Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with the self-driving cars, medical diagnosis and even betting humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML. The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory. Linear Algebra – Linear algebra notation is used in Machine Learning to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating. It covers topics such as: Scalars, Vectors, Matrices, Tensors Matrix Norms Special Matrices and Vectors Eigenvalues and Eigenvectors Multivariate Calculus – This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance. It covers topics such as: Derivatives Integrals Gradients Differential Operators Convex Optimization Probability Theory – The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course. It covers topics such as: Elements of Probability Random Variables Distributions Variance and Expectation Special Random Variables The course also includes projects and quizzes after each section to help solidify your knowledge of the topic as well as learn exactly how to use the concepts in real life. At the end of this course, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects. Enroll now and become the next AI master with this fundamentals course!

Interested in the field of Machine Learning? Then this course is for you! As, AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning. Gartner predicts the business value created by AI will reach $3.9T in 2022. IDC predicts worldwide spending on cognitive and Artificial Intelligence systems will reach $77.6B in 2022. 23% of North American enterprises have machine learning embedded in at least one company function as of last year. 19% of enterprises in developing markets including China and 21% in Europe also have successfully integrated machine learning into functions. The graph below shows the results of a McKinsey & Company survey of 2,135 enterprise senior executive respondents. The graph displays the percent of respondents whose organizations have embedded AI capabilities in at least one function or business unit. Respondents can select multiple AI capabilities Scopes of Machine Learning The machine learning technology is used in most banking and finance industry because the proper implication of technology can give the outstanding result and significant improvement can be seen in terms of replacing legacy system and developed enterprise. The machine learning technology helped the banking & Finance sector in taking company’s decision making, improving customer experience, increasing the backend and frontend staff efficiency. Improved customer services Improved customer services Investment Predictions Fraud Preventions Personalized Digital Assistant Marketing Network Security Projects In this course You will get projects with source code , so it will be more understandable and rich for learning. Like speech and face recognition, Bluex and many more. What materials you will get int this course? You will Get MIT press Notes on Machine learning and yes some of E- Books So Hurry ! Get this course and Get in the Field of Machine Learning. Quiz and Interview question will updated soon.

If you're excited to explore data science & machine learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes" , "logistic regression" , "KNN" and "decision trees" , you're in the right place . This course is PART 1 of a 4-PART SERIES designed to help you build a strong, foundational understanding of machine learning: PART 1: QA & Data Profiling PART 2: Classification PART 3: Regression & Forecasting PART 4: Unsupervised Learning This course makes data science approachable to everyday people, and is designed to demystify powerful machine learning tools & techniques without trying to teach you a coding language at the same time. Instead, we'll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most data science and machine learning courses, you won't write a SINGLE LINE of code . COURSE OUTLINE: In this Part 1 course, we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more. We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation: Section 1: Machine Learning Intro & Landscape Machine learning process, definition, and landscape Section 2: Preliminary Data QA Variable types, empty values, range & count calculations, left/right censoring, etc. Section 3: Univariate Profiling Histograms, frequency tables, mean, median, mode, variance, skewness, etc. Section 4: Multivariate Profiling Violin & box plots, kernel densities, heat maps, correlation, etc. Throughout the course we’ll introduce real-world scenarios designed to help solidify key concepts and tie them back to actual business intelligence case studies. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and much more. If you’re ready to build the foundation for a successful career in data science, this is the course for you . __________ Join today and get immediate, lifetime access to the following: High-quality, on-demand video Machine Learning: Data Profiling ebook Downloadable Excel project file Expert Q&A forum 30-day money-back guarantee Happy learning! -Josh M. (Lead Machine Learning Instructor, Maven Analytics ) __________ Looking for our full business intelligence stack? Search for " Maven Analytics " to browse our full course library, including Excel, Power BI, MySQL , and Tableau courses! See why our courses are among the TOP-RATED on Udemy: "Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C. "This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M. "Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

Are you ready to start your path to becoming a Machine Learning expert! Are you ready to train your machine like a father trains his son! A breakthrough in Machine Learning would be worth ten Microsofts." -Bill Gates There are lots of courses and lectures out there regarding Support Vector Machine. This course is different! This course is truly a step-by-step. In every new tutorial we build on what had already learned and move one extra step forward and then we assign you a small task that is solved in the beginning of next video. We start by teaching the theoretical part of concept and then we implement everything as it is practically using python This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like human and based on that learning, your machine starts making predictions as well! We’ll be using python as programming language in this course which is the hottest language nowadays if we talk about machine leaning. Python will be taught from very basic level up to advanced level so that any machine learning concept can be implemented. We’ll also learn various steps of data preprocessing which allows us to make data ready for machine learning algorithms. We’ll learn all general concepts of machine learning overall which will be followed by the implementation of one of the most important ML algorithms “Support Vector Machine”. Each and every concept of SVM will be taught theoretically and will be implemented using python. Machine learning has been ranked one of the hottest jobs on Glassdoor and the average salary of a machine learning engineer is over $110,000 in the United States according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or even those who know nothing about ML and SVM! This comprehensive course is comparable to other Machine Learning courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 11 hours of HD video lectures divided into 70+ small videos and detailed code notebooks for every lecture this is one of the most comprehensive course for Logistic regression and machine learning on Udemy! This course is really special for you because we are teaching everything from absolute beginning and for those who wants to go an extra mile and wants to learn maths behind SVM, there is a special gift for those people as well. We'll teach you how to program with Python, how to use it for data preprocessing and SVM! Here are just a few of the topics that we will be learning: Programming with Python NumPy with Python for array handling Using pandas Data Frames to handle Excel Files Use matplotlib for data visualizations Data Pre processing Machine Learning concepts, including: Model fitting Overfitting Model Validation Data snooping Data encoding SVM with sk-learn SVM from absolute scratch using NumPy Implementing SVM on different data sets Learning mathematics behind SVM (optional) and much, much more! Enroll in the course and become a data scientist today! Who this course is for: This course is for you if you want to learn how to program in Python for Machine Learning This course is for you if you want to make a predictive analysis model This course is for you if you are tired of Machine Learning courses that are too complicated and expensive This course is for you if you want to learn Python by doing

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.