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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statsml_x/
课程评论：没有评论
课程名称：掌握统计学与机器学习：直觉、数学与代码 课程概述：统计学和概率无处不在，影响着我们生活的方方面面。从YouTube的推荐算法，到潜在伴侣的邂逅，再到地震、股市走势和天气预测，许多现象都与概率和统计学息息相关。因此，我们需要掌握统计学。几乎所有人类文明的领域都在应用代码和数值计算，尤其是在数据科学、人工智能和商业智能的领域。因此，具备统计学和机器学习的知识对未来的工作及学习至关重要。课程将涵盖概率论、置信区间、k均值聚类、主成分分析、Spearman相关系数和逻辑回归等概念，并致力于在Python和MATLAB等编程语言中实践这些理论。 学习此课程的六个理由： 1. 理解统计学、机器学习和数据科学的基础知识，包括条形图、方差分析、回归分析等内容。 2. 掌握广泛的统计和机器学习分析方法，即使是一些高级方法也能打下扎实基础。 3. 课程提供数学严谨性和直观解释的良好平衡，结合实际代码的探索。 4. 课程期间可访问问答区，与教师进行互动。 5. 讲师拥有超过20年的统计学研究和教学经验，热爱数学。 6. 适合不具备统计学、机器学习或数据科学背景的学习者，无需编程经验即可完成课程。 学习本课程之前须具备：高中水平的数学基础；基本的Python或MATLAB编码技能（若希望进行编码练习）。虽然代码练习有助于加深理解，但不写代码也能顺利完成课程。 若有疑问，课程设有问答区，讲师会尽量在一天内回复所有问题，鼓励学员参与讨论提升学习效果。 如果你认真考虑学习统计学和机器学习，可以通过观看课程预览视频和查看评价来决定是否报名。 通过本课程，你将为未来的数据科学、工程、研究等领域打下坚实的基础，跃身成为数据领域的专业人才。
Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.You need to understand statistics.Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field - ranging from data scientist to engineering to research scientist to deep learning modeler - you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.There are six reasons why you should take this course:This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.Enrolling in the course gives you access to the Q & A, in which I actively participate every day.I've been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.What you need to know before taking this course:High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.I recommend taking my free course called "Statistics literacy for non-statisticians". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here!Is this course up to date?Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!). You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!What if you have questions about the material?This course has a Q & A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q & A by posting to ongoing discussions. And, you can also post your code for feedback or just to show off - I love it when students actually write better code than me! (Ahem, doesn't happen so often.)What should you do now?First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!
