Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science" . This is the most comprehensive, yet straight-forward, course for the outlier detection on UDEMY! Are you Data Scientist or Data Analyst or Financial Analyst or maybe you are interested in anomaly detection or fraud detection? The course is designed to teach you the various techniques which can be used to identify and recognize outliers in any set of data. The process of identifying outliers has many names in Data Science and Machine learning such as outlier modeling, novelty detection, or anomaly detection. Outlier detection algorithms are useful in areas such as Machine Learning, Deep Learning, Data Science, Pattern Recognition, Data Analysis, and Statistics . I will present to you very popular algorithms used in the industry as well as advanced methods developed in recent years, coming from Data Science. You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn the innovative algorithms for detection outliers in High-dimensional space . I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. Anyone who interested in programming, I developed all algorithms in PYTHON, so you can download and run them. List of Algorithms: Interquartile Range Method (IQR), Standard Deviation Method KNN, DBSCAN, Local Outlier Factor, Clustering Based Local Outlier Factor, Isolation Forest, Minimum Covariance Determinant, One-Class SVM, Histogram-Based Outlier Detection, Feature Bagging, Local Correlation Integral Angular Based Outlier Detection Autoencoders Why wait? Start learning today! Because Everyone, who deals with the data, needs to know ‘ Complete Outlier Detection Algorithms A-Z: In Data Science ’, a necessity to recognize fraudulent transactions in the data set. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you will learn have an implementation in PYTHON. You will learn how to examine data with the goal of detecting anomalies or abnormal instances or outlier data points. For the code explained in the tutorials, you can find a GitHub repository hyperlink. At the end of this course, you will have understood the different aspects that affect how this problem can be formulated, the techniques applicable for each formulation, and knowledge of some real-world applications in which they are most effective.

This, one of a kind, Career-ready Masterclass is designed to help you gain hands-on and in-depth exposure to the domain of Data Science by adopting the learn by doing approach. And the best way to land your dream job is to build a portfolio of projects. And the best platform for a Data Scientist is Kaggle! This course will give you an in-depth hands-on experience with a variety of projects from the following. By completing these projects you will gain hands-on experience with: Data Analysis Exploratory Data Analysis Machine Learning Deep Learning Computer Vision Natural Language Processing and more! This course primarily focuses on helping you stand out by building a portfolio comprising of a series of Jupyter Notebooks in Python that utilizes Competitions and Public Datasets hosted on the Kaggle platform. You will set up your Kaggle profile that will help you stand out for future employment opportunities!

I am glad you have decided to join the tens of thousands of other students who have enjoyed millions of minutes of instruction videos from this channel. Welcome to the "Python Realtime Population Health Desktop Application" course. The objective of this course is very simple and straightforward. We shall build a modern GUI application for tracking population health from scratch. You shall master all the tools used to complete this objective by the end of the course, additionally you shall have a complete working application which you can share with your friends. This course does not assume any prior knowledge of python or GUI programming, everything shall be done from the very basics. I have no doubt you will love this course. This course also comes with a 30 days money back guarantee so give it a try and let's see how it goes.

Course Description Learn to build recommendation engine with Collaborative filtering and  popular programming language Python. Build a strong foundation in Recommendation Systems with this tutorial for beginners. Understanding of recommendation systems Leverage Collaborative filtering to classify documents User Jupyter Notebook for programming Use singular value decomposition (SVD) for recommendation engine A Powerful Skill at Your Fingertips Learning the fundamentals of recommendation system puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation. Jobs in recommendation systems area are plentiful, and being able to learn Collaborative filtering and SVD will give you a strong edge. Recommendation Systems ares becoming very popular. Amazon, Walmart, Google eCommerce websites are few famous example of recommendation systems in action. Recommendation Systems are vital in information retrieval, upselling and cross selling of products.  Learning Collaborative filtering with SVD will help you become a recommendation system developer which is in high demand. Big companies like Google, Facebook, Microsoft, AirBnB and Linked In already using recommendation systens with item based collaborative in information retrieval and social platforms. They claimed that using recommendation systems has boosted productivity of entire company significantly. Content and Overview This course teaches you on how to build recommendation systems using open source Python and Jupyter framework.  You will work along with me step by step to build following answers Introduction to recommendation systems. Introduction to Collaborative filtering Build an jupyter notebook step by step using item based collaborative filtering Build a real world web application to recommend books What am I going to get from this course? Learn recommendations systems and build real world books recommendation engine from professional trainer from your own desk. Over 10 lectures teaching you how to build real world recommendation systems Suitable for beginner programmers and ideal for users who learn faster when shown. Visual training method, offering users increased retention and accelerated learning. Breaks even the most complex applications down into simplistic steps. Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges. Note: Please note that I am using short documents in this example to illustrate concepts. You can use same code for longer documents as well.

Ready to take your R Programming skills to the next level? Want to truly become proficient at Data Science and Analytics with R? This course is for you! Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD. In this course you will learn: How to prepare data for analysis in R How to perform the median imputation method in R How to work with date-times in R What Lists are and how to use them What the Apply family of functions is How to use apply(), lapply() and sapply() instead of loops How to nest your own functions within apply-type functions How to nest apply(), lapply() and sapply() functions within each other And much, much more! The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.

Learn R Programming by doing! There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, Sincerely, Kirill Eremenko

Complete Data Science Fundamental Course for Beginners First of all this is complete Data Science Fundamental Course. If you looking to begin with Data Science then this the perfect choice ever. HERE IS WHY YOU SHOULD TAKE THE COURSE The course is complete for beginners. That means by completing this course I guarantee you that you will learn all the complex Data Science Components and Machine Learning Algorithms in a easy and Understandable way. In this age of big data, companies across the globe are generating lots and lots of data. This makes Data Science a trending topic. Data Science is one of the most promising technology right now. Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Most of the businesses today are using Data Science to add value to their business operations and increase customer satisfaction and retention. And, so there is substantial increase in the demand for Data Scientists who are skilled in Data Science and related technologies. And, this is the right time to start learning Data Science.

Web Scraping has become one of the hottest topics in the data science world, for getting access to data can make or break you. This is why Fortune 500 companies like Walmart, CNN, Target, and Amazon use web scraping to get ahead and stay ahead with data. It’s the original growth tool and one of their best-kept secrets …And it can easily be yours too. Welcome to Web Scraping in Python with BeautiuflSoup and Selenium! The most up to date and project-oriented course out there currently. In this course, you're going to learn how to scrape data off some of the most well-known websites which include: Twitter Airbnb Nike Google Indeed NFL MarketWatch Worldometers IMDb Carpages At the end of this course, you will understand the most important components of web scraping and be able to build your own web scrapers to obtain new data from any website, automate any task using web scraping, and more. Plus, familiarize yourself with some of the most common scraping techniques and sharpen your Python programming skills while you’re at it! First, learn the essentials of web scraping, explore the framework of a website, and get your local environment ready to take on scraping challenges with BeautifulSoup, and Selenium. Next, cover the basics of BeautifulSoup, utilize the requests library and LXML parser, and scale up to deploy a new scraping algorithm to scrape data from any table online, and from multiple pages. Third, set up Selenium to deal with JavaScript-driven webpages, and use the unique functions of Selenium to interact with pages. Combine the concepts of BeautifulSoup and Selenium to create the most effective scrapers to deal with some of the most challenging websites. Finally, learn how to make web scraping fully automatic by running your scraper at a specific time each day. What makes this course different from the others, and why you should enroll? First, this is the most updated course currently out Second, this is the most project-based course you will find, where we will scrape many of the internets most well-known websites You will have an in-depth step by step guide on how to become a professional web scraper . You will learn how to use Selenium to scrape JavaScript websites and I can assure you, you won't find any tutorials out there that teach you how to really use Selenium like I'll be doing in this course. You will learn how to create a fully automated web scraping script that runs periodically without any intervention from you. 30 days money-back guarantee by Udemy So whether you’re a data scientist, machine learning, or AI engineer who wants to access more data sources; a web developer looking to automate tasks, or a data buff with a general interest in data science and web scraping… This course delivers an in-depth presentation of web scraping basics, methodologies, and approaches that you can easily apply to your own personal projects, or out there in the real world of business. Join me now and let’s start scraping the web together. Enroll today.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery! Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know). This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want. The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)! The topics covered in this course are: - Data Exploration and Visualizations - Neural Networks and Deep Learning - Model Evaluation and Analysis - Python 3 - Tensorflow 2.0 - Numpy - Scikit-Learn - Data Science and Machine Learning Projects and Workflows - Data Visualization in Python with MatPlotLib and Seaborn - Transfer Learning - Image recognition and classification - Train/Test and cross validation - Supervised Learning: Classification, Regression and Time Series - Decision Trees and Random Forests - Ensemble Learning - Hyperparameter Tuning - Using Pandas Data Frames to solve complex tasks - Use Pandas to handle CSV Files - Deep Learning / Neural Networks with TensorFlow 2.0 and Keras - Using Kaggle and entering Machine Learning competitions - How to present your findings and impress your boss - How to clean and prepare your data for analysis - K Nearest Neighbours - Support Vector Machines - Regression analysis (Linear Regression/Polynomial Regression) - How Hadoop, Apache Spark, Kafka, and Apache Flink are used - Setting up your environment with Conda, MiniConda, and Jupyter Notebooks - Using GPUs with Google Colab By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more . By the end, you will have a stack of projects you have built that you can show off to others. Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems. Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows. Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career. You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean! Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course! Taught By: Daniel Bourke: A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages. My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen. I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more. Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups. Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims. My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?". Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views. I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible. My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know. Questions are always welcome. -------- Andrei Neagoie: Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the course!

Welcome to Deployment of Machine Learning Models, the most comprehensive machine learning deployments online course available to date. This course will show you how to take your machine learning models from the research environment to a fully integrated production environment . What is model deployment? Deployment of machine learning models, or simply, putting models into production, means making your models available to other systems within the organization or the web, so that they can receive data and return their predictions. Through the deployment of machine learning models, you can begin to take full advantage of the model you built. Who is this course for? If you’ve just built your first machine learning models and would like to know how to take them to production or deploy them into an API, If you deployed a few models within your organization and would like to learn more about best practices on model deployment, If you are an avid software developer who would like to step into deployment of fully integrated machine learning pipelines, this course will show you how. What will you learn? We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to start creating a model in the research environment, and then transform the Jupyter notebooks into production code, package the code and deploy to an API, and add continuous integration and continuous delivery. We will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And we will also discuss the tools and platforms available to deploy machine learning models. Specifically, you will learn: The steps involved in a typical machine learning pipeline How a data scientist works in the research environment How to transform the code in Jupyter notebooks into production code How to write production code, including introduction to tests, logging and OOP How to deploy the model and serve predictions from an API How to create a Python Package How to deploy into a realistic production environment How to use docker to control software and model versions How to add a CI/CD layer How to determine that the deployed model reproduces the one created in the research environment By the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization. What else should you know? This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure. But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course . Want to know more? Read on... This comprehensive course on deployment of machine learning models includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and re-use in your own projects . In addition, we have now included in each section an assignment where you get to reproduce what you learnt to deploy a new model. So what are you waiting for? Enroll today, learn how to put your models in production and begin extracting their true value.