Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3 New Rating: 4.4 out of 54.4 (215 ratings) 32,564 students What you'll learn Description'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python.
All the sessions from Transform 2021 are available on-demand now. Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms. The platforms currently employed to train AI models are optimized for machine learning algorithms. AI models based on deep learning algorithms require their own deep learning operations (DLOps) platform, Spell head of marketing Tim Negris told VentureBeat. The Spell platform automates the entire deep learning workflow using tools the company developed in the course of helping organizations build and train AI models for computer vision and speech recognition applications that require deep learning algorithms.
Get a practical understanding of the Scikit-Learn library and learn the ML implementation New Rating: 4.2 out of 5 What you'll learn Description The goal of this course is to help the trainee's expertise working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand.
In a rare move for the dating app industry, Bumble is partnering with remote trauma support site Bloom to offer complimentary services to users. Bloom provides free online courses by and for survivors of sexual assault and harassment on mental health topics such as creating boundaries and managing anxiety. Chayn, a nonprofit based in the UK, created the project as part of its mission to provide resources and support for survivors of gender-based violence. The service, which will begin later this year, will be available to survivors of assault or abuse who met their abuser on the app. Bumble plans on expanding the program to include people who experienced assault no matter where they met their assailant.
The program aims to prepare community college students for careers tapping AI skills. Intel said Tuesday it's expanding a program that aims to educate tomorrow's engineers and technologists on the intricacies of artificial intelligence and help them find jobs in their chosen field. The AI for Workforce Program offers students courses on data collection, computer vision, AI model training, coding, the societal impacts and ethics of AI technology. Students who complete the program will be awarded a certificate or associate degree in artificial intelligence. The program began as a collaboration with an Arizona community college but is being expanded to 18 community colleges in 11 states through a partnership with Dell Technologies, which will provide guidance on how best to configure AI labs for teaching in-person, hybrid and online students.
If you are interested in learning to use NumPy, Pandas, Machine Learning, and more from the comfort of your home then you have landed to the right course. In this course, you will be taught all about using Python for data science and machine learning in the best possible manner. The instructor will explain how you can use spark for big data analysis in detail. Then you will get a chance to understand how to implement machine learning algorithms. Going further, you will get a chance to understand how to use Matplotlib for python plotting.
Understand stock market fundamentals Understand the Modern Portfolio Theory Understand stochastic processes and the famous Black-Scholes mode Understand Monte-Carlo simulations Understand Value-at-Risk (VaR) You should have an interest in quantitative finance as well as in mathematics and programming! This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main. Markowitz-model is the first step.
Data science and machine learning can be practiced with varying degrees of efficiency and productivity. Let's imagine somebody is teaching a "Productive Data Science" course or writing a book about it -- using Python as the language framework. What should the typical expectations be from such a course or book? The course/book should be intended for those who wish to leapfrog beyond the standard way of performing data science and machine learning tasks and utilize the full spectrum of the Python data science ecosystem for a much higher level of productivity. Readers should be taught how to look out for inefficiencies and bottlenecks in the standard process and how to think beyond the box.
Here are the results of the KDnuggets Poll inspired by this blog: Relax! As advances in AI continue to progress in leaps and bounds, accessibility to data science at a base level has become increasingly democratized. Traditional entry barriers to the field such as a lack of data and computing power have been swept aside with a continuous supply of new data startups popping up(some offering access for as little as a cup of coffee a day) and all powerful cloud computing removing the need for expensive onsite hardware. Rounding out the trinity of prerequisites, is the skill and know-how to implement, which has arguably become the most ubiquitous aspect of data science. One does not need to look far to find online tutorials touting taglines like "implement X model in seconds", "apply Z method to your data in just a few lines of code". In a digital world, instant gratification has become the name of the game.
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