You have a voice - but does that mean you can narrate? Read and record an entire book, or even a short essay? How do you accomplish those things? Where do you start? What equipment do you need? Learn the basics here. With a powerful software that is FREE, and very reasonable start-up costs, you, too, can capitalize on your voice, and let it become your personal Money-Making Machine.

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep *** NEW Labs - A/B Testing, Multi-model endpoints *** *** JUL-2021 NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness *** *** JUL-2021 New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime *** *** JUN-2021 Lab notebook now use spot-training as the default option. Save over 60% in training costs *** *** NOV-2020 NEW: Nuts and Bolts of Optimization, quizzes *** *** NOV-2020 All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK *** *** SEP-2020 Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest.  With labs. *** *** APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs *** *** JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added For  Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam *** Benefits There are several courses on Machine Learning and AI. What is unique about this course? Here are the top reasons : 1. Cloud-based machine learning keeps you focused on the current best practices. 2. In this course, you will learn the most useful algorithms.  Don’t waste your time sifting through mountains of techniques that are in the wild 4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages. 5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all. 6. There is also No upfront cost or commitment – Pay only for what you need and use Hands-on Labs In this course, you will learn with hands-on labs and work on exciting and challenging problems What exactly will you learn in this course? Here are the things that you will learn in this course: AWS SageMaker * You will learn how to deploy a Notebook instance on the AWS Cloud. * You will gain insight into algorithms provided by SageMaker service * Learn how to train, optimize and deploy your models AI Services In the AI Services section of this course, * You will learn about a set of pre-trained services that you can directly integrate with your application. * Within a few minutes, you can build image and video analysis applications – like face recognition * You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots. Integration * Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application. * You will learn how to host your models, scale on-demand, handle failures * Provide a clean interface for the applications using Lambda and API Gateway Data Lake * Data management is one of the most complex and time-consuming activities when working on machine learning projects. * With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets. * We will build a data lake solution in this course. Machine Learning Certification * If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course. * Timed Practice Exam and Quizzes Source Code * The source code for this course available on Git and that ensures you always get the latest code Ideal Student * The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python. Author My name is Chandra Lingam, and I am the instructor for this course. I have over 50,000 thousand students I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics. I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty. I am looking forward to meeting you. Thank you!

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. this course will help to gain advance technique in machine learning.

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.

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 .

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!

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.