Python, Java, PyCharm, Android Studio and MNIST. Learn to code and build apps! Use machine learning models in hands-on projects. A wildly successful Kickstarter funded this course Explore machine learning concepts. Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. We explore Python 3.6.2 and Java 8 languages, and how to use PyCharm 2017.2.3 and Android Studio 3 to build apps. A machine learning framework for everyone If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you. Be one of the first There are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment. We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do. No prior knowledge is required We will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners. Jump into a field that has more demand than supply Machine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations. "This course is GREAT! This is what I want!" -- Rated 5 Stars by Mammoth Interactive Students Enroll Now While On Sale

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.

This is the bite size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, Neural Network in this course. Content Getting Started Getting Started 2 Data Mining Process Simple Linear Regression Regression in Weka KMeans Clustering KMeans Clustering in Weka Agglomeration Clustering Agglomeration Clustering in Weka Decision Tree: ID3 Algorithm Decision Tree in Weka KNN Classiifcation KNN in Weka Naive Bayes Naive Bayes in Weka What Algorithm to use? Model Evaluation Weka Advanced Attribute Selection Weka Advanced Data Visualizations Weka Model Selection and Deployment

This course will help you and your team to build skills required to pass the most in demand and challenging, Azure DP-100 Certification exam . It will earn you one of the most in-demand certificate of Microsoft Certified: Azure Data Scientist Associate . DP-100 is designed for Data Scientists. This exam tests your knowledge of Data Science and Machine learning to implement machine learning models on Azure. So you must know right from Machine Learning fundamentals, Python, planning and creating suitable environments in Azure, creating machine learning models as well as deploying them in production. Why should you go for DP-100 Certification? One of the very few certifications in the field of Data Science and Machine Learning. You can successfully demonstrate your knowledge and abilities in the field of Data Science and Machine Learning. You will improve your job prospects substantially in the field of Data Science and Machine Learning. Key points about this course Covers the most current syllabus as on May, 2021. 100% syllabus of DP-100 Exam is covered. Very detailed and comprehensive coverage with more than 200 lectures and 25 Hours of content Crash courses on Python and Azure Fundamentals for those who are new to the world of Data Science Machine Learning is one of the hottest and top paying skills. It's also one of the most interesting field to work on. In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models using Azure Machine Learning Service as well as the Azure Machine Learning Studio. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve. This course will help you prepare for the entry to this hot career path of Machine Learning as well as the Azure DP-100: Azure Data Scientist Associate exam . ----- Exam Syllabus for DP-100 Exam ----- 1. Set up an Azure Machine Learning Workspace (30-35%) Create an Azure Machine Learning workspace Create an Azure Machine Learning workspaceConfigure workspace settings Manage a workspace by using Azure Machine Learning studio Manage data objects in an Azure Machine Learning workspace Register and maintain datastores Create and manage datasets Manage experiment compute contexts Create a compute instance Determine appropriate compute specifications for a training workload Create compute targets for experiments and training Run Experiments and Train Models (25-30%) Create models by using Azure Machine Learning Designer Create a training pipeline by using Azure Machine Learning designer Ingest data in a designer pipeline Use designer modules to define a pipeline data flow Use custom code modules in designer Run training scripts in an Azure Machine Learning workspace Create and run an experiment by using the Azure Machine Learning SDK Configure run settings for a script Consume data from a dataset in an experiment by using the Azure Machine Learning SDK Generate metrics from an experiment run Log metrics from an experiment run Retrieve and view experiment outputs Use logs to troubleshoot experiment run errors Automate the model training process Create a pipeline by using the SDK Pass data between steps in a pipeline Run a pipeline Monitor pipeline runs Optimize and Manage Models (20-25%) Use Automated ML to create optimal models Use the Automated ML interface in Azure Machine Learning studio Use Automated ML from the Azure Machine Learning SDK Select pre-processing options Determine algorithms to be searched Define a primary metric Get data for an Automated ML run Retrieve the best model Use Hyperdrive to tune hyperparameters Select a sampling method Define the search space Define the primary metric Define early termination options Find the model that has optimal hyperparameter values Use model explainers to interpret models Select a model interpreter Generate feature importance data Manage models Register a trained model Monitor model usage Monitor data drift Deploy and Consume Models (20-25%) Create production compute targets Consider security for deployed services Evaluate compute options for deployment Deploy a model as a service Configure deployment settings Consume a deployed service Troubleshoot deployment container issues Create a pipeline for batch inferencing Publish a batch inferencing pipeline Run a batch inferencing pipeline and obtain outputs Publish a designer pipeline as a web service Create a target compute resource Configure an Inference pipeline Consume a deployed endpoint Some feedback from previous students, "The instructor explained every concept smoothly and clearly. I'm an acountant without tech background nor excellent statistical knowledge. I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020. This course really help." "Cleared DP-100 today with the help of this course. I would say this is the one of the best course to get in depth knowledge about Azure machine learning and clear the DP-100 with ease. Thank you Jitesh and team for this wonderful tutorial which helped me clear the certification." "The instructor explained math concept clearly. These math concepts are necessary as fundation of machine learning, and also are very helpful for studying DP-100 exam concepts. Passed DP-100." I am committed to and invested in your success. I have always provided answers to all the questions and not a single question remains unanswered for more than a few days. The course is also regularly updated with newer features. Learning data science and then further deploying Machine Learning Models have been difficult in the past. To make it easier, I have explained the concepts using very simple and day-to-day examples. Azure ML is Microsoft's way of democratizing Machine Learning. We will use this revolutionary tool to implement our models. Once learnt, you will be able to create and deploy machine learning models in less than an hour using Azure Machine Learning Studio. Azure Machine Learning Studio is a great tool to learn to build advance models without writing a single line of code using simple drag and drop functionality. Azure Machine Learning (AzureML) is considered as a game changer in the domain of Data Science and Machine Learning. This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists to learn the AzureML tool. You can skip some of the initial lectures or run them at 2x speed, if you are already familiar with the concepts or basics of Machine Learning. The course is very hands on and you will be able to develop your own advance models while learning, Advance Data Processing methods Statistical Analysis of the data using Azure Machine Learning Modules MICE or Multiple Imputation By Chained Equation SMOTE or Synthetic Minority Oversampling Technique PCA; Principal Component Analysis Two class and multiclass classifications Logistic Regression Decision Trees Linear Regression Support Vector Machine (SVM) Understanding how to evaluate and score models Detailed Explanation of input parameters to the models How to choose the best model using Hyperparameter Tuning Deploy your models as a webservice using Azure Machine Learning Studio Cluster Analysis K-Means Clustering Feature selection using Filter-based as well as Fisher LDA of AzureML Studio Recommendation system using one of the most powerful recommender of Azure Machine Learning All the slides and reference material for offline reading You will learn and master, all of the above even if you do not have any prior knowledge of programming. This course is a complete Machine Learning course with basics covered. We will not only build the models but also explain various parameters of all those models and where we can apply them. We would also look at Steps for building an ML model. Supervised and Unsupervised learning Understanding the data and pre-processing Different model types The AzureML Cheat Sheet. How to use Classification and Regression What is clustering or cluster analysis KDNuggets one of the leading forums on Data Science calls Azure Machine Learning as the next big thing in Machine Learning . It further goes on to say, "people without data science background can also build data models through drag-and-drop gestures and simple data flow diagrams." Azure Machine Learning's library has many pre-built models that you can re-use as well as deploy them. So, hit the enroll button and I will see you inside the course. Best-

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.

The easiest way to learn and do various machine learning in the world.Lectures that will definitely satisfy the beginners. Lectures that will surprise any skilled person.Lectures that make you become familiar with the machine through machine learning.Lectures that make you wait for the next lectures.You will learn how to conduct, compare, validate and present a variety of machine learning and their result.Sample data for all lectures are given.Free unlimited tools to try it out are given.