内容简介:A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was
If I had to start learning Data Science again. ¿How would I do it?
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.
I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.
So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.
As you will see my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.
Kaggle micro-courses
I know it may be weird to start here, many would prefer to start with the heaviest foundations and math videos to fully understand what is happening behind each ML model. But from my perspective starting with something practical and concrete helps to have a better view of the whole picture.
In addition, these micro-courses take around 4 hours/each to complete so meeting those little goals up front adds an extra motivational boost.
Kaggle micro-course: Python
If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.
Link: https://www.kaggle.com/learn/python
Price: Free
Kaggle micro-course: Pandas
Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.
Link: https://www.kaggle.com/learn/pandas
Price: Free
Kaggle micro-course: Data Visualization
Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.
Link: https://www.kaggle.com/learn/data-visualization
Price: Free
Kaggle micro-course: Intro to Machine Learning
This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.
Link: https://www.kaggle.com/learn/intro-to-machine-learning
Precio: Free
Kaggle micro-course: Intermediate Machine Learning
This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.
Link: https://www.kaggle.com/learn/intermediate-machine-learning
Price: Free
Let’s stop here for a moment. It should be clear that these 5 micro-courses are not going to be a linear process, you are probably going to have to come and go between them to refresh concepts. When you are working in the Pandas one you may have to go back to the Python course to remember some of the things you learned or go to the pandas documentation to understand new functions that you saw in the Introduction to Machine Learning course. And all of this is fine, right here is where the real learning is going to happen.
Now, if you realize these first 5 courses will give you the necessary skills to do exploratory data analysis (EDA) and create baseline models that later you will be able to improve. So now is the right time to start with simple Kaggle competitions and put in practice what you’ve learned.
Kaggle Playground Competition: Titanic
Here you’ll put into practice what you learned in the introductory courses. Maybe it will be a little intimidating at first, but it doesn’t matter it’s not about being first in the leaderboard, it’s about learning. In this competition you will learn about classification and relevant metrics for these types of problems such as precision, recall and accuracy.
Link: https://www.kaggle.com/c/titanic
Kaggle Playground Competition: Housing Prices
In this competition you are going to apply regression models and learn about relevant metrics such as RMSE.
Link: https://www.kaggle.com/c/home-data-for-ml-course
By this point you already have a lot of practical experience and you’ll feel that you can solve a lot of problems, buuut chances are that you don’t fully understand what is happening behind each classification and regression algorithms that you have used. So this is where we have to study the foundations of what we are learning.
Many courses start here, but at least I absorb this information better once I have worked on something practical before.
Book: Data Science from Scratch
At this point we will momentarily separate ourselves from pandas, scikit-learn and other Python libraries to learn in a practical way what is happening “behind” these algorithms.
This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.
Link: Amazon
Price: $26 aprox
If you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums and explore new methods that you find in other participants solutions.
Online Course: Machine Learning by Andrew Ng
Here we are going to see many of the things that we have already learned but we are going to watch it explained by one of the leaders in the field and his approach is going to be more mathematical so it will be an excellent way to understand our models even more.
Link: https://www.coursera.org/learn/machine-learning
Price: Free without the certificate — $79 with the certificate
Book: The Elements of Statistical Learning
Now the heavy math part starts. Imagine if we had started from here, it would have been an uphill road all along and we probably would have given up easier.
Link: Amazon
Price: $60, there is an official free version in the Stanford page.
Online Course: Deep Learning by Andrew Ng
By then you have probably already read about deep learning and play with some models. But here we are going to learn the foundations of what neural networks are, how they work and learn to implement and apply the different architectures that exist.
Link: https://www.deeplearning.ai/deep-learning-specialization/
Price: $49/month
At this point it depends a lot on your own interests, you can focus on regression and time series problems or maybe go more deep into deep learning.
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
快速傅里叶变换
K. R. Rao、D. N. Kim、J. J. Hwang / 万帅、杨付正 / 机械工业出版社 / 2013-3 / 98.00元
《国际信息工程先进技术译丛·快速傅里叶变换:算法与应用》深入浅出地阐述了快速傅里叶变换(FFT)的原理,系统地总结了各类FFT算法,并广泛精辟地介绍了FFT在视频和音频信号处理中的各种应用。《国际信息工程先进技术译丛·快速傅里叶变换:算法与应用》在阐述了离散傅里叶变换(DFT)的原理和性质之后,详细讨论了时域抽取(DIT)和频域抽取(DIF)的各类快速算法。论述了近似计算DFT的整数FFT、二维及......一起来看看 《快速傅里叶变换》 这本书的介绍吧!