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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ml-and-python-in-finance-real-cases-and-practical-solutions/
课程评论：没有评论
课程名称：Python与机器学习在金融分析中的应用 课程概述：您是否准备好学习Python编程基础并将其直接应用于金融和银行领域的实际应用中？如果您的回答是肯定的，欢迎加入“完整的Python与机器学习在金融分析中的应用”课程。在这里，您将学习到开发实用金融/银行应用所需的所有知识。 为什么选择Python？Python被评选为2020年最值得学习的编程语言，原因包括： 1. 人工智能与机器学习的首选语言。 2. 容易学习，尤其对编程新手友好。 3. Python开发者需求大，但供应不足，非常适合现在学习。 4. 平均年薪约为116,000美元。 5. 可扩展性强，许多知名应用（如谷歌、Instagram、YouTube、Spotify）均基于Python开发。 6. 多功能性，适用于数据科学、金融分析、机器学习等多个领域。 课程独特之处： 1. 课程分为三个主要部分： a) Python编程基础：涵盖数据类型、变量赋值、循环、条件语句、函数和文件操作，并介绍数据科学必备库如Numpy和Pandas，以及数据可视化工具如Matplotlib、Seaborn、Plotly和Bokeh。 b) Python中的金融分析：涉及日常投资组合回报、风险与Sharpe比率、资本资产定价模型（CAPM）、马科维茨投资组合优化及有效前沿，此外还涵盖动量交易和移动平均交易策略。 c) 金融/银行领域的AI/ML：实践项目涉及深度神经网络（如LSTM）进行股票价格预测，K-Means聚类和主成分分析用于银行客户细分，基础自然语言处理（NLP）用于股票情感分析。 2. 多个迷你挑战和练习贯穿整个课程，采用实践学习的方式，几乎每个视频均有编码练习，使学习更加直观。 3. 基于项目的学习方法：您将共同构建6个以上的实际项目，可以添加至个人项目集，展示给未来雇主。 适合人群： - 希望通过数据科学和AI优化业务流程的金融分析师。 - 希望获得Python及数据科学基础知识的Python初学者和数据科学家。 - 希望提升职业生涯、建立数据科学作品集并获得实践经验的投资银行家和金融分析师。 无需任何编程经验，课程会从基础知识开始，逐步提升。 在本课程中，您将获得： 1. 实际项目为基础的学习体验，构建6个以上的项目； 2. 访问所有代码和幻灯片； 3. 完成证书，可展示在LinkedIn以展现您的Python编程技能； 4. 30天无风险退款保证，您可以放心尝试。 欢迎今天注册，我期待在课堂上见到您！
Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking?If the answer is yes, then welcome to the "The Complete Python and Machine Learning for Financial Analysis" course in which you will learn everything you need to develop practical real-world finance/banking applications in Python!So why Python?Python is ranked as the number one programming language to learn in 2020, here are 6 reasons you need to learn Python right now!1. #1 language for AI & Machine Learning: Python is the #1 programming language for machine learning and artificial intelligence.2. Easy to learn: Python is one of the easiest programming language to learn especially of you have not done any coding in the past.3. Jobs: high demand and low supply of python developers make it the ideal programming language to learn now.4. High salary: Average salary of Python programmers in the US is around $116 thousand dollars a year.5. Scalability: Python is extremely powerful and scalable and therefore real-world apps such as Google, Instagram, YouTube, and Spotify are all built on Python.6. Versatility: Python is the most versatile programming language in the world, you can use it for data science, financial analysis, machine learning, computer vision, data analysis and visualization, web development, gaming and robotics applications.This course is unique in many ways:1. The course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry. A detailed overview is shown below:a) Part #1 - Python Programming Fundamentals: Beginner's Python programming fundamentals covering concepts such as: data types, variables assignments, loops, conditional statements, functions, and Files operations. In addition, this section will cover key Python libraries for data science such as Numpy and Pandas. Furthermore, this section covers data visualization tools such as Matplotlib, Seaborn, Plotly, and Bokeh.b) Part #2 - Financial Analysis in Python: This part covers Python for financial analysis. We will cover key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio. In addition, we will cover Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier. We will also cover trading strategies such as momentum-based and moving average trading.c) Part #3 - AI/Ml in Finance/Banking: This section covers practical projects on AI/ML applications in Finance. We will cover application of Deep Neural Networks such as Long Short Term Memory (LSTM) networks to perform stock price predictions. In addition, we will cover unsupervised machine learning strategies such as K-Means Clustering and Principal Components Analysis to perform Baking Customer Segmentation or Clustering. Furthermore, we will cover the basics of Natural Language Processing (NLP) and apply it to perform stocks sentiment analysis.2. There are several mini challenges and exercises throughout the course and you will learn by doing. The course contains mini challenges and coding exercises in almost every video so you will learn in a practical and easy way.3. The Project-based learning approach: you will build more than 6 full practical projects that you can add to your portfolio of projects to showcase your future employer during job interviews.So who is this course for?This course is geared towards the following:Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors.Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.There is no prior experience required, Even if you have never used python or any programming language before, don't worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge.In this course, (1) you will have a true practical project-based learning experience, we will build more than 6 projects together (2) You will have access to all the codes and slides, (3) You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in python programming to employers. (4) All of this comes with a 30 day money back guarantee so you can give a course a try risk free! Check out the preview videos and the outline to get an idea of the projects we will be covering.Enroll today and I look forward to seeing you inside!
