avatarSerafeim Loukas, PhD

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Abstract

            <div><p>www.coursera.org</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*yyzzvn-0S7tVRSIP)"></div>
          </div>
        </div>
      </a>
    </div><p id="25c9">This is the <b>famous</b> and must-do Deep Learning Specialization by <b>Prof. Andrew Ng.</b></p><p id="1e5a">This is a <b>long</b> specialization of 5 courses focused on Neural Networks, one of the most important algorithms nowadays, and the best to work with unstructured data (images, sound, text, video, etc.). It covers the <b>foundations</b> and <b>math</b> behind Neural Networks in the first course to <b>hyper-parameters tuning</b>, <b>project planning and strategy</b>, <b>convolutional architectures,</b> and, finally, <b>sequence models</b> architectures.</p><h1 id="3867">Books</h1><h2 id="7cd0">1. Python Data Science Handbook (PRICE: FREE)</h2><div id="5462" class="link-block">
      <a href="https://jakevdp.github.io/PythonDataScienceHandbook/">
        <div>
          <div>
            <h2>Python Data Science Handbook</h2>
            <div><h3>This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on…</h3></div>
            <div><p>jakevdp.github.io</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/)"></div>
          </div>
        </div>
      </a>
    </div><p id="95db">Python is and will be the leading language for data science and machine learning. <b>The Python Data Science Handbook is the perfect book for boosting our Python skills. </b>This is a perfect reference to keep close by for those frequent data manipulation tasks using Pandas.</p><p id="da56">This book covers <b>IPython</b>, <b>Numpy</b> for computations, Data manipulation with <b>Pandas</b>, Data visualizations with <b>Matplotlib</b>, Machine learning with <b>Scikit</b>-<b>Learn</b>.</p><p id="d3c8"><i>Programming language: Python3</i></p><h2 id="6913">2. Introduction to Statistical Learning (PRICE: FREE)</h2><div id="b0ba" class="link-block">
      <a href="http://faculty.marshall.usc.edu/gareth-james/ISL/">
        <div>
          <div>
            <h2>Introduction to Statistical Learning</h2>
            <div><h3>with Applications in R Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Home Download the book PDF…</h3></div>
            <div><p>faculty.marshall.usc.edu</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*p6hL7_CNG1jGDyQQ)"></div>
          </div>
        </div>
      </a>
    </div><p id="285e">One of the best introductory textbooks for machine learning. It provides easy to understand explanations of concepts and coding examples with R.</p><p id="916b">The book covers K-fold cross-validation, Regularization, Feature selection, Polynomial regression, Decision Trees, Support vector machines, Unsupervised learning i.e. Clustering.</p><p id="1e75"><i>Programming language: R</i></p><h2 id="a9dc">3. The Elements of Statistical Learning (PRICE: FREE)</h2><figure id="0a63"><img src="https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*Ymk54ceyubXXrBN3KKL2KQ.jpeg"><figcaption></figcaption></figure><p id="e692">This book covers everything from linear methods to neural nets, boosting, and random forests.</p><p id="4d87">Download link: <a href="https://web.stanford.edu/~hastie/ElemStatLearn/">https://web.stanford.edu/~hastie/ElemStatLearn/</a></p><h2 id="46f2">4. Understanding Machine Learning: From Theory to Algorithms (PRICE: FREE)</h2><div id="1f97" class="link-block">
      <a href="https://www.cambridge.org/core/books/understanding-machine-learning/3059695661405D25673058E43C8BE2A6">
        <div>
          <div>
            <h2>Understanding Machine Learning by Shai Shalev-Shwartz</h2>
            <div><h3>Machine learning is one of the fastest-growing areas of computer science, with far-reaching applications. The aim of…</h3></div>
            <div><p>www.cambridge.org</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*_P01SoTS4LdAj3cK)"></div>
          </div>
        </div>
      </a>
    </div><p id="ad3c">This is the gold standard book if you want to get a deeper understanding of machine learning algorithms. This book provides an extensive theory on the most famous and widely used algorithms.</p><p id="0079">Download link: <a href="http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf">http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf</a></p><h2 id="c81e">5. Deep Learning (An MIT Press book) (PRICE: FREE)</h2><div id="885b" class="link-block">
      <a href="https://www.deeplearningbook.org/">
        <div>
          <div>
            <h2>Deep Learning</h2>
            <div><h3>The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine…</h3></div>
            <div><p>www.deeplearningbook.org</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/)"></div>
          </div>
        </div>
      </a>
    </div><p id="2df2"><b>I could not finish this post without adding a free book for deep learning. </b>The <b>Deep Learning textbook</b> is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. <b>The online version of the book is now complete and will remain available online for free.</b></p><h2 id="eb15">6. Think Stats (PRICE: FREE)</h2><div id="adf4" class="link-block">
      <a href="https://www.goodreads.com/book/show/12042357-think-stats">
        <div>
          <div>
            <h2>Think Stats</h2>
            <div><h3>Think Stats book. Read 47 reviews from the world’s largest community for readers. If you know how to program, you have…</h3></div>
            <div><p>www.goodreads.com</p></div>
          </div>
          <div>
            <div style="background-image: url(https://miro.readmedium

Options

.com/v2/resize:fit:320/0*3ziZhNt3IVS8C_Uu)"></div> </div> </div> </a> </div><p id="608c">Link to Download it: <a href="http://greenteapress.com/thinkstats/">http://greenteapress.com/thinkstats/</a></p><p id="de86"><b>I could not finish this post without adding a free book for statistics.</b></p><p id="d2b8">As a data scientist, it’s important that you have a solid grasp of probability and statistics. Machine learning models are rooted in the fundamentals of probability theory.</p><p id="4413">This book covers Descriptive statistics, distribution functions, Probability, Hypothesis testing, and many others.</p><p id="def2">That’s all folks! I hope you liked this article!</p><h1 id="d4cf">Stay tuned & support this effort</h1><p id="db8b">If you liked and found this article useful, <b>follow</b> me to be able to see all my new posts.</p><p id="5f90">My profile if you want to follow me:</p><div id="8d43" class="link-block"> <a href="https://medium.com/@seralouk"> <div> <div> <h2>Serafeim Loukas — Medium</h2> <div><h3>Read writing from Serafeim Loukas on Medium. Diploma in Electrical & Computer Engineering (NTUA). Master of Science in…</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*R_tpW50K0OfhC-fr)"></div> </div> </div> </a> </div><p id="032c">Questions? Post them as a comment and I will reply as soon as possible.</p><h1 id="98d4">Latest posts</h1><div id="f9c8" class="link-block"> <a href="https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-facebooks-prophet-model-9ee1657132b5"> <div> <div> <h2>Time-Series Forecasting: Predicting Stock Prices Using Facebook’s Prophet Model</h2> <div><h3>Predict stock prices using a forecasting model publicly available from Facebook: The Prophet</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*hnJmoDkR6-inqCe_JRxW0w.png)"></div> </div> </div> </a> </div><div id="4ebb" class="link-block"> <a href="https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c"> <div> <div> <h2>ROC Curve Explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification…</h2> <div><h3>In this post, I clearly explain what a ROC curve is and how to read it. I use a COVID-19 example to make my point and I…</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*qW3Mobeew1xxnXJnBPy8LQ.jpeg)"></div> </div> </div> </a> </div><div id="8c2a" class="link-block"> <a href="https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8"> <div> <div> <h2>Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems…</h2> <div><h3>In this article, I explain the core of the SVMs, why, and how to use them. Additionally, I show how to plot the support…</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*z_B0o4JbD0C6gpmcenUc4w.jpeg)"></div> </div> </div> </a> </div><div id="eef8" class="link-block"> <a href="https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e"> <div> <div> <h2>PCA clearly explained — How, when, why to use it and feature importance: A guide in Python</h2> <div><h3>In this post, I explain what PCA is, when, and why to use it and how to implement it in Python using scikit-learn. Also…</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*ba0XpZtJrgh7UpzWcIgZ1Q.jpeg)"></div> </div> </div> </a> </div><div id="381c" class="link-block"> <a href="https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79"> <div> <div> <h2>Everything you need to know about Min-Max normalization in Python</h2> <div><h3>In this post, I explain what Min-Max scaling is, when to use it and how to implement it in Python using scikit-learn but…</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*44jKK-vMeP4EGGvyIXPypg.png)"></div> </div> </div> </a> </div><div id="01f5" class="link-block"> <a href="https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832"> <div> <div> <h2>How Scikit-Learn’s StandardScaler works</h2> <div><h3>In this post, I am explaining why and how to apply Standardization using scikit-learn</h3></div> <div><p>towardsdatascience.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*UPLv3kNw9JTtNabr70dQDQ.png)"></div> </div> </div> </a> </div><h1 id="7cae">Get in touch with me</h1><ul><li><b>LinkedIn</b>: <a href="https://www.linkedin.com/in/serafeim-loukas/">https://www.linkedin.com/in/serafeim-loukas/</a></li><li><b>ResearchGate</b>: <a href="https://www.researchgate.net/profile/Serafeim_Loukas">https://www.researchgate.net/profile/Serafeim_Loukas</a></li></ul></article></body>

Data Science, Opinion

The Best Free Data Science Resources: Books & Online Courses

The most useful and free books and online courses for anyone who wants to learn more about Data Science.

Photo by Franki Chamaki on Unsplash

Introduction

I decided to write this post in order to provide some of the most useful books and online courses for anyone who wants to learn more about Data Science. All of the resources that I will provide are FREE (open source). The provided resources cover topics from basic data visualization to fitting a complex machine learning model and understanding the applied statistics behind the models.

  • NEW: After a great deal of hard work and staying behind the scenes for quite a while, we’re excited to now offer our expertise through a platform, the “Data Science Hub” on Patreon (https://www.patreon.com/TheDataScienceHub). This hub is our way of providing you with bespoke consulting services and comprehensive responses to all your inquiries, ranging from Machine Learning to strategic data analytics planning.
  • Another resource. Learn Data Science and ML with the help of an 🤖 AI-powered tutor. Start here https://aigents.co/learn choose a topic and he will show up where you need him. No paywall, no signups, no ads.

Before we start

I want to make clear that online courses will not make you a Data Scientist. To become a Data Scientist you need a combination of things such as:

  • An engineering/computer science degree/diploma (not mandatory, but helps)
  • Knowledge about the tools used (e.g. what are the most frequently used clustering methods?)
  • Practical experience on real-world and on projects (e.g. by joining some of the infinite Kaggle competitions or by having a data-science-related job)
  • Theoretical knowledge of how data structures, systems, and algorithms work under the hood (e.g. knowledge of calculus, statistics, applied maths & programming).

As stated before the following courses and books alone are not going to make you a Data Scientist but these resources will enable you to learn a lot of things about the Data Science 101 i.e. from visualization, programming, how the models work, how to plot a decision surface of an SVM model to building a neural network.

The courses

1. Introduction to Computer Science and Programming Using Python (PRICE: FREE)

This is the best course in my personal opinion in order to learn Python 3.

Programming language: Python3

2. Machine Learning by Stanford University (PRICE: FREE)

Probably the most famous course about Machine Learning offered by the Coursera platform. The famous professor and AI advocate Andrew Ng, from Stanford University, is an instructor for this online course. The course is amazing and focuses on explaining the most famous Machine Learning algorithms, including its math foundations.

Programming language: Python3

3. Learning From Data (Introductory Machine Learning) (PRICE: FREE)

Introductory Machine Learning course covers theory, algorithms, and applications. The focus is on real understanding, not just “knowing. It is offered by the California Institute of Technology (CALTEC).

4. Deep Learning Specialization (PRICE: FREE)

This is the famous and must-do Deep Learning Specialization by Prof. Andrew Ng.

This is a long specialization of 5 courses focused on Neural Networks, one of the most important algorithms nowadays, and the best to work with unstructured data (images, sound, text, video, etc.). It covers the foundations and math behind Neural Networks in the first course to hyper-parameters tuning, project planning and strategy, convolutional architectures, and, finally, sequence models architectures.

Books

1. Python Data Science Handbook (PRICE: FREE)

Python is and will be the leading language for data science and machine learning. The Python Data Science Handbook is the perfect book for boosting our Python skills. This is a perfect reference to keep close by for those frequent data manipulation tasks using Pandas.

This book covers IPython, Numpy for computations, Data manipulation with Pandas, Data visualizations with Matplotlib, Machine learning with Scikit-Learn.

Programming language: Python3

2. Introduction to Statistical Learning (PRICE: FREE)

One of the best introductory textbooks for machine learning. It provides easy to understand explanations of concepts and coding examples with R.

The book covers K-fold cross-validation, Regularization, Feature selection, Polynomial regression, Decision Trees, Support vector machines, Unsupervised learning i.e. Clustering.

Programming language: R

3. The Elements of Statistical Learning (PRICE: FREE)

This book covers everything from linear methods to neural nets, boosting, and random forests.

Download link: https://web.stanford.edu/~hastie/ElemStatLearn/

4. Understanding Machine Learning: From Theory to Algorithms (PRICE: FREE)

This is the gold standard book if you want to get a deeper understanding of machine learning algorithms. This book provides an extensive theory on the most famous and widely used algorithms.

Download link: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

5. Deep Learning (An MIT Press book) (PRICE: FREE)

I could not finish this post without adding a free book for deep learning. The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

6. Think Stats (PRICE: FREE)

Link to Download it: http://greenteapress.com/thinkstats/

I could not finish this post without adding a free book for statistics.

As a data scientist, it’s important that you have a solid grasp of probability and statistics. Machine learning models are rooted in the fundamentals of probability theory.

This book covers Descriptive statistics, distribution functions, Probability, Hypothesis testing, and many others.

That’s all folks! I hope you liked this article!

Stay tuned & support this effort

If you liked and found this article useful, follow me to be able to see all my new posts.

My profile if you want to follow me:

Questions? Post them as a comment and I will reply as soon as possible.

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