Name: Big Data - BCS Foundation Award
Price: 325 GBP
Availability: InStock

<strong>Let's learn the basic concepts for developing chatbots with machine learning models. This compact course will help you learn to use the power of Python to evaluate your chatbot datasets based on conversational notes, online resources, and websites. Garner hands-on practice in text generation with Python for chatbot development.</strong>
        <p>Chatbots are software applications used for online chat conversations through text or text-to-speech instead of providing direct contact with a live human agent. Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering.

This course begins with a brief overview of chatbots, their need, and the types of chatbots. We will explore rule-based versus self-learning chatbots. We will understand the working mechanism of chatbots. We will explore machine learning-based chatbots and understand the ML-based architecture of chatbots. You will learn about the purpose of ML-based chatbots and their impact. We will get an overview of the Natural Language Toolkit (NLTK). You will learn to install packages and create a corpus with Python. We will delve into text preprocessing and helper function deployment, generate responses, and implement term-frequency times inverse document-frequency. We will train and test rule-based chatbots and finally work on a project developing an artificial intelligence question-answer chatbot using NLTK.

Upon course completion, you will be able to relate the concepts and theories for chatbots in various domains, understand and implement machine learning models for building real-time chatbots, and evaluate machine learning models in chatbots.

All resources are available at: https://github.com/PacktPublishing/Basics-of-Chatbots-with-Machine-Learning-Python</p>
        <h2>What You Will Learn</h2><p>Learn about chatbot types, rule-based and self-learning chatbots<br>Learn text preprocessing and develop helper functions with Python<br>Explore the impact and overview of the Natural Language Toolkit<br>Gain hands-on practice, generate text in Python to develop chatbots<br>Explore testing and training of chatbot with machine learning<br>Implement term-frequency times inverse document-frequency hands-on</p>
        <h2>Audience</h2><p>This course delivers content to people wishing to advance their skills in applied machine learning, master data analysis with machine learning, build customized chatbots for their applications, and implement machine learning algorithms for chatbots. This course is for you if you are passionate about rule-based and conversational chatbots. Machine learning practitioners, research scholars, and data scientists can benefit from the course. No prior knowledge of chatbots, or machine learning, is needed. You will need to know basic to intermediate Python coding, which is not taught separately in the course.</p>
        <h2>Approach</h2><p>The course is designed to help you understand concepts easily and provides a unique hands-on experience in developing complete chatbots for your customized dataset using various projects. This well-structured learning-by-doing course will help you master the concepts and methodology efficiently. This course is easily understandable, expressive, self-explanatory, and concise, with live coding.</p>
        <h2>Key Features</h2><p>Learn chatbot basics, rule- and self-learning chatbots, and chatbot machine-learning architecture * Explore machine learning technology's impact on chatbots and Natural Language Toolkit (NLTK) * Implement hands-on term/inverse document-frequency, chatbot testing/training with machine learning</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Basics-of-Chatbots-with-Machine-Learning-Python</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</h4><p>This section focuses on the introduction to the instructor and AI Sciences. The author discusses the course content and how the course would be useful to the learners.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course and Instructor Introduction</p><p style='margin: 0'>This video introduces the author/instructor and outlines his experience and qualifications as an instructor in this online course content discipline.</p></td></tr><tr><td style='padding: 15px;'><p>2. AI Sciences Introduction</p><p style='margin: 0'>This video outlines the background of the AI Sciences group, its core competencies, and the expertise they bring to the courses.</p></td></tr><tr><td style='padding: 15px;'><p>3. Course Description</p><p style='margin: 0'>In this video, we will explore the course's prerequisites and discuss the course content and the concepts that will be covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>4. ML-Based Chatbots</p><p style='margin: 0'>This video overviews the machine learning-based chatbot's general architecture, purpose, ML-enabled features, and ML revolutionaries.</p></td></tr><tr><td style='padding: 15px;'><p>5. Conversational Chatbots</p><p style='margin: 0'>In this lecture, we will understand conversational chatbots and briefly examine the features of a conversational chatbot, its installation, and its development.</p></td></tr></tbody></table><br><h4>2. Overview of Chatbots</h4><p>This section focuses on chatbot development with machine learning, including the applications of chatbots, virtual assistants, types of chatbots, mechanisms of chatbots, and the challenges of using chatbots.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Overview</p><p style='margin: 0'>This video provides an overview of the concepts to be learned in this module. You will learn about chatbots and what they are used for and overview them in general.</p></td></tr><tr><td style='padding: 15px;'><p>2. History of Chatbots</p><p style='margin: 0'>This video elaborates on the history of chatbots that predated the 60s and 70s era of technology. We will understand when chatbots were first created and how they have gradually progressed to become the present versions of chatbots.</p></td></tr><tr><td style='padding: 15px;'><p>3. Applications of Chatbots</p><p style='margin: 0'>In this lesson, you will learn about the different applications of chatbots, including helpdesk assistants, tutors, home assistants, office assistants, phone assistants, and medical assistants.</p></td></tr><tr><td style='padding: 15px;'><p>4. Chatbots Versus Virtual Assistants Versus Personal Assistants</p><p style='margin: 0'>This lecture compares chatbots, virtual assistants, and personal assistants; delves into the three types of traits; and brings out their advantages and disadvantages to the forefront.</p></td></tr><tr><td style='padding: 15px;'><p>5. Benefits of Chatbots</p><p style='margin: 0'>This video will elaborate on the benefits of using chatbots and the need for adopting chatbots, including personalized customer experience, efficient staff onboarding, standardized solutions, cost saving, and 24x7 availability.</p></td></tr><tr><td style='padding: 15px;'><p>6. Why Should Companies Pick Chatbots</p><p style='margin: 0'>Here, we will discuss why organizations adopt chatbots, including simplicity of functioning, efficiency and speed, improving experience, personalization, and sustainability.</p></td></tr><tr><td style='padding: 15px;'><p>7. Chatbot Types</p><p style='margin: 0'>In this lesson, we will look at classifying chatbots based on how the chatbots can be trained and tested, including the rule-based chatbot-where rules are specified-and self-learning chatbots, where the chatbot learns on its own with the data fed into it.</p></td></tr><tr><td style='padding: 15px;'><p>8. Rule-Based Chatbots</p><p style='margin: 0'>Let's understand what rule-based chatbots are and their mechanism of functioning in detail. Here, certain rules or criteria are provided as support to the chatbots, and the tasks performed, and the responses are based on those rules.</p></td></tr><tr><td style='padding: 15px;'><p>9. Self-Learning Chatbots</p><p style='margin: 0'>Here, we will understand self-learning chatbots, which, as suggested, means that the chatbots can learn independently. The chatbot asks a series of queries and stores the responses before deciding on an action based on the responses.</p></td></tr><tr><td style='padding: 15px;'><p>10. Mechanism of Chatbots</p><p style='margin: 0'>Let's understand the mechanism of how a chatbot works. It needs a predefined goal or objective to function, draft potential solution flows, validate examples, define the solution flows, and launch the chatbot.</p></td></tr><tr><td style='padding: 15px;'><p>11. Challenges of Chatbots</p><p style='margin: 0'>In this video, we will understand the challenges we face using a chatbot, including emerging trends, security and privacy, customer saturation, predicting human behavior, user satisfaction, and user-bot communication.</p></td></tr><tr><td style='padding: 15px;'><p>12. Quiz</p><p style='margin: 0'>This video is a quiz/exercise based on the concepts learned in this section so far.</p></td></tr><tr><td style='padding: 15px;'><p>13. Quiz Solution</p><p style='margin: 0'>This video is the solution to the quiz/exercise based on the concepts learned in this section.</p></td></tr></tbody></table><br><h4>3. Machine Learning-Based Chatbots</h4><p>This section focuses on machine learning-based chatbots and discusses the architecture of an ML chatbot, the enabled features, tokens, chatbot greets, and response generation.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>Let's look at the different concepts you will be learning in this specific module that focuses entirely on machine learning-based chatbots, including purpose, and finally working on a project at the end.</p></td></tr><tr><td style='padding: 15px;'><p>2. Module Overview</p><p style='margin: 0'>In this video, we will outline the concepts, including the general architecture of machine learning, the purpose of chatbots, the Natural Language Toolkit (NLTK), and developing a rule-based chatbot using NLTK.</p></td></tr><tr><td style='padding: 15px;'><p>3. Architecture of ML Chatbots</p><p style='margin: 0'>Here, we will focus on and simplify the machine-learning architecture of chatbots with the help of an example. We will understand the NLTK library and its role in developing chatbots.</p></td></tr><tr><td style='padding: 15px;'><p>4. ML Enabled Features</p><p style='margin: 0'>In this lesson, we will explore the enabling features of a machine learning-based chatbot, including scaling operations, task automation, user engagement, social media interaction, multilingual models, and connectivity.</p></td></tr><tr><td style='padding: 15px;'><p>5. Revolution with ML</p><p style='margin: 0'>In this video, we will look at the various technologies in which machine learning has revolutionized, including customer services, eCommerce, healthcare, travel and tourism, banking and finance, and general services.</p></td></tr><tr><td style='padding: 15px;'><p>6. NLTK Features</p><p style='margin: 0'>Let's explore the Natural Language Toolkit, a library used for specific tasks related to natural language processing, including tokenization, filtering words, stemming, lemmatization, chunking/chinking, and entity recognition.</p></td></tr><tr><td style='padding: 15px;'><p>7. Rule-Based Chatbots</p><p style='margin: 0'>This video overviews the steps in developing rule-based chatbots, including installing the package, inputting data, adding word tokens, lemmatizing, and response generation.</p></td></tr><tr><td style='padding: 15px;'><p>8. Package Installation</p><p style='margin: 0'>This video demonstrates the steps involved in installing NLTK. Another package we will need to install for creating our chatbot is the Wikipedia app and then importing the required libraries.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Input</p><p style='margin: 0'>In this lesson, we will begin inputting the data we will use for our rule-based chatbot.</p></td></tr><tr><td style='padding: 15px;'><p>10. Word Tokens and Remove ASCII</p><p style='margin: 0'>In this video, you will learn to add word tokens using the normalize function and develop the word token. We are going to use the NLTK.word_tokenize function.</p></td></tr><tr><td style='padding: 15px;'><p>11. Remove Tags and Lemmatize</p><p style='margin: 0'>In this video, we will look at removing tags or symbols that we do not want to use in our word token development process. We will then lemmatize the word token using the tag map lemmatizer.</p></td></tr><tr><td style='padding: 15px;'><p>12. Chatbot Greets</p><p style='margin: 0'>After creating the word tokens, removing the tags, and lemmatizing the words, we will create the chatbot's welcome responses or welcome inputs.</p></td></tr><tr><td style='padding: 15px;'><p>13. Response Generation</p><p style='margin: 0'>In this video, we will define a generateResponse() function and define the expected user response from the chatbot.</p></td></tr><tr><td style='padding: 15px;'><p>14. Wiki Search</p><p style='margin: 0'>In this video, you will learn how to search Wikipedia. We will define a function to input data and obtain a response from the Wikipedia_data input we created.</p></td></tr><tr><td style='padding: 15px;'><p>15. Developing Results</p><p style='margin: 0'>We will now test the chatbot for response generation and verify that the chatbot is working perfectly. We will assess the results based on the data input and the response.</p></td></tr><tr><td style='padding: 15px;'><p>16. Local Search and Wikipedia Search</p><p style='margin: 0'>In this lesson, you will learn how to conduct a local search versus a Wikipedia search. We will generate results based on the responses from the Wikipedia search and the local search.</p></td></tr></tbody></table><br><h4>4. Project: Conversational Chatbot Development with Machine Learning</h4><p>This section focuses on developing the conversational chatbot using machine learning, including inputting data, tokenization, lemmatization, adding greet functions, generating responses, finishing the bot, and testing.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>This is a brief introductory video regarding the concepts learned in this module, including preparing and developing the conversational chatbot project using the NLTK library.</p></td></tr><tr><td style='padding: 15px;'><p>2. Project Overview and Packages</p><p style='margin: 0'>In this lesson, we will go over the project conception in detail and install and import all the required libraries for the project, input the data, and perform other tasks to project completion.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting the Data</p><p style='margin: 0'>In this video, we will look at using the request library to seek out the webpage from which we want the information. In our case, we have chosen the webpage of artificial intelligence.</p></td></tr><tr><td style='padding: 15px;'><p>4. Elimination</p><p style='margin: 0'>After collecting all the data, you will learn to eliminate the text that we do not want to use in the chatbot project through an elimination process.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tokenization</p><p style='margin: 0'>In this lesson, we will perform two types of tokenization-sentence tokenization and word tokenization-using the NLTK library.</p></td></tr><tr><td style='padding: 15px;'><p>6. Lemmatization and Processed Text</p><p style='margin: 0'>In this video, we will look at the project's next step, the lemmatization process, using the WordNet lemmatizer and the NLTK library for the conversational chatbot.</p></td></tr><tr><td style='padding: 15px;'><p>7. Greeting Function</p><p style='margin: 0'>After setting everything up, we will now look at creating a few greeting inputs and a few greeting responses for the conversational chatbot to respond with.</p></td></tr><tr><td style='padding: 15px;'><p>8. Generate Response</p><p style='margin: 0'>We will import two libraries, tfidf and cosine_similarity, and then use the TF-IDF vectorizer to generate chatbot responses based on the query input.</p></td></tr><tr><td style='padding: 15px;'><p>9. Bot Finishing</p><p style='margin: 0'>After setting all elements of the chatbot, we will complete the bot by inputting queries and data and receiving responses from the chatbot based on the question placed.</p></td></tr><tr><td style='padding: 15px;'><p>10. Testing the Bot</p><p style='margin: 0'>After finishing the bot and making it send responses, we will perform the final step: check the chatbot for accuracy of the responses and verify that the tasks are performed correctly.</p></td></tr></tbody></table>

Packt

Data Analysis

Python

Let's learn the basic concepts for developing chatbots with machine learning models. This compact course will help you learn to use the power of Python to evaluate your chatbot datasets based on conversational notes, online resources, and websites. Garner hands-on practice in text generation with Python for chatbot development.

Basics of Chatbots with Machine Learning & Python

General Admission

<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>

Nexus Human

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

Azure Machine Learning

Microsoft AI-900

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

<strong>In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.</strong>
        <p>Data cleaning is always a big hassle, especially if we are short on time and want to deliver crucial data analysis insights to our audience. KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.

In this course, we will learn how to use additional helpful KNIME nodes not covered in the other two classes. Solve data cleaning challenges together for different datasets. Use pre-trained models in TensorFlow in KNIME (involves Python coding).

Also, learn the fundamentals for NLP tasks (Natural Language Processing) in KNIME using only KNIME nodes (without any additional coding).

By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.

All the resources and support files for this course are available at https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning</p>
        <h2>What You Will Learn</h2><p>How to use TensorFlow in KNIME<br>How to do data science in KNIME with and without coding<br>How to solve data cleaning and data preparation challenges<br>How to replace Excel and start KNIME for ETL and data cleaning issues<br>Examples of data science machine learning workflows with KNIME</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.</p>
        <h2>Approach</h2><p>This course is practical and consists of a case study where you can and should follow along to solve tasks.</p>
        <h2>Key Features</h2><p>No coding required * Solve data cleaning challenges together and enhance your basic KNIME skills * Learn the fundamentals for NLP tasks in KNIME using only KNIME nodes</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/KNIME-for-Data-Science-and-Data-Cleaning</p>
        <h2>About the Author</h2><h3>Dan We</h3><p>Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master's degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors.

He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.</p>
        <h2>Course Outline</h2><h4>1. Einleitung</h4><p></p><br><h4>2. Older videos KNIME version before 4.3</h4><p></p>

Data Science

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.

KNIME for Data Science and Data Cleaning

<strong>In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!</strong>
        <p>The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.

We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
The code files are available here: https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>What You Will Learn</h2><p>Learn coding in Julia programming language<br>Use DataFrames (equivalent to Pandas) in Julia<br>Create ML models from scratch in a way that helps you make modifications easily<br>Learn data wrangling with Julia<br>Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis<br>Classify using decision trees and random forests</p>
        <h2>Audience</h2><p>This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.<br><br>A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.</p>
        <h2>Approach</h2><p>This course follows a code-oriented and case-study-based approach. In this course, you will learn by practice as every concept explanation is followed by practical implementation.</p>
        <h2>Key Features</h2><p>Learn the syntax of Julia and its differences from Python * Learn machine learning models, both traditional and deep * Explore data science case studies, including analysis and clustering</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Intro and Setting up</h4><p></p><br><h4>2. Core Language Basics</h4><p></p><br><h4>3. Arrays and Matrices: Native Language Support</h4><p></p><br><h4>4. Functions and Fun Stuff</h4><p></p><br><h4>5. Getting Started with Data Science</h4><p></p><br><h4>6. Case Studies in Data Science</h4><p></p><br><h4>7. Deep Learning - Flux in Julia</h4><p></p><br><h4>8. Parting Words</h4><p></p>

Programming

In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

Julia Programming Language - From Zero to Expert

<p>Attention all tech enthusiasts and aspiring programmers in the UK! Are you looking to gain a competitive edge in the ever-growing field of machine learning? Look no further than the QLS endorsed Python Programming &amp; Machine Learning course!</p> <p>With this comprehensive course, you'll dive deep into Python programming fundamentals and develop a solid understanding of machine learning techniques. Our expert instructors will guide you through real-world examples and hands-on projects, giving you the practical skills you need to succeed in this dynamic field.</p> <p>And the best part? Upon completion of the course, you'll receive a hardcopy certificate from Quality Licence Scheme, a highly respected institution in the UK. This certificate is a testament to your dedication and hard work and will serve as a valuable asset as you pursue your career in machine learning.</p> <p>So what are you waiting for? Enrol in the QLS Python Programming &amp; Machine Learning course today and start paving the way towards a successful and fulfilling career in tech!</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Courses:</strong></p> <ul> <li><strong>Course 01: </strong>Diploma in Python Programming at QLS Level 5</li><li><strong>Course 02:</strong> Certificate in Machine Learning with Python at QLS Level 3</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 03:</strong> Azure Machine Learning</li><li><strong>Course 04:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 05:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 06:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul> <p><strong>Learning Outcomes:</strong></p> <ol> <li>Develop a strong foundation in Python programming language</li><li>Gain practical experience in machine learning techniques and algorithms</li><li>Understand the basics of Azure Machine Learning and AWS SageMaker Canvas</li><li>Gain expertise in data science and statistics</li><li>Learn how to build and deploy machine learning models</li><li>Develop skills in pain management and physiology</li><li>Become proficient in first aid at work protocols</li></ol> <p><strong>Please Note:</strong> Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The QLS endorsed First Aid at Work, Physiology &amp; Pain Management bundle includes two highly comprehensive courses - the Diploma in Python Programming and the Machine Learning with Python course.</p> <p>The Diploma in Python Programming course provides a thorough introduction to Python programming language. Students will learn how to write and execute basic Python code, manipulate strings and variables, use functions, control statements, and more. The course is designed to equip learners with the fundamental skills necessary to write complex Python code and build robust applications.</p> <p>The Machine Learning with Python course builds upon the concepts covered in the Diploma in Python Programming course and explores the world of machine learning. Students will gain an understanding of supervised and unsupervised learning, classification, regression, clustering, and deep learning. Additionally, learners will be exposed to real-world examples and hands-on projects that will enable them to build and deploy machine learning models.</p> <p><strong>Certification</strong></p> <p>In pursuing QLS-endorsed courses, learners attain not only profound understanding but also amplify their professional prowess, enriching their curriculum vitae.</p> <p><strong>The Quality Licence Scheme </strong>stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Investing in this comprehensive package, learners will not only receive one but TWO HARD COPY QLS ENDORSED CERTIFICATES conveniently <strong>delivered to their doorstep!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                             <ol> <li>Aspiring data scientists and machine learning engineers</li><li>Software developers seeking to expand their knowledge of Python programming language</li><li>Professionals seeking to gain expertise in Azure Machine Learning and AWS SageMaker Canvas</li><li>Healthcare professionals seeking to improve their understanding of pain management and physiology</li><li>Individuals seeking to acquire first aid at work protocols and certification</li></ol> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ol> <li>Machine Learning Engineer: £45,000-£100,000 per year</li><li>Data Scientist: £40,000-£80,000 per year</li><li>Python Developer: £30,000-£60,000 per year</li><li>Cloud Solutions Architect: £50,000-£90,000 per year</li><li>Business Intelligence Analyst: £30,000-£55,000 per year</li><li>Healthcare Consultant: £30,000-£70,000 per year</li><li>First Aid Officer: £20,000-£35,000 per year</li></ol> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Imperial Academy

Level 3 & 5 Endorsed Diploma | QLS Hard Copy Certificate Included | Plus 5 CPD Courses | Lifetime Access

Python Programming & Machine Learning - QLS Certificate

<p>In the United Kingdom, the demand for skilled programmers and developers is also rising. According to recent statistics, the tech sector employs above 1.5 million people in the UK, and the number of job openings in this sector has increased by 36% over the year. Furthermore, the average salary for a software developer in the UK is around £40,000 per year, with top earners making upwards of £70,000.</p><p>Our Machine Learning with Python, Introduction to Java, and Diploma in Python Programming bundle is the perfect resource for anyone looking to break into this exciting and lucrative field. With a strong emphasis on foundation and real-world applications, our bundle provides the knowledge and tools you need to succeed in the rapidly evolving tech industry.</p><p>In the Introduction to Java component of our bundle, you'll learn the basics of one of the most popular programming languages in the world. You'll explore the syntax and structure of Java code, as well as the principles and practices of object-oriented programming. Whether you're new to programming or simply looking to add a new language to your repertoire, this component of our bundle has something for everyone.</p><p>Finally, in our bundle's Diploma in Python Programming component, you'll gain a thorough understanding of one of the most versatile and powerful programming languages in use today. You'll learn the fundamentals of Python syntax and data structures, as well as more advanced topics such as web development, data science, and artificial intelligence.</p><p>This bundle demonstrates the excellent level of education obtained through QLS-endorsed courses. This bundle includes three courses, ensuring learners receive high-quality, instructive, enjoyable learning. Investing in your education is always a sensible decision, and with 3 QLS Endorsed certifications under your belt, you can be confident that you're double-boosting your rÃ©sumÃ©. Besides the QLS courses, the individuals will have access to 10 CPD courses related to the subject. They will widen your wisdom and improve your general comprehension.</p><p>So why wait? Enrol now and start building your skills in Machine Learning with Python, Introduction to Java, and Diploma in Python Programming today, and take your first step towards a rewarding and in-demand career in the tech sector!</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Certificate in Machine Learning with Python at QLS Level 3</li><li><strong>Course 02:</strong> Diploma in Introduction to Java at QLS Level 5</li><li><strong>Course 03:</strong> Diploma in Python Programming at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04: </strong>Azure Machine Learning</li><li><strong>Course 05:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 06:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 07:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 08:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</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>Personal Development</li><li><strong>Course 10:</strong> Career Development Plan Fundamentals</li><li><strong>Course 11: </strong>Leadership and Management Course</li><li><strong>Course 12: </strong>Communication Building Masterclass</li><li><strong>Course 13: </strong>Positive Psychology Masterclass</li></ul><p>Learning Outcomes:</p><ul><li>Identify the basic concepts of machine learning and its applications</li><li>Learn about the different types of machine learning algorithms and their implementation in Python</li><li>Increase aptitude in data preprocessing, feature selection, and model evaluation</li><li>Learn about the object-oriented programming concepts in Java</li><li>Understand the syntax and semantics of the Java programming language</li><li>Enhance knowledge in Java programming and application development</li></ul><p><strong>Please Note:</strong> Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>This course bundle offers a comprehensive learning experience for individuals interested in developing their machine learning and programming knowledge. <strong>The course includes three courses: Machine Learning with Python, Introduction to Java, and Diploma in Python Programming.</strong></p><p>In the first course, <strong>Machine Learning with Python</strong>, students will learn the fundamentals of machine learning, including the basic concepts, techniques, and algorithms used in this field. They will also gain experience by working on various projects, such as classification and regression models, clustering, and natural language processing.</p><p>In the second course, <strong>Introduction to Java</strong>, students will learn the fundamentals of the Java programming language. They will cover basic concepts such as variables, data types, control structures, and methods. Additionally, learners will learn object-oriented programming concepts such as inheritance, polymorphism, and encapsulation.</p><p>In the third course, <strong>Diploma in Python Programming</strong>, learners will better understand the Python programming language. They will learn advanced concepts such as object-oriented programming, data structures, and algorithms. Learners will also work on several projects to help them apply their knowledge to real-world scenarios.</p><p>Overall, this course bundle is an excellent opportunity for individuals who want to learn machine learning, Java, and Python programming. Individuals will gain the necessary knowledge to develop their projects and contribute to the field of data science.</p><p><strong>Certificate</strong></p><p>QLS-endorsed courses are designed to provide learners with the skills and knowledge they need to succeed in their chosen field.<br /><strong>The Quality Licence Scheme</strong> is a distinguished and respected accreditation in the UK, denoting exceptional quality and excellence. It carries significant weight among industry professionals and recruiters.<br />Upon completion, learners will receive Three <strong>QLS Endorsed</strong> hard copy certificates, making it easy for them to showcase their achievements. These certificates validate their course completion, the level of the course they have completed, and the QLS endorsement.</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>Students or professionals in computer science, engineering, or related fields.</li><li>Programmers looking to expand their knowledge in machine learning and Java programming.</li><li>Anyone interested in learning about machine learning principles and their applications in Python.</li><li>Individuals looking to gain practical skills in Python programming and data analysis.</li><li>Entrepreneurs looking to start a software development or machine learning business.</li><li>Those who want to learn about the basics of object-oriented programming concepts in Java.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Machine Learning Engineer - £35,000-£85,000</li><li>Software Developer - £25,000-£60,000</li><li>Data Analyst - £23,000-£45,000</li><li>Java Developer - £25,000-£55,000</li><li>Python Developer - £25,000-£60,000</li><li>Artificial Intelligence Specialist - £35,000-£80,000</li><li>Data Scientist - £35,000-£80,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

3 QLS Endorsed Diploma | QLS Hard Copy Certificate Included | Plus 10 CPD Courses | Lifetime Access

Machine Learning, Java and Python Programming

Duco Digital Training

IT Certifications

IT Skills

IT (Information Technology)

Computing & IT

IT Literacy

Thinking about learning more about Artificial Intelligence?

The BCS Foundation Certificate in Artificial Intelligence is the advanced version of our Essentials Course Artificial Intelligence and includes more detail and insights about algebraic equations, vector calculus and schematics used in artificial intelligence and machine learning for you to learn how this new technology works.

Artificial Intelligence - BCS Foundation Certificate

<p><strong>Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Save Up To £4,169 and get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now</strong></p><p>Give a compliment to your career and take it to the next level. This <strong>Machine Learning </strong>will provide you with the essential knowledge and skills required to shine in your professional career. Whether you want to develop skills for your next job or want to elevate skills for your next promotion, this <strong>Machine Learning </strong>will help you keep ahead of the pack.</p><p>The <strong>Machine Learning </strong>incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can reinforce your professional skills and essential knowledge, reaching out to the level of expertise required for your position. Further, this <strong>Machine Learning </strong>will add extra value to your resume to stand out to potential employers.</p><p>Throughout the programme, it stresses how to improve your competency as a person in your profession while at the same time it outlines essential career insights in this job sector. Consequently, you'll strengthen your knowledge and skills; on the other hand, see a clearer picture of your career growth in future. By the end of the <strong> Machine Learning,</strong> you can equip yourself with the essentials to keep you afloat into the competition.</p><p>Along with this <strong>Machine Learning </strong>course<strong>,</strong> you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free.</p><p><strong>This Bundle Consists of the following Premium courses: </strong></p><ul><li><strong>Course 01: Machine Learning with Python</strong><strong><br /></strong></li><li><strong>Course 02: Advanced Diploma in User Experience UI/UX Design<br /></strong></li><li><strong>Course 03: Data Science &amp; Machine Learning with R<br /></strong></li><li><strong>Course 04: Python Programming for Everybody<br /></strong></li><li><strong>Course 05: Data Structures Complete Course<br /></strong></li><li><strong>Course 06: Data Science with Python<br /></strong></li><li><strong>Course 07: Computer Science: Graph Theory Algorithms<br /></strong></li><li><strong>Course 08: Higher Order Functions in Python - Level 03<br /></strong></li><li><strong>Course 09: AWS Essentials<br /></strong></li><li><strong>Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002)<br /></strong></li><li><strong>Course 11: Introduction to Data Analysis</strong></li></ul><p>So, enrol now to advance your career!</p><p><strong>Benefits you'll get choosing Apex Learning for this Machine Learning:</strong></p><ul><li>One payment, but lifetime access to 11 CPD courses</li><li>Certificate, student ID for the title course included in a one-time fee</li><li>Full tutor support available from Monday to Friday</li><li>Free up your time - don't waste time and money travelling for classes</li><li>Accessible, informative modules taught by expert instructors</li><li>Learn at your ease - anytime, from anywhere</li><li>Study the course from your computer, tablet or mobile device</li><li>CPD accredited course - improve the chance of gaining professional skills</li></ul><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free (Previously it was £10 * 11 = £110)</strong></li></ul><ul><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><p>If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a<strong> Flat 50% discount </strong>on hard copy certificates, and you can get each for just <strong>£10</strong>!</p><p><strong>P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. </strong></p><p><strong>Curriculum of the Bundle </strong></p><p><strong>Course 01: Machine Learning with Python</strong></p><ul><li>Module 01: Introduction to Algorithms</li><li>Module 02: Preprocessing</li><li>Module 03: Regression</li><li>Module 04: Classification</li></ul><p><strong>Course 02: Data Science &amp; Machine Learning with R<br /></strong></p><ul><li>Data Science and Machine Learning Course Intro</li><li>Data Types and Structures in R</li><li>Data Types and Structures in R</li><li>Intermediate R</li><li>Data Manipulation in R</li><li>Data Visualization in R</li><li>Creating Reports with R Markdown</li><li>Building Webapps with R Shiny</li><li>Introduction to Machine Learning</li><li>Starting A Career in Data Science</li></ul><p><strong>Course 03: Python Programming for Everybody<br /></strong></p><p>Module 01</p><ul><li>A Installing Python</li><li>Documentation</li><li>Command Line</li><li>Variables</li><li>Simple Python Syntax</li><li>Keywords</li><li>Import Module</li></ul><p>Module 02</p><ul><li>Additional Topics</li><li>If Elif Else</li><li>Iterable</li><li>For</li><li>Loops</li><li>Execute</li><li>Exceptions</li></ul><p>Module 03</p><ul><li>Data Types</li><li>Number Types</li><li>More Number Types</li><li>Strings</li><li>More Strings</li><li>Files</li><li>Lists</li><li>Dictionaries</li><li>Tuples</li><li>Sets</li></ul><p>Module 04</p><ul><li>Comprehensions</li><li>Definitions</li><li>Functions</li><li>Default Arguments</li><li>Doc Strings</li><li>Variadic Functions</li><li>Factorial</li></ul><p>Module 05</p><ul><li>Function Objects</li><li>Lambda</li><li>Generators</li><li>Closures</li><li>Classes</li><li>Object Initialization</li><li>Class Static Members</li><li>Classic Inheritance</li><li>Data Hiding</li></ul><p><strong>Course 04: Advanced Diploma in User Experience UI/UX Design<br /></strong></p><ul><li>UX/UI Course Introduction</li><li>Introduction To The Web Industry</li><li>Foundations of Graphic Design</li><li>UX Design (User Experience Design)</li><li>UI Design (User Interface Design)</li><li>Optimization</li><li>Starting a Career in UX/UI Design</li></ul><p><strong>Course 05: Data Structures Complete Course<br /></strong></p><ul><li>Unit 01: Introduction</li><li>Unit 02: Arrays</li><li>Unit 03: Liked List</li><li>Unit 04: Stack</li><li>Unit 05: Queues</li><li>Unit 06: Priority Queues (PQs)</li><li>Unit 07: Union Find</li><li>Unit 08: Binary Search Trees</li><li>Unit 09: Fenwick Tree</li><li>Unit 10: Hash Tables</li><li>Unit 11: Suffix Array</li><li>Unit 12: AVL Trees</li><li>Unit 13: Indexed Priority Queue</li><li>Unit 14: Sparse Tables</li></ul><p><strong>Course 06: Data Science with Python<br /></strong></p><ul><li>Unit 01: Introduction to Python Data Science</li><li>Unit 02: Data Cleaning Packages</li><li>Unit 03: Data Visualization packages</li></ul><p><strong>Course 07: Computer Science: Graph Theory Algorithms</strong></p><ul><li>Module 00: Promo</li><li>Module 01: Introduction</li><li>Module 02: Common Problem</li><li>Module 03: Depth First Search</li><li>Module 04: Breadth First Search</li><li>Module 05: Breadth First Search Shortest Path on a Grid</li><li>Module 06: Trees</li><li>Module 07: Topological Sort</li><li>Module 08: Dijkstra</li><li>Module 09: Bellman-Ford Algorithm</li><li>Module 10: Floyd-Warshall Algorithm</li><li>Module 11: Bridge and Algorithm Points</li><li>Module 12: Tarjan Algorithm</li><li>Module 13: Travelling Salesman Problem (TSP)</li><li>Module 14: Eulerian Paths and Circuits</li><li>Module 15: Prim's Minimum Spanning Tree Algorithm</li><li>Module 16: Network Flow</li></ul><p><strong>Course 08: Higher Order Functions in Python - Level 03<br /></strong></p><ul><li>Module 01: Course Introduction</li><li>Module 02: Simple Higher Order Functions</li><li>Module 03: Sorting with Keys</li><li>Module 04: Map Function</li><li>Module 05: Filter Function</li><li>Module 06: List Comprehension Alternative</li><li>Module 07: Recursion Introduction</li></ul><p><strong>Course 09: AWS Essentials</strong></p><ul><li>Section 01: AWS Foundations and Services</li><li>Section 02: AWS Security and Costs</li></ul><p><strong>Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002)</strong></p><ul><li>Section 01: What You Need to Know</li><li>Section 02: Introducing the Cloud</li><li>Section 03: System Requirements for Cloud Deployments</li><li>Section 04: Cloud Storage</li><li>Section 05: Cloud Compute</li><li>Section 06: Cloud Networking</li><li>Section 07: Cloud Security</li><li>Section 08: Migrating to the Cloud</li><li>Section 09: Maintaining Cloud Solutions</li><li>Section 10: Troubleshooting Cloud Solutions</li></ul><p><strong>Course 11: Introduction to Data Analysis<br /></strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Agenda and Principles of Process Management</li><li>Module 03: The Voice of the Process</li><li>Module 04: Working as One Team for Improvement</li><li>Module 05: Exercise: The Voice of the Customer</li><li>Module 06: Tools for Data Analysis</li><li>Module 07: The Pareto Chart</li><li>Module 08: The Histogram</li><li>Module 09: The Run Chart</li><li>Module 10: Exercise: Presenting Performance Data</li><li>Module 11: Understanding Variation</li><li>Module 12: The Control Chart</li><li>Module 13: Control Chart Example</li><li>Module 14: Control Chart Special Cases</li><li>Module 15: Interpreting the Control Chart</li><li>Module 16: Control Chart Exercise</li><li>Module 17: Strategies to Deal with Variation</li><li>Module 18: Using Data to Drive Improvement</li><li>Module 19: A Structure for Performance Measurement</li><li>Module 20: Data Analysis Exercise</li><li>Module 21: Course Project</li><li>Module 22: Test your Understanding</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        125 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>Anyone from any background can enrol in this <strong>Machine Learning </strong> bundle<strong>. </strong>Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Our <strong>Machine Learning </strong>is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p>There is no time limit for completing this course, it can be studied in your own time at your own pace.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having these various expertise will increase the value in your CV and open you up to multiple job sectors.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Apex Learning

Learning & Development

Online Course Marketing

Online Course Creation

Managed Learning Programme (MLP)

Professional Skills Course (PSC)

Trainers Course

Machine Learning - CPD Accredited

<p>Do you want to prepare for your dream job but strive hard to find the right courses? Then, stop worrying, for our strategically modified<strong> Data Science &amp; Machine Learning with R</strong> bundle will keep you up to date with the relevant knowledge and most recent matters of this emerging field. So, invest your money and effort in our <strong>33</strong> course mega bundle that will exceed your expectations within your budget.</p> <p>The<strong> Data Science &amp; Machine Learning with R</strong> related fields are thriving across the UK, and recruiters are hiring the most knowledgeable and proficient candidates. It's a demanding field with magnitudes of lucrative choices. If you need more guidance to specialise in this area and need help knowing where to start, then <strong> StudyHub </strong>proposes a preparatory bundle. This comprehensive <strong>Data Science &amp; Machine Learning with R</strong> bundle will help you build a solid foundation to become a proficient worker in the sector.</p> <p><strong>This Data Science &amp; Machine Learning with R Bundle consists of the following</strong><strong> 30 </strong><strong>CPD Accredited Premium courses - </strong></p> <ul> <li>Course 01 :R Programming for Data Science</li><li>Course 02 :Data Science &amp; Machine Learning with R</li><li>Course 03 :Quick Data Science Approach from Scratch</li><li>Course 04 :Secure Programming of Web Applications</li><li>Course 05 :Build WebXR, VR and AR, experiences using Three.JS</li><li>Course 06 :Complete AutoLISP Programming</li><li>Course 07 :Javascript Programming for Beginners</li><li>Course 08 :Master JavaScript with Data Visualization</li><li>Course 09 :jQuery: JavaScript and AJAX Coding Bible</li><li>Course 10 :Kotlin Programming : Android Coding</li><li>Course 11 :SQL Programming Masterclass</li><li>Course 12 :SQL Server for Beginners</li><li>Course 13 :SQL NoSQL Big Data and Hadoop</li><li>Course 14 :Microsoft SQL Server Development for Everyone</li><li>Course 15 :C# Programming - Beginner to Advanced</li><li>Course 16 :AutoCAD Programming using C# with Windows Forms</li><li>Course 17 :AutoCAD Programming using VB.NET with Windows Forms</li><li>Course 18 :AutoCAD to Excel - VBA Programming Hands-On!</li><li>Course 19 :AutoCAD VBA Programming - Beginner course</li><li>Course 20 :Cyber Security Incident Handling and Incident Response</li><li>Course 21 :Computer Networks Security</li><li>Course 22 :IT Asset Department</li><li>Course 23 :IT Support Technician Training</li><li>Course 24 :Admin Support Assistant</li><li>Course 25: Computer Vision: C++ and OpenCV with GPU support</li><li>Course 26: Functional Skills IT</li><li>Course 27: CompTIA Healthcare IT Technician</li><li>Course 28: Basic Google Data Studio</li><li>Course 29: Data Analytics with Tableau</li><li>Course 30: Recovering from a Job Loss in Technology</li></ul> <p><strong>3 </strong><strong>Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques-</strong></p> <ul> <li>Course 01 :Career Development Plan Fundamentals</li><li>Course 02 :CV Writing and Job Searching</li><li>Course 03 :Interview Skills: Ace the Interview</li></ul> <p><strong>Learning Outcome</strong></p> <p><strong>This tailor-made </strong><strong>Data Science &amp; Machine Learning with R</strong><strong> bundle will allow you to-</strong></p> <ul> <li>Uncover your skills and aptitudes to break new ground in the related fields</li><li>Deep dive into the fundamental knowledge </li><li>Acquire some hard and soft skills in this area </li><li>Gain some transferable skills to elevate your performance</li><li>Maintain good report with your clients and staff</li><li>Gain necessary office skills and be tech savvy utilising relevant software </li><li>Keep records of your work and make a report </li><li>Know the regulations around this area</li><li>Reinforce your career with specific knowledge of this field </li><li>Know your legal and ethical responsibility as a professional in the related field</li></ul> <p>This Data Science &amp; Machine Learning with R Bundle resources were created with the help of industry experts, and all subject-related information is kept updated on a regular basis to avoid learners from falling behind on the latest developments.</p> <p><strong>Certification</strong></p> <p>After studying the complete training you will be able to take the assessment. After successfully passing the assessment you will be able to claim all courses pdf certificates and <strong>1 hardcopy certificate for the Title Course completely free</strong>. Other Hard Copy certificates need to be ordered at an additional cost of •8.</p> <h2>
        
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                                        330 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                             <p>Ambitious learners who want to strengthen their CV for their desired job should take advantage of the Data Science &amp; Machine Learning with R bundle! This bundle is also ideal for professionals looking for career advancement.</p> 
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        <div>Requirements</div>
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                             <p>To participate in this course, all you need is -</p> <ul> <li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Data Science &amp; Machine Learning with R</li></ul> 
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                             <p>Upon completing this essential Bundle, you will discover a new world of endless possibilities. These courses will help you to get a cut above the rest and allow you to be more efficient in the relevant fields.</p> 
                        </div>

Studyhub UK

Computing Skills

Data Skills

Data Science & Machine Learning with R

Machine Learning (ML)

Big Data Analytics

Big Data

Thinking about learning Big Data?

The BCS Foundation Award in Big Data teaches learners to understand how to manage Big Data and its use with Machine Learning.

Big Data - BCS Foundation Award

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

Data Engineering

Microsoft AZ-303

Microsoft AZ-700

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

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

Machine Learning 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>

TensorFlow

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

The Complete Machine Learning Course with Python

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

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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