{"id":10683,"date":"2020-10-24T07:29:00","date_gmt":"2020-10-24T07:29:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=10683"},"modified":"2023-10-17T09:15:48","modified_gmt":"2023-10-17T09:15:48","slug":"top-books-kickstart-machine-learning-journey","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/top-books-kickstart-machine-learning-journey\/","title":{"rendered":"Top 3 Books to Kickstart Your Machine Learning Journey"},"content":{"rendered":"\t\t
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Machine learning isn\u2019t as hard as it used to be. New books <\/a> and courses come out daily, and it is becoming harder to pick the ones you\u2019ll stick to. This list should help you get there somewhat quick, irrelevant of your background.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The list has something for everyone \u2013 no matter if you\u2019re on a tight schedule or have little to none machine learning experience. The books aren\u2019t ordered in any way, so pick the one you feel most comfortable with.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Let\u2019s start with the first one.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Hundred-Page Machine Learning Book<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It\u2019s more of a 150-page machine learning book, but you get the point. It\u2019s designed for people in a hurry, as it doesn\u2019t dwell that much on the details, but still manages to get everything important covered. It starts from the basics \u2013 introduction to machine learning and the most important statistical concepts (like random variables and Bayes theorem).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The story doesn\u2019t end there, as every major supervised algorithm is well explained, from linear regression<\/em> to \nsupport vector machines<\/em>. Unsupervised algorithms are also covered, but later. Essential concepts from feature engineering, like one-hot encoding, normalization\/standardization, and handling missing data are also covered.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The book also goes through the most important metrics for model evaluation, such as accuracy, precision, recall, and AUC\/ROC curves. You can also expect to get some coverage of deep learning techniques, but the book is pretty vague there.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Overall, it is a no-brainer for anyone starting out or anyone who doesn\u2019t have the time to sit through an 800-page book.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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If you\u2019re the type of person willing to spend months going through 800+ page book \u2013 congrats, you\u2019re in for a treat. This book is a long-time best-seller on Amazon, just because it covers everything one might need to work in the field, explained with perfect clarity. Seriously, the book covers topics from the machine learning definition to GANs<\/em> and reinforcement learning<\/em>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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I\u2019m glad this book was mandatory on my machine learning college course, as I\u2019ve learned much more from it than from the professor. If your professor isn\u2019t Andrew Ng<\/em>, but more someone like Siraj<\/em>, I reckon it will do the same.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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As mentioned earlier, the books goes from simple topics, like data gathering, EDA, feature scaling, to actual machine learning through algorithms such as decision trees, random forest, and gradient boosting. It also covers the main dimensionality reduction techniques and unsupervised learning. All of that in the first 300 pages!<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The rest is reserved for neural networks and deep learning, from theory to application in the TensorFlow<\/em> library. Expect to learn a lot about ANNs, CNNs, RNNs, Autoencoders, GANs, and reinforcement learning.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Another no brainer if you have the time. And will.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Deep Learning for Coders with Fastai and PyTorch<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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PyTorch<\/em> is my all-time favorite deep learning library, and FastAI<\/em> is another one worth your time, as it uses PyTorch to develop production-ready models \u2013 you\u2019ve guessed it \u2013 fast. It is aimed towards software developers, as it\u2019s focused more on the practical part. It\u2019s also perfectly suitable for data scientists not willing to dive too deep into the theory. The theoretical part is available later in the book, but I don\u2019t see it as a mandatory read.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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As mentioned before, the main focus is on the\u00a0 FastAI\u00a0library, which is widely used to handle computer vision, tabular, NLP, and recommender system tasks with as little code as possible. The\u00a0official website<\/a>\u00a0offers some free courses, so it\u2019s a good idea to check these out.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The book covers the previously mentioned four topics in much depth, which is just enough to use the power of deep learning in your applications.<\/p>\n

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Overall, it\u2019s an excellent book, definitely worth your time.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Parting words<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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These are the three books I found particularly useful and easy enough to read. There\u2019s no need to read all of them, so refer to the bullets below if you cannot decide:<\/p>\n



  • Read the first book if you\u2019re starting with machine learning and don\u2019t have much of free time<\/li>
  • Read the last book if you\u2019re interested in practical machine learning and deep learning<\/li>
  • Read the second book if you have the time to learn both theoretical and practical aspects of the field<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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    And that does it for this time. Thanks for reading.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    Machine learning isn\u2019t as hard as it used to be. New books and courses come out daily, and it is becoming harder to pick the ones you\u2019ll stick to. This list should help you get there somewhat quick, irrelevant of your background.<\/p>\n","protected":false},"author":947,"featured_media":10689,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94,482,92],"ppma_author":[3858],"class_list":["post-10683","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science","tag-influential-books","tag-machine-learning"],"authors":[{"term_id":3858,"user_id":947,"is_guest":0,"slug":"dario","display_name":"Dario Rade\u010di\u0107","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/381c757328f431480808d4196b2ff4ab662300331f3b0367b78cd801c9d01110?s=96&d=mm&r=g","user_url":"https:\/\/www.betterdatascience.com\/%20","last_name":"Rade\u010di\u0107","first_name":"Dario","job_title":"","description":"Dario Rade\u010di\u0107 is Consultant at NEOS, a leading company in Data Warehouse, Business Intelligence and Application Development fields."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10683","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/947"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=10683"}],"version-history":[{"count":6,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10683\/revisions"}],"predecessor-version":[{"id":33494,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10683\/revisions\/33494"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/10689"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=10683"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=10683"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=10683"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=10683"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}