Name: Machine Learning Model Using AWS SageMaker Canvas Course
Price: 12 GBP
Availability: InStock

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.</strong>
        <p>Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.

TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.

In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock 'price' predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks.

By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.</p>
        <h2>What You Will Learn</h2><p>Learn about simple RNNs (Elman unit)<br>Covers GRU (gated recurrent unit)<br>Learn how to use LSTM (long short-term memory unit)<br>Learn how to preform time series forecasting<br>Learn how to predict stock price and stock return with LSTM<br>Learn how to apply RNNs to NLP</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks. The course is well-balanced with theory that explains the RNN concepts and hands-on coding exercises for practical understanding. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.</p>
        <h2>Key Features</h2><p>Build your own RNNs with TensorFlow 2 * Explains RNNs, time series, and sequence data * Preform text preprocessing and text classification with LSTMs</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will get introduced to recurrent neural networks and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Recurrent Neural Networks (RNNs), Time Series, and Sequence Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Sequence Data</p><p style='margin: 0'>In this video, you will learn about sequence data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forecasting</p><p style='margin: 0'>In this video, we will discuss about forecasting.</p></td></tr><tr><td style='padding: 15px;'><p>3. Autoregressive Linear Model for Time Series Prediction</p><p style='margin: 0'>In this video, we will dive into coding and learn about the autoregressive linear model for time series prediction.</p></td></tr><tr><td style='padding: 15px;'><p>4. Proof That the Linear Model Works</p><p style='margin: 0'>In this video, you will learn how the linear model is about to forecast for a sine wave.</p></td></tr><tr><td style='padding: 15px;'><p>5. Recurrent Neural Networks (Elman Unit Part 1)</p><p style='margin: 0'>In this video, we will get introduced to simple recurrent neural networks also called as Elman Unit.</p></td></tr><tr><td style='padding: 15px;'><p>6. Recurrent Neural Networks (Elman Unit Part 2)</p><p style='margin: 0'>In this video, you will learn how to use the simple RNN to solve many-to-one tasks and many-to-many problems.</p></td></tr><tr><td style='padding: 15px;'><p>7. RNN Code Preparation</p><p style='margin: 0'>In this video, we will understand how to write code in TensorFlow 2 for a simple RNN.</p></td></tr><tr><td style='padding: 15px;'><p>8. RNN for Time Series Prediction</p><p style='margin: 0'>In this video, we will do the same forecasting exercise that we did with an autoregressive liner model, but with a simple RNN instead.</p></td></tr><tr><td style='padding: 15px;'><p>9. Paying Attention to Shapes</p><p style='margin: 0'>In this video, we will work on shapes in RNN.</p></td></tr><tr><td style='padding: 15px;'><p>10. GRU and LSTM (Part 1)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called GRU.</p></td></tr><tr><td style='padding: 15px;'><p>11. GRU and LSTM (Part 2)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called LSTM.</p></td></tr><tr><td style='padding: 15px;'><p>12. A More Challenging Sequence</p><p style='margin: 0'>In this video, you will learn how to preform time series prediction with a complex signal.</p></td></tr><tr><td style='padding: 15px;'><p>13. Demo of the Long-Distance Problem</p><p style='margin: 0'>In this video, we will understand how LSTMs work for resolving a long-distance problem.</p></td></tr><tr><td style='padding: 15px;'><p>14. RNN for Image Classification (Theory)</p><p style='margin: 0'>In this video, we will understand how to apply RNN for image classification.</p></td></tr><tr><td style='padding: 15px;'><p>15. RNN for Image Classification (Code)</p><p style='margin: 0'>In this video, you will learn how to preform RNN for image classification on the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>16. Stock Return Predictions Using LSTMs (Part 1)</p><p style='margin: 0'>In this video, we will understand why one-step prediction on stock price is misleading and unconventional.</p></td></tr><tr><td style='padding: 15px;'><p>17. Stock Return Predictions Using LSTMs (Part 2)</p><p style='margin: 0'>In this video, we will work on a more conventional prediction called stock return.</p></td></tr><tr><td style='padding: 15px;'><p>18. Stock Return Predictions Using LSTMs (Part 3)</p><p style='margin: 0'>In this video, we will make use of all the data and try to predict whether the price will go up or down.</p></td></tr><tr><td style='padding: 15px;'><p>19. Other Ways to Forecast</p><p style='margin: 0'>In this video, we will discuss some other ways that might generate multi-step forecast.</p></td></tr><tr><td style='padding: 15px;'><p>20. Suggestion Box</p><p style='margin: 0'>In this video, the author shares how you can get in touch with him through the suggestion box.</p></td></tr></tbody></table><br><h4>3. Natural Language Processing (NLP)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Embeddings</p><p style='margin: 0'>In this video, we will understand what embedding is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (NLP)</p><p style='margin: 0'>In this video, you will learn how to write the code to turn a sequence of words into an acceptable format such that they can be converted into a matrix of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>3. Text Preprocessing</p><p style='margin: 0'>In this video, we will work on text preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification with LSTMs</p><p style='margin: 0'>In this video, we will work on text classification with LSTMs.</p></td></tr></tbody></table>

Packt

Data Science

Python

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

Deep Learning - Recurrent Neural Networks with TensorFlow

General Admission

<strong>If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.</strong>
        <p>If you are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course takes a holistic approach to teach visualization techniques.

We will be taking real-life business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare the raw data to suit the appropriate visualization needs. You will learn about data analytics and exploratory data analysis techniques using multiple different data structures with NumPy and Pandas libraries. You will also learn various chart/graph types, customization/configuration, and vectorization techniques.

We will look at advanced visualizations using business applications such as single and multiple bar charts, pie charts, and bubble charts with the vectorization of properties. We will further explore Seaborn Boxplot, Violin plot, Categorical Scatterplot, and how to create heat maps.

By the end of the course, you will learn the foundational techniques of data analytics and deeper customizations on visualizations. You will be able to confidently use Python visualization libraries such as Matplotlib, Seaborn, and Bokeh in your future projects.

All resources and code files are placed here: https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations
</p>
        <h2>What You Will Learn</h2><p>Learn about the various visualization concepts<br>Learn to create simple plots using Matplotlib<br>Learn about marginal histograms and marginal boxplots<br>Learn handling images using pixel metrics<br>Learn about categorical variables and histograms (with EDA)<br>Learn various data generation techniques</p>
        <h2>Audience</h2><p>This course is for Python and machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial to leaders, managers, and anyone whose job involves presenting data in the form of visuals, which include developers, architects, and system analysts.<br><br>A basic understanding of Python will be helpful, but not mandatory.</p>
        <h2>Approach</h2><p>This practical hands-on course has easy, step-by-step explanations with code to draw over 20 diverse kinds of charts and graphs using Python. Extensive quizzes are infused at logical points to validate the learning effectiveness.</p>
        <h2>Key Features</h2><p>The art of presenting data in the form of powerful, innovative, and intuitive visualizations * In-depth coverage of Matplotlib, Seaborn, and Bokeh visualization libraries * Use of data analytics techniques/Exploratory Data Analysis (EDA) using several data generations and manipulation methods</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Analytics-using-Python-Visualizations</p>
        <h2>About the Author</h2><h3>Manas Dasgupta</h3><p>Manas Dasgupta holds a master's degree (MSc) from the Liverpool John Moore's University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on.

His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods.

He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.</p>
        <h2>Course Outline</h2><h4>1. Matplotlib and Seaborn - Libraries and Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Promotional Video</p><p style='margin: 0'>This video explains the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Author Introduction</p><p style='margin: 0'>This video introduces you to the author.</p></td></tr><tr><td style='padding: 15px;'><p>3. What You Will Learn</p><p style='margin: 0'>This video explains what the course outcomes are.</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualization Concepts</p><p style='margin: 0'>This video explains visualization concepts.</p></td></tr><tr><td style='padding: 15px;'><p>5. Introduction to Matplotlib</p><p style='margin: 0'>This video explains Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>6. Creating Simple Plots Using Matplotlib</p><p style='margin: 0'>This video explains how to create simple plots using Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>7. Creating Scatter Plots</p><p style='margin: 0'>This video explains creating scatter plots.</p></td></tr><tr><td style='padding: 15px;'><p>8. Creating Axis Limits</p><p style='margin: 0'>This video explains how to create axis limits.</p></td></tr><tr><td style='padding: 15px;'><p>9. Parameterizing Plots</p><p style='margin: 0'>This video explains parameterizing plots.</p></td></tr><tr><td style='padding: 15px;'><p>10. Creating Error Bars</p><p style='margin: 0'>This video explains creating error bars.</p></td></tr><tr><td style='padding: 15px;'><p>11. Plotting Histograms and Box Plots</p><p style='margin: 0'>This video explains plotting histograms and box plots.</p></td></tr><tr><td style='padding: 15px;'><p>12. Plotting 2D Histograms</p><p style='margin: 0'>This video explains plotting 2D histograms.</p></td></tr><tr><td style='padding: 15px;'><p>13. Marginal Histograms and Marginal Boxplots</p><p style='margin: 0'>This video explains marginal histograms and marginal boxplots.</p></td></tr><tr><td style='padding: 15px;'><p>14. Working with Subplots</p><p style='margin: 0'>This video explains working with subplots.</p></td></tr><tr><td style='padding: 15px;'><p>15. Stock Trend / Time Series Plot and Annotations</p><p style='margin: 0'>This video explains stock trend / time series plot and annotations.</p></td></tr><tr><td style='padding: 15px;'><p>16. Plotting Images and Clustering</p><p style='margin: 0'>This video explains plotting images and clustering.</p></td></tr><tr><td style='padding: 15px;'><p>17. Creating 2D Contour plots for 3D Data</p><p style='margin: 0'>This video explains creating 2D contour plots for 3D data.</p></td></tr><tr><td style='padding: 15px;'><p>18. Creating 3D Plots Including 3D Contours</p><p style='margin: 0'>This video explains creating 3D plots including 3D contours.</p></td></tr><tr><td style='padding: 15px;'><p>19. Stylesheets, rcParam, and Custom Stylesheets</p><p style='margin: 0'>This video explains stylesheets, rcParam, and custom stylesheets.</p></td></tr></tbody></table><br><h4>2. Advanced Visualizations Using Business Applications</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Single and Multiple Bar Charts</p><p style='margin: 0'>This video explains single and multiple bar charts.</p></td></tr><tr><td style='padding: 15px;'><p>2. Area and Stacked-Area Charts</p><p style='margin: 0'>This video explains area and stacked-area charts.</p></td></tr><tr><td style='padding: 15px;'><p>3. Drawing Pie Charts</p><p style='margin: 0'>This video explains drawing pie charts.</p></td></tr><tr><td style='padding: 15px;'><p>4. Bubble Charts with Vectorization of Properties</p><p style='margin: 0'>This video explains bubble charts with vectorization of properties.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Regression Lines with OLS (ML)</p><p style='margin: 0'>This video explains plotting regression lines with OLS (ML).</p></td></tr><tr><td style='padding: 15px;'><p>6. Categorical Variables and Histograms (with EDA)</p><p style='margin: 0'>This video explains categorical variables and histograms.</p></td></tr><tr><td style='padding: 15px;'><p>7. Seaborn Boxplot, Violin plot, Categorical Scatterplot</p><p style='margin: 0'>This video explains Seaborn Boxplot, Violin plot, and Categorical Scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>8. Seaborn Slopeplots for Comparing Distributions</p><p style='margin: 0'>This video explains Seaborn Slopeplots for comparing distributions.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dumbbell Plot for Category-Wise Value Movement</p><p style='margin: 0'>This video explains Dumbbell plot for category-wise value movement.</p></td></tr><tr><td style='padding: 15px;'><p>10. Creating Heatmaps</p><p style='margin: 0'>This video explains creating heatmaps.</p></td></tr><tr><td style='padding: 15px;'><p>11. Working with Pairplots</p><p style='margin: 0'>This video explains working with Pairplots.</p></td></tr><tr><td style='padding: 15px;'><p>12. Seasonal Trendcharts</p><p style='margin: 0'>This video explains seasonal Trendcharts.</p></td></tr><tr><td style='padding: 15px;'><p>13. Yearplot and Calendarplot for Color-Scaled Trends</p><p style='margin: 0'>This video explains Yearplot and Calendarplot for color-scaled trends.</p></td></tr><tr><td style='padding: 15px;'><p>14. Radarplot to Compare Scores of Multiple Parameters</p><p style='margin: 0'>This video explains Radarplot to compare scores of multiple parameters.</p></td></tr></tbody></table><br><h4>3. Working with the Beautiful and Powerful Bokeh Library</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Bokeh</p><p style='margin: 0'>This video introduces you to Bokeh.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating Simple and Multiple Line Plots</p><p style='margin: 0'>This video explains how to create simple and multiple line plots.</p></td></tr><tr><td style='padding: 15px;'><p>3. Customizing Your Plots</p><p style='margin: 0'>This video explains how to customize your plots.</p></td></tr><tr><td style='padding: 15px;'><p>4. Creating Bubble Plots - Vectorizing Your Plot</p><p style='margin: 0'>This video explains how to create bubble plots - vectorizing your plot.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Layouts - Row / Column / Grid</p><p style='margin: 0'>This video explains working with layouts - row / column / grid.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using the ColumnDataSource Object</p><p style='margin: 0'>This video explains using the ColumnDataSource object.</p></td></tr><tr><td style='padding: 15px;'><p>7. Applying Filters - IndexFilter, BooleanFilter, GroupFilter</p><p style='margin: 0'>This video explains applying filters - IndexFilter, BooleanFilter, GroupFilter.</p></td></tr><tr><td style='padding: 15px;'><p>8. Widgets - Dynamic Plot Controls</p><p style='margin: 0'>This video explains widgets - dynamic plot controls.</p></td></tr><tr><td style='padding: 15px;'><p>9. Plotting on a Google Map Using Google Map API</p><p style='margin: 0'>This video explains plotting on a Google Map using Google Map API.</p></td></tr><tr><td style='padding: 15px;'><p>10. Closing Notes</p><p style='margin: 0'>This video wraps up the course.</p></td></tr></tbody></table>

Analytics

Data Visualization

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

Data Analytics Using Python Visualizations

Studyhub UK

Online Course Marketing

Programming Fundamentals

Trainers Course

Complete Python Machine Learning & Data Science Fundamentals

<p>According to recent studies, the demand for data analysis and data science professionals is on the rise, with the industry expected to grow by almost 30% in the next decade. In today's digital world, having the skills to analyse and interpret data has never been more crucial, and it's never too late to start learning. In the UK alone, there are over 100,000 job openings in data analysis and data science, with salaries ranging from £40,000 to £100,000 per year.</p><p>This is why we are proud to introduce the Data Analysis, Data Analytics with Tableau &amp; Data Science &amp; Machine Learning with R bundle, the ultimate learning experience for anyone looking to boost their skills in this field. This bundle is a testament to the high quality of education that can be achieved through QLS-endorsed courses. With three such courses included in this bundle, learners are sure to receive a top-notch education that is both informative and engaging.</p><p>Investing in your education is always a wise choice, and with 3 QLS Endorsed certificates to your name, you can be sure that you're double boosting your resume. Apart from the QLS courses, the learners will get access to 10 CPD courses that are in line with the subject matter. These will enhance your overall understanding as well as broaden your knowledge.</p><p>Whether you're a beginner or have some experience in the field, this bundle has something for everyone. So why wait? Start your journey to a successful career in data analysis and data science today!</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Data Analysis Fundamentals at QLS Level 5</li><li><strong>Course 02:</strong> Advanced Diploma in Data Analytics with Tableau at QLS Level 6</li><li><strong>Course 03:</strong> Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04: </strong>Microsoft Excel - Beginner, Intermediate &amp; Advanced</li><li><strong>Course 05:</strong> Conditional Formatting in Excel</li><li><strong>Course 06:</strong> Excel Lesson for Beginners: Create Pivot Table</li><li><strong>Course 07:</strong> Data Center Training Essentials: General Introduction</li><li><strong>Course 08:</strong> Google Data Studio: Data Analytics</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>learning outcomes </strong></p><ul><li>Understanding of basic data analysis techniques and tools</li><li>Knowledge of data visualisation and reporting using Tableau</li><li>Ability to perform data analysis using R programming language</li><li>Understanding of data science and machine learning algorithms</li><li>Ability to create predictive models and interpret results</li></ul><p><strong>Basic Data Analysis</strong>: This course provides an introduction to data analysis techniques and tools. You'll learn how to collect, clean, and analyse data, as well as how to interpret results and create meaningful visualisations.</p><p><strong>Introduction to Data Analytics with Tableau:</strong> In this course, you'll learn how to use Tableau to analyse and visualise data. You'll learn how to connect to data sources, create reports, and publish interactive dashboards.</p><p><strong>2021 Data Science &amp; Machine Learning with R from A-Z: </strong>This comprehensive course covers data science and machine learning using R programming language. You'll learn how to perform data analysis, create predictive models, and interpret results.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        30 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>Business professionals and managers who want to improve their data analysis skills</li><li>Marketing and sales professionals who want to gain a deeper understanding of customer behaviour and preferences</li><li>IT professionals who want to specialise in data analysis and machine learning</li><li>Data analysts and data scientists who want to expand their skill set</li><li>Anyone who is interested in learning about data analysis, data science, and machine learning.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £40,000 to £70,000</li><li>Data Scientist: £50,000 to £100,000</li><li>Business Intelligence Analyst: £45,000 to £80,000</li><li>Data Engineer: £55,000 to £100,000</li><li>Data and Analytics Manager: £70,000 to £120,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

One Education

Data Analysis,  Data Analytics with Tableau & Data Science & Machine Learning with R Course

NextGen Learning

Statistics & Probability for Data Science & Machine Learning Course

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					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Data Science &amp; Machine Learning with R Complete Training has been specially designed to help learners gain a good command of Data Science &amp; Machine Learning with R Complete Training, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Data Science &amp; Machine Learning with R Complete Training, you will gain both practical and theoretical understanding of Data Science &amp; Machine Learning with R Complete Training that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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										<span>Familiar yourself with the recent development and updates of the relevant industry</span>
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										<span>Know how to use your theoretical knowledge to adapt in any working environment</span>
									</li>
								<li>
											<span>
													</span>
										<span>Get help from our expert tutors anytime you need</span>
									</li>
								<li>
											<span>
													</span>
										<span>Access to course contents that are designed and prepared by industry professionals</span>
									</li>
								<li>
											<span>
													</span>
										<span>Study at your convenient time and from wherever you want</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
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			</div>
		</section>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
									</li>
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											<span>
													</span>
										<span>You will receive a completion certificate upon completing the course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
									</li>
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											<span>
													</span>
										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
									</li>
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											<span>
													</span>
										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
									</li>
						</ul>
				</div>
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			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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											<span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
									</li>
						</ul>
				</div>
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			</div>
		</div>
						</div>
			</div>
		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Data Science and Machine Learning Course Intro</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Introduction</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>What is Data Science</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Overview</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Who is This Course for</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Marketplace</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Job Opportunities</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Getting Started with R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Basics</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Files</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>RStudio</td><td><span>🕐 00:07:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Tidyverse</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Resources</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Types and Structures in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Unit Introduction</td><td><span>🕐 00:30:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Basic Type</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vector Part One</td><td><span>🕐 00:20:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors Part Two</td><td><span>🕐 00:25:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Missing Values</td><td><span>🕐 00:16:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Coercion</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Naming</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Misc</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Matrics</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>List</td><td><span>🕐 00:32:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to Data Frames</td><td><span>🕐 00:19:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Data Frames</td><td><span>🕐 00:20:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Frames: Helper Functions</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Frames Tibbles</td><td><span>🕐 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Intermediate R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Intermediate Introduction</td><td><span>🕐 00:47:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Relational Operations</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Conditional Statements</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Loops</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Functions</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Packages</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Factors</td><td><span>🕐 00:28:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Dates and Times</td><td><span>🕐 00:30:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Functional Programming</td><td><span>🕐 00:37:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Import or Export</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Database</td><td><span>🕐 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Manipulation in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Manipulation in R Introduction</td><td><span>🕐 00:36:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Tidy Data</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Pipe Operator</td><td><span>🕐 00:15:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Filter Verb</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Select Verb</td><td><span>🕐 00:46:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Mutate Verb</td><td><span>🕐 00:32:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Arrange Verb</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Summarize Verb</td><td><span>🕐 00:23:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Pivoting</td><td><span>🕐 00:43:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>JSON Parsing</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>String Manipulation</td><td><span>🕐 00:33:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Web Scraping</td><td><span>🕐 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Visualization in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Visualization in R Section Intro</td><td><span>🕐 00:17:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started</td><td><span>🕐 00:16:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Aesthetics Mappings</td><td><span>🕐 00:25:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Single Variable Plots</td><td><span>🕐 00:37:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Two Variable Plots</td><td><span>🕐 00:21:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Facets, Layering, and Coordinate Systems</td><td><span>🕐 00:18:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Styling and Saving</td><td><span>🕐 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Creating Reports with R Markdown</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating with R Markdown</td><td><span>🕐 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Building Webapps with R Shiny</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to R Shiny</td><td><span>🕐 00:26:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>A Basic R Shiny App</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Other Examples with R Shiny</td><td><span>🕐 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Introduction to Machine Learning</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Part 1</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Part 2</td><td><span>🕐 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Starting A Career in Data Science</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Starting a Data Science Career Section Overview</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science Resume</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started with Freelancing</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Top Freelance Websites</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Personal Branding</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Importance of Website and Blo</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Networking Do's and Don'ts</td><td><span>🕐 00:04:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Skill Up

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Data Science & Machine Learning with R Complete Training

<div>
					<div>
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		</div>
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		<div>
				<div>
				<div>
			<h4>Overview</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
							<p><span>By enroling in Spatial Data Visualization and Machine Learning in Python, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Spatial Data Visualization and Machine Learning in Python course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Spatial Data Visualization and Machine Learning in Python course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Spatial Data Visualization and Machine Learning in Python Program.</span></p><p><span>So enrol in our Spatial Data Visualization and Machine Learning in Python course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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				<div>
			<h4>Description</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
							<p><span>Enroling in this Spatial Data Visualization and Machine Learning in Python course can improve your Spatial Data Visualization and Machine Learning in Python perspective, regardless of your skill levels in the Spatial Data Visualization and Machine Learning in Python topics you want to master. If you're already a Spatial Data Visualization and Machine Learning in Python expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Spatial Data Visualization and Machine Learning in Python insights that will help you make the most of your time. This Spatial Data Visualization and Machine Learning in Python course will act as a guide for you if you've ever wished to excel at Spatial Data Visualization and Machine Learning in Python.</span></p>						</div>
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		</div>
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				</div>
		<div>
				<div>
				<div>
			<h4>Why Choose Us?</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>This course is accredited by the CPD Quality Standards.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Lifetime access to the whole collection of the learning materials.</span>
									</li>
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											<span>
													</span>
										<span>Online test with immediate results.</span>
									</li>
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											<span>
													</span>
										<span>Enroling in the course has no additional cost.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You can study and complete the course at your own pace.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
									</li>
						</ul>
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			<h4>Will I Receive A Certificate Of Completion?</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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			<h4>Who Is This Course For?</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
							<p><span>This Spatial Data Visualization and Machine Learning in Python course is a great place to start if you're looking to start a new career in Spatial Data Visualization and Machine Learning in Python field. This training is for anyone interested in gaining in-demand Spatial Data Visualization and Machine Learning in Python proficiency to help launch a career or their business aptitude. </span></p>						</div>
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			<h4>Requirements</h4>		</div>
				</div>
				</div>
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				<div>
				<div>
							<p><span>The Spatial Data Visualization and Machine Learning in Python course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Spatial Data Visualization and Machine Learning in Python, without any fuss.</span></p>						</div>
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			<h4>Career Path</h4>		</div>
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							<p><span>This Spatial Data Visualization and Machine Learning in Python training will assist you develop your Spatial Data Visualization and Machine Learning in Python ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Spatial Data Visualization and Machine Learning in Python professional story and personalise your path to a new career. Furthermore, developing this Spatial Data Visualization and Machine Learning in Python skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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				<h3>
	<span>Course Curriculum</span>
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					<td><span><i></i> 00:14:00</span></td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td><span><i></i> 00:24:00</span></td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:22:00</span></td>
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					<td><span><i></i> 00:31:00</span></td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td><span><i></i> 00:13:00</span></td>
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					<td>Adding predicted data to our plots - Part 2</td>
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					<td><span><i></i> 00:14:00</span></td>
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					<td>Adding predicted data to our plots - Part 3</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td>Adding the Grid Plot</td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td colspan="4">Section 07: Creating the Dashboard Server</td>
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					<td>Installing Visual Studio Code</td>
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					<td><span><i></i> 00:01:00</span></td>
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					<td>Creating the Project and Virtual Environment</td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td>Building and Running the Server</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:00:00</span></td>
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					<td colspan="4">Order Your Certificate</td>
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					<td>Order Your Certificate</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
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			</div>

Spatial Data Visualization and Machine Learning in Python Course

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Make the most of the plotting and AI capabilities of the world's benchmark programming language by taking this course to create </span><b>Spatial Data Visualisation and Machine Learning in Python Level 4. </b><span>Using the intuitive syntax available to you, you will be amazed at the results you can achieve with the power of its libraries and mapping potential for all manner of complex projects.  </span></p><p><span>This comprehensive </span>Python <span>tutorial is an excellent way to learn the important and potentially ground-breaking aspects of machine learning. With the benefit of expert guidance and step-by-step training, IT technology, you will be taken from quick installations to complex coding. You will learn how to become proficient with coding capabilities that will put you at the forefront of advanced programming techniques and the aptitude to envisage AI projects that will impress and be used for practical and useful purposes. </span></p></div>
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					<div><p>This best selling <strong>Spatial Data Visualization and Machine Learning in Python Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Spatial Data Visualization and Machine Learning in Python Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p>
<p>The <strong>Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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> Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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

Spatial Data Visualization and Machine Learning in Python Level 4

<p><strong>Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2</strong></p><p>The Machine Learning Model Using AWS SageMaker Canvas is all you need to advance your career in the relevant fields.</p><p>Enrol now to discover everything you need to know about the Machine Learning Model Using AWS SageMaker Canvas and the skills to improve your talents in this field and be confident in your knowledge bucket with One Education as your right hand!</p><p><strong>Expert Support</strong></p><p>Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course.</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><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>Quality License Endorsed</li><li>Certificate Available Upon Course Completion</li></ul><p><strong>Course Curriculum:</strong></p><p><strong><strong>Introduction to Machine Learning</strong></strong></p><ul><li>What is Machine Learning?</li></ul><p><strong><strong>Introduction to AWS</strong></strong></p><ul><li>What is Amazon Web Services (AWS)?</li><li>Signing into AWS Console</li></ul><p><strong><strong>Introduction to SageMaker</strong></strong></p><ul><li><strong>What is SageMaker, and how it is used for Machine Learning?</strong></li><li>What is SageMaker Canvas?</li></ul><p><strong><strong>Setup</strong></strong></p><ul><li>SageMaker Domain and User Setup</li><li>Setup Data in S3 Buckets for use in SageMaker</li></ul><p><strong><strong>SageMaker Canvas Interface Walkthrough</strong></strong></p><ul><li>Navigating in SageMaker Canvas</li></ul><p><strong><strong>Project 1: Banknote Authentication</strong></strong></p><ul><li>Adding Training Data</li><li>Building and Using Model for Prediction</li><li>Predict Single &amp; Batch Dataset</li><li>Validating Accuracy of Batch Predictions</li></ul><p><strong><strong>Project 2: Spam SMS Detection</strong></strong></p><ul><li>Adding Train &amp; Test Data</li><li>Building and Using Model for Prediction</li><li>Predicting Data and Validating Accuracy</li></ul><p><strong><strong>Project 3: Customer Churn Prediction</strong></strong></p><ul><li>Adding Data</li><li>Building Model</li><li>Performing &amp; Validating Predictions</li></ul><p><strong><strong>Project 4: Wine Quality Prediction</strong></strong></p><ul><li>Adding &amp; Joining Datasets</li><li>Building Model</li><li>Predicting Test Data</li></ul><p><strong><strong>Assignmen</strong>t</strong></p><ul><li>White Wine Quality Prediction</li></ul><p><strong><strong>Other Important Features in SageMaker Canvas</strong></strong></p><ul><li>Versioning</li></ul><p><strong><strong>Congratulations &amp; Next Steps</strong></strong></p><ul><li>Getting Datasets for Practice</li><li>Getting Help on SageMaker Canvas</li><li>Congratulations &amp; Thankyou</li></ul><p><strong>Exam and Assessment</strong></p><ul><li>MCQ based test</li><li>60% Marks to pass</li><li>Instant Assessment and Feedback</li></ul><p><strong>Certification</strong></p><ul><li>CPD Accredited PDF and Hardcopy Certificate</li><li>Level 2 QLS Endorsed Hardcopy Certificate for <strong>Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        120 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>This Machine Learning Model Using AWS SageMaker Canvas course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Machine Learning Model Using AWS SageMaker Canvas course is open to all, with no formal entry requirements.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon successful completion of the Machine Learning Model Using AWS SageMaker Canvas Course, learners will be equipped with many indispensable skills and have the opportunity to grab.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £69
        </p>
            <div> <p><strong>QLS Endorsed Certificate</strong></p><p>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: £69</li><li>International: £69 + £10 (postal charge) = £79 </li></ul><p><strong>CPD Accredited Certification from One Education</strong></p><ul><li>Hardcopy Certificate (within the UK): £15</li><li>Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</li></ul> </div>

                        </div>

Machine Learning Model Using AWS SageMaker Canvas Course

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p>The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.</p><p></p><p></p><p>This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. </p><p></p>Prerequisites<p>Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.   Specifically:  </p><ul> <li>Experience using computers and the internet.</li> <li>Interest in use cases for AI applications and machine learning models. </li> <li>A willingness to learn through hands-on exp...</li><p></p></ul></div><div id="course-pills-2"><h3>1 - Fundamental AI Concepts</h3> <ul> <li>Understand machine learning</li> <li>Understand computer vision</li> <li>Understand natural language processing</li> <li>Understand document intelligence and knowledge mining</li> <li>Understand generative AI</li> <li>Challenges and risks with AI</li> <li>Understand Responsible AI</li> </ul> <h3>2 - Fundamentals of machine learning</h3> <ul> <li>What is machine learning?</li> <li>Types of machine learning</li> <li>Regression</li> <li>Binary classification</li> <li>Multiclass classification</li> <li>Clustering</li> <li>Deep learning</li> <li>Azure Machine Learning</li> </ul> <h3>3 - Fundamentals of Azure AI services</h3> <ul> <li>AI services on the Azure platform</li> <li>Create Azure AI service resources</li> <li>Use Azure AI services</li> <li>Understand authentication for Azure AI services</li> </ul> <h3>4 - Fundamentals of Computer Vision</h3> <ul> <li>Images and image processing</li> <li>Machine learning for computer vision</li> <li>Azure AI Vision</li> </ul> <h3>5 - Fundamentals of Facial Recognition</h3> <ul> <li>Understand Face analysis</li> <li>Get started with Face analysis on Azure</li> </ul> <h3>6 - Fundamentals of optical character recognition</h3> <ul> <li>Get started with Vision Studio on Azure</li> </ul> <h3>7 - Fundamentals of Text Analysis with the Language Service</h3> <ul> <li>Understand Text Analytics</li> <li>Get started with text analysis</li> </ul> <h3>8 - Fundamentals of question answering with the Language Service</h3> <ul> <li>Understand question answering</li> <li>Get started with the Language service and Azure Bot Service</li> </ul> <h3>9 - Fundamentals of conversational language understanding</h3> <ul> <li>Describe conversational language understanding</li> <li>Get started with conversational language understanding in Azure</li> </ul> <h3>10 - Fundamentals of Azure AI Speech</h3> <ul> <li>Understand speech recognition and synthesis</li> <li>Get started with speech on Azure</li> </ul> <h3>11 - Fundamentals of Azure AI Document Intelligence</h3> <ul> <li>Explore capabilities of document intelligence</li> <li>Get started with receipt analysis on Azure</li> </ul> <h3>12 - Fundamentals of Knowledge Mining with Azure Cognitive Search</h3> <ul> <li>What is Azure Cognitive Search?</li> <li>Identify elements of a search solution</li> <li>Use a skillset to define an enrichment pipeline</li> <li>Understand indexes</li> <li>Use an indexer to build an index</li> <li>Persist enriched data in a knowledge store</li> <li>Create an index in the Azure portal</li> <li>Query data in an Azure Cognitive Search index</li> </ul> <h3>13 - Fundamentals of Generative AI</h3> <ul> <li>What is generative AI?</li> <li>Large language models</li> <li>What is Azure OpenAI?</li> <li>What are copilots?</li> <li>Improve generative AI responses with prompt engineering</li> </ul> <h3>14 - Fundamentals of Azure OpenAI Service</h3> <ul> <li>What is generative AI</li> <li>Describe Azure OpenAI</li> <li>How to use Azure OpenAI</li> <li>Understand OpenAI's natural language capabilities</li> <li>Understand OpenAI code generation capabilities</li> <li>Understand OpenAI's image generation capabilities</li> <li>Describe Azure OpenAI's access and responsible AI policies</li> </ul> <h3>15 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

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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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

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.</p><p></p><p></p><p>AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language.</p><p></p>Prerequisites<p>Before attending this course, students must have:  </p><li>Knowledge of Microsoft Azure and ability to navigate the Azure portal  </li><li>Knowledge of either C# or Python  </li><li>Familiarity with JSON and REST programming semantics  <br />  Recommended course prerequisites  </li><li>AI-900T00: Microsoft Azure AI Fundamentals course<p></p></li></div><div id="course-pills-2"><h3>1 - Prepare to develop AI solutions on Azure</h3> <ul> <li>Define artificial intelligence</li> <li>Understand AI-related terms</li> <li>Understand considerations for AI Engineers</li> <li>Understand considerations for responsible AI</li> <li>Understand capabilities of Azure Machine Learning</li> <li>Understand capabilities of Azure AI Services</li> <li>Understand capabilities of the Azure Bot Service</li> <li>Understand capabilities of Azure Cognitive Search</li> </ul> <h3>2 - Create and consume Azure AI services</h3> <ul> <li>Provision an Azure AI services resource</li> <li>Identify endpoints and keys</li> <li>Use a REST API</li> <li>Use an SDK</li> </ul> <h3>3 - Secure Azure AI services</h3> <ul> <li>Consider authentication</li> <li>Implement network security</li> </ul> <h3>4 - Monitor Azure AI services</h3> <ul> <li>Monitor cost</li> <li>Create alerts</li> <li>View metrics</li> <li>Manage diagnostic logging</li> </ul> <h3>5 - Deploy Azure AI services in containers</h3> <ul> <li>Understand containers</li> <li>Use Azure AI services containers</li> </ul> <h3>6 - Analyze images</h3> <ul> <li>Provision an Azure AI Vision resource</li> <li>Analyze an image</li> <li>Generate a smart-cropped thumbnail</li> </ul> <h3>7 - Classify images</h3> <ul> <li>Provision Azure resources for Azure AI Custom Vision</li> <li>Understand image classification</li> <li>Train an image classifier</li> </ul> <h3>8 - Detect, analyze, and recognize faces</h3> <ul> <li>Identify options for face detection analysis and identification</li> <li>Understand considerations for face analysis</li> <li>Detect faces with the Azure AI Vision service</li> <li>Understand capabilities of the face service</li> <li>Compare and match detected faces</li> <li>Implement facial recognition</li> </ul> <h3>9 - Read Text in images and documents with the Azure AI Vision Service</h3> <ul> <li>Explore Azure AI Vision options for reading text</li> <li>Use the Read API</li> </ul> <h3>10 - Analyze video</h3> <ul> <li>Understand Azure Video Indexer capabilities</li> <li>Extract custom insights</li> <li>Use Video Analyzer widgets and APIs</li> </ul> <h3>11 - Analyze text with Azure AI Language</h3> <ul> <li>Provision an Azure AI Language resource</li> <li>Detect language</li> <li>Extract key phrases</li> <li>Analyze sentiment</li> <li>Extract entities</li> <li>Extract linked entities</li> </ul> <h3>12 - Build a question answering solution</h3> <ul> <li>Understand question answering</li> <li>Compare question answering to Azure AI Language understanding</li> <li>Create a knowledge base</li> <li>Implement multi-turn conversation</li> <li>Test and publish a knowledge base</li> <li>Use a knowledge base</li> <li>Improve question answering performance</li> </ul> <h3>13 - Build a conversational language understanding model</h3> <ul> <li>Understand prebuilt capabilities of the Azure AI Language service</li> <li>Understand resources for building a conversational language understanding model</li> <li>Define intents, utterances, and entities</li> <li>Use patterns to differentiate similar utterances</li> <li>Use pre-built entity components</li> <li>Train, test, publish, and review a conversational language understanding model</li> </ul> <h3>14 - Create a custom text classification solution</h3> <ul> <li>Understand types of classification projects</li> <li>Understand how to build text classification projects</li> </ul> <h3>15 - Create a custom named entity extraction solution</h3> <ul> <li>Understand custom named entity recognition</li> <li>Label your data</li> <li>Train and evaluate your model</li> </ul> <h3>16 - Translate text with Azure AI Translator service</h3> <ul> <li>Provision an Azure AI Translator resource</li> <li>Specify translation options</li> <li>Define custom translations</li> </ul> <h3>17 - Create speech-enabled apps with Azure AI services</h3> <ul> <li>Provision an Azure resource for speech</li> <li>Use the Azure AI Speech to Text API</li> <li>Use the text to speech API</li> <li>Configure audio format and voices</li> <li>Use Speech Synthesis Markup Language</li> </ul> <h3>18 - Translate speech with the Azure AI Speech service</h3> <ul> <li>Provision an Azure resource for speech translation</li> <li>Translate speech to text</li> <li>Synthesize translations</li> </ul> <h3>19 - Create an Azure AI Search solution</h3> <ul> <li>Manage capacity</li> <li>Understand search components</li> <li>Understand the indexing process</li> <li>Search an index</li> <li>Apply filtering and sorting</li> <li>Enhance the index</li> </ul> <h3>20 - Create a custom skill for Azure AI Search</h3> <ul> <li>Create a custom skill</li> <li>Add a custom skill to a skillset</li> </ul> <h3>21 - Create a knowledge store with Azure AI Search</h3> <ul> <li>Define projections</li> <li>Define a knowledge store</li> </ul> <h3>22 - Plan an Azure AI Document Intelligence solution</h3> <ul> <li>Understand AI Document Intelligence</li> <li>Plan Azure AI Document Intelligence resources</li> <li>Choose a model type</li> </ul> <h3>23 - Use prebuilt Azure AI Document Intelligence models</h3> <ul> <li>Understand prebuilt models</li> <li>Use the General Document, Read, and Layout models</li> <li>Use financial, ID, and tax models</li> </ul> <h3>24 - Extract data from forms with Azure Document Intelligence</h3> <ul> <li>What is Azure Document Intelligence?</li> <li>Get started with Azure Document Intelligence</li> <li>Train custom models</li> <li>Use Azure Document Intelligence models</li> <li>Use the Azure Document Intelligence Studio</li> </ul> <h3>25 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>26 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>27 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>28 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>29 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>30 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul> <h3>31 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div>

Microsoft AZ-104

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

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