MASTERCLASS, WORLD CLASS COURSE - DJANGO WEB DEVELOPMENT, MACHINE LEARNING + AI INTEGRATIONS Master practical and theoretical concepts This full-stack web, Django and AI combination course leads you through a complete range of software skills and languages, skilling you up to be an incredibly on-demand developer. The combination of being able to create full-stack websites AND machine learning and AI models is very rare - something referred to as a unAIcorn. This is exactly what you will be able to do by the end of this course. Why you need this course Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your own tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unAIcorn. It won't matter if you're a complete beginner to software or a seasoned veteran. This course will fill all the gaps in between. I will be there with you through your complete learning experience. What you will get out of this course I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to start coding your own programs, solving problems that inspire you and instilling the 'developer's mindset' of problem solving into you. I don't just throw you in at the deep end - I provide you with the resources to learn and develop what you need at a pace that works for you and then help you stroll through to the finish line. Studies have shown that to learn effectively from online courses tutorials should last around ten minutes each. Therefore to maximise your learning experience all of the lectures in this course have been created around this amount of time or less. My course integrates all of the aspects required to get you on the road becoming a successful web, software and machine learning developer. I teach and I preach, with live, practical exercises and walkthroughs throughout each of the sections. By paying a small cost for this course I believe you will get your value back, with a lot more by the time you have completed it. Ask yourself - how much is mastering a full spectrum of skills in some of of the most exciting areas of software worth to you? How long will it take? Although everyone is different, on average it has taken existing students between 1 - 6 months to complete the course, whilst developing their skills and knowledge along the way. It's best not to speed through the content, and instead go through a handful of lectures, try out the concepts by coding, yourself, and move on once you feel you've grasped the basics of those lectures. Who this is not for This course is not for anyone looking for a one-click fix. Although I provide you with a path walked enough times that it can be a smooth journey it still requires time and effort from you to make it happen. If you're not interested in putting in your energy to truly better yours skills then this may not be the right course for you. Is there a money back guarantee if I'm not happy? Absolutely. I am confident that my course will bring you more value than you spend on the course. As one of the top featured Udemy Instructors my motto is 'your success is my success'. If within the first 30 days you feel my course is not going to help you to achieve your goals then you get a no questions asked, full discount . What materials are included? The majority of my lectures I have chosen to be as video so that you can hear me and see my workings when we're going through each and every area of the course. I include a vast array of practical projects that you can then use in the future to showcase your skills as you develop them, along with introductory clips and quizzes in each section to ensure that you're grasping the concepts effectively. I will be consistently adding more content and resources to the course as time goes by. Keep checking back here if you're not sure right now and feel free to send me a message with any questions or requests you may have. So go ahead and click the ' Buy now ' button when you feel ready on your screen. I look forward to seeing you in the course.

So you know the theory of Machine Learning and know how to create your first algorithms. Now what? There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? Then welcome to “Machine Learning Practical” . We gathered best industry professionals with tons of completed projects behind. Each presenter has a unique style , which is determined by his experience, and like in a real world , you will need adjust to it if you want successfully complete this course. We will leave no one behind! This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience . If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV , how to not look like a noob in the recruiter's eyes, then you came to the right place! This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science. There are most exciting case studies including: ●      diagnosing diabetes in the early stages ●      directing customers to subscription products with app usage analysis ●      minimizing churn rate in finance ●      predicting customer location with GPS data ●      forecasting future currency exchange rates ●      classifying fashion ●      predicting breast cancer ●      and much more! All real. All true. All helpful and applicable. And as a final bonus: In this course we will also cover Deep Learning Techniques and their practical applications. So as you can see, our goal here is to really build the World’s leading practical machine learning course. If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are. They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty. So if you want to get hands-on experience which you can add to your portfolio, then this course is for you. Enroll now and we’ll see you inside.

Update: This course has been updated to include 8 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future! If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them. So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand. This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer. The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects. The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples! 9 Projects That Are Included in This Course: Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project. Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud. Project 3 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking. Project 4– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network. Project 5 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality. Project 6 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language Processing by solving a text classification task using multiple classification algorithms. Project 7 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset. Project 8 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering. All of this and so much more is included in this course. So, what are you waiting for? Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.

Machine learning and Big data analytics are the most future looking skillset. Are you ready to upgrade your skills? Around 85% of companies were likely to adopt AI and ML algorithm to run their business, therefore it will increase job opportunities as well as stiff competition. Even Big data analytics also playing a vital role in finding meaningful insights from unstructured big data.  Meaningful insights will help business  to understand customer needs and changes in the trends. This course will cover ML and big data analytics services offered by Microsoft Azure. ML services includes LUIS, QnA Maker, Computer vision, Content moderator, Translator, Text Analytics whereas for big data analytics service includes   Stream Analytics, Data Lake and Data Analytics using HDInsight with Apache Spark, Jupyter and Zappeline. Microsoft Azure is one of the popular cloud computing platform where you'll  deploy all mentioned services. Topics covered in this learning path: Simple chatbot integrates in HTML websites Echo Bot Facebook Chat bot Question and Answer Maker LUIS (Language Understanding) Text Analytics Detecting Language Analyze image and video Recognition handwritten from text Generate Thumbnail Content Moderator Translate and many more things In this course, you'll learn machine learning, data analytics and also cloud computing as well. All of them are most trending domain of IT . So enroll this course and gain skills to beat the thriving competition .

THE REVIEWS ARE IN: Another Excellent course from a brilliant Instructor. Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google's Cloud. -- Julie Johnson Awesome! -- Satendra Great learning experience!! -- Lakshminarayana Wonderful learning... -- Rajesh Excellent -- Dipthi Clear and to the point. Fit's a lot of knowledge into short, easy to understand concepts/thoughts/scenarios. -- Sam Course was fantastic . -- Narsh Great overview of ML -- Eli Very helpful for beginners, All concept explained well. Overall insightful training session. Thank you ! --Vikas Very good training. Concepts were well explained . -- Jose I like the real world touch given to course material . This is extremely important. -- Soham Learned some new terms and stuffs in Machine Learning. Ideal for learners who needs to get some overview of ML. -- Akilan This session is very good and giving more knowledge about machine learning -- Neethu Got to know many things on machine learning with data as a beginner. Thanks Mike. --Velumani Really well explained and very informative. -- Vinoth COURSE INTRODUCTION: Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers . This course will show you the basics of machine learning for data engineers . The course is geared towards answering questions for the Google Certified Data Engineering exam. This is NOT a general course or introduction to machine learning. This is a very focused course for learning the concepts you'll need to know to pass the Google Certified Data Engineering Exam. At this juncture, the Google Certified Data Engineer is the only real world certification for data and machine learning engineers. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed.” The vast majority of applied machine learning is supervised machine learning . The word applied means you build models in the real world. Supervised machine learning is a type of machine learning that involves building models from data that exists . A good way to think about supervised machine learning is:  If you can get your data into a tabular format , like that of an excel spreadsheet, then most machine learning models can model it. In the course , we’ll learn the different types of algorithms used. We will also cover the nomenclature specific to machine learning. Every discipline has their own vernacular and data science is not different. You’ll also learn why the Python programming language has emerged as the gold standard for building real world machine learning models. Additionally, we will write a simple neural network and walk through the process and the code step by step . Understanding the code won't be as important as understanding the importance and effectiveness of one simple artificial neuron. *Five Reasons to take this Course.* 1) You Want to be a Data Engineer It's the number one job in the world. (not just within the computer space) The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. 2) The Google Certified Data Engineer Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone.  Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google. 3) The Growth of Data is Insane Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 Exabytes a day. That number doubles every month. 4) Machine Learning in Plain English Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer.  Google expects data engineers to be able to build machine learning models. In this course, we will cover all the basics of machine learning at a very high level. 5) You want to be ahead of the Curve The data engineer role is fairly new.  While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field.  You know that the first to be certified means the first to be hired and first to receive the top compensation package. Thanks for your interest in An Introduction to Machine Learning for Data Engineers.

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

This course aims at making you comfortable with the most important optimization technique - Linear Programming. It starts with the concept of linear, takes you through linear program formulation, brings you at ease with graphical method for optimization and sensitivity, dives into simplex method to get to the nuances of optimization, prepares you to take advantage of duality and also discusses various special situations that can help you in becoming smart user of this technique.

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

If you are a developer, an architect, an engineer, a techie, an IT enthusiast, a student or just a curious person, if you are interested in taking on machine learning but you are not too sure where to start, this is probably the right course for you!! In this course, we start with the basics and we explain the concept of supervised learning in depth, we also go over the various types of problems that can be solved using supervised learning techniques. Then we get more hands-on and illustrate some concepts relative to data preparation and model evaluation with bits of code that you can easily reuse. And last, we actually train and evaluate several models based on the most common machine learning algorithms for supervised learning such as K-nearest neighbors, logistic regression, decision trees and random forests. I hope that you find this course fun and easy to follow and that it gives you the machine learning background you need to kick start your journey and be successful in this field!

Given the constantly increasing amounts of data they're faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively. This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will also use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. Further to get a complete hold on the technology, you will work with tools using which you can build predictive models in Python. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. In the first course, Getting Started with Machine Learning in Python , you will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will then use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will also learn to understand and estimate the value of your dataset. Next, you will learn how to clean data for your application, and how to recognize which machine learning task you are dealing with. The second course, Building Predictive Models with Machine Learning and Python , will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems. By the end of this course, you will be able to take the Python machine learning toolkit and apply it to your own projects to build and deploy machine learning models in just a few lines of code. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action. Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed-back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a Machine Learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and Machine Learning. Quantitative trading was also a great platform from which to learn about reinforcement learning in depth, and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.