This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you're struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.

Amazon India is inviting applications from students for the second edition of its online Machine Learning (ML) summer school. The course aims to teach key machine learning technologies by scientists at Amazon and to make the candidates industry-ready for careers in science. The free course, which was launched in 2021, is open to students enrolled in any recognised institute in India, who are expected to graduate in 2023 or 2024. Rajeev Rastogi, vice president of applied science in ML, says, "The tutorial sessions covering the right mix of theoretical and practical knowledge will be delivered by our ML scientists who are experts in their field. This programme will be a platform to help foster ML excellence and strive towards developing applied science skills in young talent."

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

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.

Are you looking for the Udacity AI Product Manager Nanodegree Review?… If yes, this latest Udacity AI Product Manager Nanodegree Review will help you to decide whether to enroll in the program or not. So, without further ado, let's get started- You are looking for Udacity AI Product Manager Nanodegree Review, which means you have a doubt about whether to enroll in this program or not. And this doubt is common because Udacity Nanodegree Programs are expensive as compared to other MOOCs programs. So, I will help you to decide whether to invest in this expensive Nanodegree Program or not. Along with that, I will also share my tips and tricks to save a few bucks while enrolling in the Udacity AI Product Manager Nanodegree Program.

We consider prediction with expert advice for strongly convex and bounded losses, and investigate trade-offs between regret and "variance" (i.e., squared difference of learner's predictions and best expert predictions). With $K$ experts, the Exponentially Weighted Average (EWA) algorithm is known to achieve $O(\log K)$ regret. We prove that a variant of EWA either achieves a negative regret (i.e., the algorithm outperforms the best expert), or guarantees a $O(\log K)$ bound on both variance and regret. Building on this result, we show several examples of how variance of predictions can be exploited in learning. In the online to batch analysis, we show that a large empirical variance allows to stop the online to batch conversion early and outperform the risk of the best predictor in the class. We also recover the optimal rate of model selection aggregation when we do not consider early stopping. In online prediction with corrupted losses, we show that the effect of corruption on the regret can be compensated by a large variance. In online selective sampling, we design an algorithm that samples less when the variance is large, while guaranteeing the optimal regret bound in expectation. In online learning with abstention, we use a similar term as the variance to derive the first high-probability $O(\log K)$ regret bound in this setting. Finally, we extend our results to the setting of online linear regression.

DataCamp, Data skills from non-coding essentials to data science and machine learning, 350+, free access to fist course chapter, from $25/month …

This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.