09: Lab scale set: 00:11:00</p></div><div><div><p>•Unit 10: Marketspace and Serverless: 00:07:00</p></div><div><div><p>•Unit 11: Event HUB and Logic APPS: 00:07:00</p></div><div><div><p>•Unit 12: DevOps Overview: 00:04:00</p></div><div><div><p>•Unit 13: Azure Databases Overview: 00:04:00</p></div><div><div><p>•Unit 14: Lab SQL: 00:08:00</p></div><div><div><p>•Unit 15: What are AI and machine learning: 00:10:00</p></div><div><div><p>•Unit 16: Powershell and CLI: 00:09:00</p></div><div><div><p>•Unit 17: Azure Advisor: 00:05:00</p></div><div><div><p>•Unit 18: Review Core Azure: 00:04:00</p></div><div><div><p>•Unit 19: Azure security compliance and trust: 00:03:00</p></div><div><div><p>•Unit 20: Lab DDOS and NSGs: 00:07:00</p></div><div><div><p>•Unit 21: Authentication and Authorisation: 00:07:00</p></div><div><div><p>•Unit 22: Azure security centre: 00:06:00</p></div><div><div><p>•Unit 23: LAB Azure key vault and AIP overview: 00:06:00</p></div><div><div><p>•Unit 24: Azure Advanced Threat Protection (Azure ATP): 00:06:00</p></div><div><div><p>•Unit 25: Azure monitoring: 00:05:00</p></div><div><div><p>•Unit 26: Manage Azure Governance: 00:07:00</p></div><div><div><p>•Unit 27: Azure privacy and compliance: 00:04:00</p></div><div><div><p>•Unit 28: Summary: 00:03:00</p></div><div><div><p>•Unit 29: Azure Pricing and support: 00:10:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Microsoft AZ-900

AWS Certified Cloud Practitioner

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Microsoft Azure Cloud Concepts

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Packt

Python

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

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Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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