Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You'll learn the steps necessary to create a successful machine learning application with Python and the sci-kit learn library. Authors Andreas Maller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and mat plot lib libraries will help you get even more from this book.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of boosting, how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
This book offers the readers The basics of machine learning in a very simple, user-friendly language. While browsing the table of Contents, you will realize that you are given an introduction to every concept that comes under the umbrella of machine learning. This book is aimed at students who are new to the topic of machine learning. It is meant for students studying machine learning in their undergraduate and postgraduate courses in Information Technology. It is also aimed at computer engineering students. It will help familiarize students with the Terms and terminologies used in machine learning. We hope that this book serves as an entry point for students to pursue their future studies and careers in machine learning.
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as sci-kit learn.
Some tutorials for every beginner who want to begin their career in machine learning.
Some courses for every beginner in machine learning from multiple sites which can help them grow well from the beginning.
The team of 50+ global experts has done in-depth research to come up with this compilation of Best Machine Learning and Deep Learning Course for 2019. All these courses are available online and will help you learn and excel at Machine Learning and Deep Learning. These are suitable for beginners, intermediate learners as well as experts. This compilation is reviewed and updated monthly. So far, 65,000+ students and professionals have benefited from it.
Stanford Coursera CourseThis is the single highest rated course on Machine Learning on the entire internet. Created by Andrew Ng, Co-Founder of Coursera and Professor at Stanford University, the program has been taken up by more than 1,678,000 students & professionals globally, who have given it an average rating of a whopping 4.9 out of 5. One look at the testimonials and you will know why we so highly recommend it.
The topics covered in the course include supervised learning, best practices, and innovation in ML and AI, while you also get to encounter numerous case studies and applications among a host of other things. One of the best parts about the course is that you can enroll for a 7 day trial before going on to purchase the entire course. If you were to take our word for it, this is hands down the best program for the subject on the internet.
Let us just begin by absorbing the fact that 201,000+ students have taken this course and it has an average rating of 4.5 out of 5. We consider this as one of the Best Machine Learning Course and it is developed by Kirill Eremenko, Data Scientist & Forex Systems Expert and Hadelin de Ponteves, Data Scientist.
This course will help you Master Machine Learning on Python and R, make accurate predictions, build a great intuition of many machine learning models, handle specific tools like reinforcement learning, NLP and Deep Learning. Most importantly it teaches you to choose the right model for each type of problem. Basic high school mathematics is all you are supposed to know to take up this course. With 40 hours of learning + 19 articles, we don't know what else we should say to make you check this out. You can also have a look at some Machine Learning Certifications.
This micro masters program designed by Columbia University brings you a rigorous, advanced, professional and graduate level foundational class in AI and its subfields like machine learning, neural networks and more.
With a total of 4 courses in this program go over the important concepts of this topic none by one. Gain a solid foundation of the guiding principles of AI and apply the knowledge of machine learning to real-world challenges and applications. Along with this, you will also learn to design neural networks and utilize them to work on relevant problems. By the end of the program, you will have adequate practical knowledge to enhance your portfolio, apply to relevant job profiles or go freelance.
This certification course has been developed by a team of 21 lecturers, professors and researchers; and it is an advanced level journey into the world of ML. Only those with basic or intermediate knowledge around the subject should enroll for this one. You will be taught about natural language understanding, reinforcement learning, computer vision, and Bayesian methods. Some of the trainers for this program include Pavel Shvechikov, Researcher at HSE and Sberbank AI Lab, Anna Kozlova, Team Lead; Evgeny Sokolov, Senior Lecturer; Alexey Artemov, Senior Lecturer and Sergey Yudin, Analyst developer among multiple other trainers.
Data Scientist has been ranked as a topmost sought after job on Glassdoor and the average remuneration of a data scientist is above $120,000 in the United States itself. This comprehensive course will help you understand how to use Python to analyze data, create beautiful visualizations and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, ScikitLearn, Machine Learning, Plotly, Tensorflow and more.
The course has been designed by well renowned online instructor Jose Portilla, a BS and MS in Engineering from Santa Clara University. Even Tan Apaydin, who has a Ph.D. in computer science highly recommends this course.