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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-algorithms-basics-to-advanced/
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课程名称：机器学习算法：基础到高级 课程概述：如果你热衷于学习一些酷炫的机器学习算法，并掌握使用Pandas进行先进数据分析的技巧，那么这个课程就是为你量身定制的！针对现实世界中的各种复杂数据问题，专门开发了多种机器学习算法以提供完美的解决方案。同时，集成学习是一种强大的模型升级方式，它结合了多个模型，不假设单一模型是最准确的。本课程以实用的方法逐步引导学生掌握Python数据分析，帮助你探索各种机器学习算法，开发自己的集成学习模型，并高效使用这些模型。 课程结构包括三个完整的子课程，内容安排周密，以提供最全面的培训。 第一个课程，掌握Python数据分析与Pandas，学生将学习如何运用Pandas解决一些重要但简单的金融任务，例如组合建模、基于风险计算最佳投资组合等。通过实际案例，学生将深入理解Pandas在定量金融问题中的优势，掌握数据分析的基本技能。 第二个课程，七天掌握机器学习算法，将介绍数据科学与机器学习领域的七个关键算法。学生将学习如何对大数据集进行预聚类，以优化和分类数据。此外，将探讨如何基于已有趋势预测数据。本课程解决了准确和高效的数据分类与预测问题，通过七天的学习，学生能够理解针对聚类、分类和回归问题如何选择合适的算法，并自信地构建和实施数据科学算法。 第三个课程，集成机器学习技术，将向学生展示如何结合各种模型以实现比基本模型更高的准确性。这种方法在Netflix和Kaggle等竞赛中获胜方案中被广泛使用，适合希望深入了解机器学习模型并希望构建可靠模型的学习者。 讲师介绍： Prabhat Ranjan是一位在Python、R和机器学习领域拥有广泛行业经验的专家，对各种实时项目场景充满热情。他是一位激情四溢且经验丰富的讲师，擅长教授Python、R、数据科学及大数据Hadoop的概念和高级场景。 Shovon Sengupta是一名经验丰富的数据科学家，拥有超过10年的高级预测分析、机器学习、深度学习和强化学习经验，曾设计出多个获奖解决方案。 Arish Ali是一名机器学习专家，曾在印度科学研究所和微软举办的全国性机器学习竞赛中获胜，并在Mu Sigma等主要分析公司工作，专注于多触点归因模型和市场组合模型等前沿问题。 通过这个课程，你将能够系统地学习并实践机器学习算法，提升数据分析能力，实现更高的预测精度。
Are you really keen to learn some cool Machine Learning algorithms along with mastering advanced data analysis using financial examples in Pandas? Then this Course is for you!To address the complex nature of various real-world data problems, specialized Machine Learning algorithms have been developed that solve these problems perfectly. On the other hand, the Ensemble is a powerful way to upgrade your model as it combines models and doesn't assume a single model is the most accurate.This well thought out sequential course takes a practical approach to Mastering Python Data Analysis with Pandas helping you exploring various Machine Learning algorithms to develop your own Ensemble Learning models and methods to use them efficiently. Then, you will learn how to pre-cluster your data to optimize and classify it for large datasets. Along with this, you will also focus on algorithms such as k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, and much more. Finally, you will combine various models to achieve higher accuracy than base models can and develop robust models using the bagging technique.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Mastering Python Data Analysis with Pandas, you will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice. By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.The second course, Machine Learning Algorithms in 7 Days you'll learn about 7 key algorithms in the realm of Data Science and Machine Learning. You will learn how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on existing trends in your datasets. This video addresses problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. This course covers algorithms such as k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-Series. On completion of the course, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. You will be able to easily and confidently build and implement data science algorithms.The third course, Ensemble Machine Learning Techniques will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods. If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.About the Authors:Prabhat Ranjan has extensive industry experience in Python, R, and Machine Learning. He has a passion for using Python, Pandas, and R for various new, real-time project scenarios. He is a passionate and experienced trainer when it comes to teaching concepts and advanced scenarios in Python, R, data science, and big data Hadoop.His teaching experience and strong industry expertise make him the best in this arena.Shovon Sengupta is an experienced data scientist with over 10 years' experience in advanced predictive analytics, machine learning, deep learning, and reinforcement learning. He has worked extensively in designing award winning solutions for various organizations, for different business problems in the realm of Finance. Currently, he works as Senior Lead Data Scientist at one of the leading NBFCs in USA. Shovon holds an MS in Advanced Econometrics from one of the leading universities in India.Arish Ali started his machine learning journey 5 years ago by winning an All-India machine learning competition conducted by the Indian Institute of Science and Microsoft. He worked as a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some cutting-edge problems in Multi-Touch Attribution Modeling, Market Mix Modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers a course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.
