They say we live in a world where machines are getting smarter and people dumber! While the jury's still out on whether we are getting dumber, there is no doubt that machines are getting smarter. They are increasingly able to do things we always thought were too vast and complex for anyone but the smartest humans. Intelligence is no longer the exclusive domain of human beings.  Computers are increasingly outsmarting the world's best professionals in areas as diverse as the most complex multi player games, the most difficult medical diagnoses and the most challenging legal problems, to name but three. At the heart of this revolution in 'artificial intelligence' lies machine learning. It's changing every field, everyday. None of us are left untouched. It therefore essential for all of us to understand what machine learning is, why it is becoming so critical and fundamental, and how machines learn. This course takes you through all of this in a simple, easy to understand manner. So whether you plan to engage with machine learning professionally - as a technologist, data scientist or business professional; or whether you are just a layperson interested in understanding what the big deal is, this course is for you!

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises.  Apache Spark is a very commonly used framework for running machine learning models.  Best practices for using Spark will be covered in this course.  Best practices for data manipulation, model training, and model tuning will also be covered.  The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies.   By the end of this course you will be able to: 1.  Use Apache Spark's RDDs, dataframes, and a pipeline 2.  Employ spark-submit scripts to interface with Spark environments 3.  Explain how collaborative filtering and content-based filtering work 4.  Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5.  Analyze hyperparameters in machine learning models on Apache Spark 6.  Deploy machine learning algorithms using the Apache Spark machine learning interface 7.  Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019 ! With brand new sections as well as updated and improved content , you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them: Brand new sections include: Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions. And the following sections have all been improved and added to : All the codes have been updated to work with Python 3.6 and 3.7 The codes have been refactored to work with Google Colab Deep Learning and NLP Binary and multi-class classifications with deep learning Get the most up to date machine learning information possible, and get it in a single course! *         *         * The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival’s “project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star ratings , it's already the longest and best rated Machine Learning course on Udemy! Build Powerful Machine Learning Models to Solve Any Problem You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Gain complete machine learning tool sets to tackle most real world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of t rain/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much much more! No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area. Make This Investment in Yourself If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Take this course and become a machine learning engineer!

Note: Machine Learning typically data analyst are some of the most expensive and coveted professionals around today.Data analysts enjoy one of the top-paying jobs, with an average salary of $140,000 according to Glassdoor .That's just the average! And it's the future. Machine Learning is very important in Data mining. Also,machine Learning is a growing field. and it is widely used when searching the web, placing ads, credit scoring, stock trading and for many other applications.Ubiquitous examples of machine learning are Google’s web search, spam filters and self-driving cars.Machine learning as it relates to artificial intelligence is an exciting field that focuses on developing complex programs that enable computers to teach themselves to grow. Our course is designed to make it easy for everyone to master machine learning. It has been divided in to following main sections : Introduction to machine learning Hadoop Data Flow Systems Big data This amazing Course will help you quickly master all the difficult concepts and will the learning will be a breeze. Let's get started.

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.