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Create Machine Learning Models in Microsoft Azure

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Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production.


Advanced Data Science with IBM

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Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks.


Data Science & Deep Learning for Business 20 Case Studies

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Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.


Building a Hand-written Digit Recognition Web App with Tensorflow

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Long ago, I built a hand-written digit recognition web app using Flask and TensorFlow. It was my first ML project as a beginner which didn't end up dying in a notebook, so I think it's worth sharing. This is how it's gonna look: In this tutorial, we will build our digit recognition model using TensorFlow and the MNIST dataset, which contains 70,000 images of hand-written digits 0 to 9, convert it into a TFLite model, and then build the web app. We'll be using Google Colab throughout this guide, because it's the easiest way to get started. We'll use the Keras Datasets API to load our MNIST images, because it makes it extremely easy to load the data.


Beginning Machine Learning with AWS

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Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this course, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects.


Machine Learning on Google Cloud (Vertex AI & AI Platform)

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Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP? If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.



100 Days Data Science Bootcamp: Build 100 Real Life Projects

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Make powerful analysis, Make robust Machine Learning models Make robust Machine Learning models, Master Machine Learning on Python Real life case studies and projects to understand how things are done in the real world Implement Machine Learning Algorithms Learn to perform Classification and Regression modelling Know which Machine Learning model to choose for each type of problem Understand the full product workflow for the machine learning lifecycle. According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary. This makes Data Science a highly lucrative career choice.


Machine Learning Top 5 Models Implementation "A-Z"

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I have worked with IBM, Cisco, EMC-RSA and others, and I have been an academics for a couple of years. I worked in four continents and travelled extensively. I have a PhD in Engineering, an MSc in AI and an MBA, i am also a Certified Blockchain Expert.


Data Science: Supervised Machine Learning in Python

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In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.