551 Machine Learning (ML) courses delivered Online

This AWS SageMaker Canvas course will help you become a machine learning expert and will enhance your skills by offering you comprehensive knowledge and the required hands-on experience on this newly launched cloud-based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.

Managing Successful Machine Learning Projects Machine learning projects are a different beast. You have to secure access to the required data, often from multiple siloed sources. You have to switch back and forth between research mode and execution mode. You have to delicately guide data exploration towards a well-defined machine learning objective. You have to align this machine learning objective with your business objectives. You have to ensure that any sensitive data is adequately protected. How do you tame this beast and lead your project to successful completion? In this presentation, Dr. Neeraj Kashyap will share some practical tips for succeeding at machine learning, gained from his years at Google and in healthcare. We will discuss the life cycles of healthy machine learning projects and unhealthy ones so that you can identify impending disasters and avert them before they get out of hand. Throughout the session, we will emphasize data privacy, because no amount of intelligence is worth compromising your users for.

This course will help you learn the programming fundamentals with Python 3. It is designed for beginners in Python and is a complete masterclass. This course will help you understand Python GUI, data science, full-stack web development with Django, machine learning, artificial intelligence, Natural Language Processing, and Computer Vision.

Unleashing the Power of Deep Learning: Mastering Neural Network with R Dive into the fascinating realm of artificial intelligence with our course, 'Deep Learning Neural Network with R.' Imagine a world where machines learn and make decisions, mimicking the intricacies of the human brain. This course is your gateway to unlocking the secrets of deep learning, focusing on neural networks implemented using the versatile R programming language. Immerse yourself in hands-on projects, from creating single-layer neural networks for agriculture analysis to mastering multi-layer neural networks for predicting deaths in wars. The journey begins with reviewing datasets and creating dataframes, leading you through running neural network code and generating insightful output plots. Join us in this captivating exploration, where coding meets creativity, and algorithms come to life. Learning Outcomes Master the fundamentals of single-layer neural networks, gaining the skills to analyze agricultural datasets effectively. Acquire proficiency in implementing multi-layer neural networks, specifically tailored for predicting outcomes in complex scenarios like deaths in wars. Develop hands-on experience in creating and manipulating dataframes for enhanced data analysis. Gain a deep understanding of neural network syntax, commands, and code execution in the R programming language. Hone your ability to generate meaningful output plots, transforming raw data into visually compelling insights. Why choose this Deep Learning Neural Network with R course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Deep Learning Neural Network with R course for? Aspiring data scientists and analysts eager to delve into the world of deep learning. R programming enthusiasts looking to enhance their skills with practical applications. Students and professionals in computer science, statistics, or related fields. Individuals seeking to understand the implementation of neural networks in real-world scenarios. Anyone fascinated by the intersection of coding, data analysis, and artificial intelligence. Career path Machine Learning Engineer: £40,000 - £70,000 Data Scientist: £35,000 - £60,000 Artificial Intelligence Researcher: £45,000 - £80,000 Research Scientist (Machine Learning): £50,000 - £90,000 Data Analyst (AI/ML): £30,000 - £55,000 Senior AI Developer: £60,000 - £100,000 Prerequisites This Deep Learning Neural Network with R does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning Neural Network with R was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1) Reviewing Dataset 00:14:00 Creating Dataframes 00:09:00 Generating Output 00:12:00 Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2) Running Neural Network Code 00:11:00 Importing Dataset 00:09:00 Neural Network Plots for Hidden Layer 1 00:08:00 Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1) Syntax and Commands for MLP 00:11:00 Running the Code 00:08:00 Testing for Dataframes 00:13:00 Predict Results 00:08:00 Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2) Creating R Folder 00:14:00 Generating Output Plot 00:12:00 Testing and Predicting the Outputs 00:16:00

🚀 Unlock the Power of Data with Our Machine Learning Course! 🤖 Are you ready to dive into the revolutionary world of Machine Learning? Welcome to our comprehensive course designed to equip you with the skills and knowledge needed to harness the potential of data-driven decision-making. 🎓 Machine Learning has rapidly emerged as one of the most transformative technologies of the 21st century. From powering intelligent virtual assistants to revolutionizing healthcare diagnostics, its applications are boundless. With our expertly crafted course, you'll embark on a journey that will demystify the complexities of Machine Learning and empower you to leverage its capabilities for diverse purposes. 💡 Why Machine Learning? In today's data-driven world, organizations across industries are seeking professionals who can extract actionable insights from vast amounts of data. Machine Learning offers the tools and techniques necessary to analyze complex datasets, identify patterns, and make predictions with unprecedented accuracy. By mastering Machine Learning, you'll gain a competitive edge in the job market and position yourself as a valuable asset to any organization. 📈 What You'll Learn: Our Machine Learning course covers a wide array of topics, including: Fundamentals of Machine Learning algorithms Supervised, unsupervised, and reinforcement learning techniques Data preprocessing and feature engineering Model evaluation and validation Deep learning and neural networks Practical applications and case studies With hands-on projects and real-world examples, you'll not only understand the theory behind Machine Learning but also gain practical experience in implementing algorithms and solving complex problems. Whether you're a beginner or an experienced data professional, our course is tailored to accommodate learners of all levels. 📊 Who is this for? Our Machine Learning course is ideal for: Aspiring data scientists and analysts Software engineers looking to transition into Machine Learning roles Business professionals seeking to leverage data for strategic decision-making Students and academics interested in exploring the forefront of technology No matter your background or experience level, our course provides a solid foundation in Machine Learning principles and techniques, setting you on the path to success in this rapidly evolving field. 🌟 Career Path: By mastering Machine Learning, you'll open doors to a myriad of exciting career opportunities, including: Data Scientist Machine Learning Engineer AI Researcher Business Intelligence Analyst Data Engineer With the demand for Machine Learning professionals on the rise, employers are actively seeking individuals with the skills and expertise to drive innovation and deliver impactful solutions. Whether you're looking to advance your current career or embark on a new professional journey, our course will equip you with the tools and knowledge needed to thrive in today's competitive job market. 💼 FAQ: Q: Is prior programming experience required to enroll in the course? A: While prior programming experience can be beneficial, our course is designed to accommodate learners of all backgrounds. We provide comprehensive tutorials and resources to help you grasp the fundamentals of programming and get started with Machine Learning. Q: How long does it take to complete the course? A: The duration of the course varies depending on your pace and level of commitment. On average, most learners complete the course within 3 to 6 months. However, you have the flexibility to study at your own pace and revisit materials as needed. Q: Are there any prerequisites for enrolling in the course? A: While there are no strict prerequisites, familiarity with basic mathematics, statistics, and programming concepts can be advantageous. We provide supplementary materials and support to help you build the necessary foundation for success in the course. Q: Will I receive a certificate upon completion of the course? A: Yes, upon successfully completing the course requirements, you'll receive a certificate of completion that validates your proficiency in Machine Learning concepts and techniques. This certificate can enhance your credentials and demonstrate your expertise to potential employers. Q: How does the course structure accommodate working professionals? A: Our course offers flexible scheduling options, allowing you to balance your studies with your professional and personal commitments. With on-demand access to course materials and resources, you can learn at your own convenience and progress at a pace that suits your lifestyle. Don't miss out on the opportunity to unlock your full potential with our Machine Learning course! Enroll today and embark on a transformative journey that will shape the future of your career. 🌐✨ Course Curriculum Module 1_ Introduction to Machine Learning Introduction to Machine Learning 00:00 Module 2_ Linear Regression Linear Regression 00:00 Module 3_ Logistic Regression Logistic Regression 00:00 Module 4_ Decision Trees and Random Forests Decision Trees and Random Forests 00:00 Module 5_ Support Vector Machines (SVMs) Support Vector Machines (SVMs) 00:00 Module 6_ k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) 00:00 Module 7_ Naive Bayes Naive Bayes 00:00 Module 8_ Clustering Clustering 00:00 Module 9_ Dimensionality Reduction Dimensionality Reduction 00:00 Module 10_ Neural Networks Neural Networks 00:00

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

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

Overview This comprehensive course on Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning with Python comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00

Machine Learning and other AI: Are You Ready? Machine Learning is the latest 'hot' title in computing and Artificial Intelligence. It sounds new but is influencing your life already. Machine Learning and AI will affect more and more of your life as they mature and more enabling technologies intersect with them. Machine Learning will change many disciplines and careers, overcoming scale issues, enabling better knowledge and insights, and augmenting many professions. Are you ready? This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.