{"id":1398,"date":"2019-02-15T10:32:07","date_gmt":"2019-02-15T10:32:07","guid":{"rendered":"http:\/\/kusuaks7\/?p=1003"},"modified":"2023-06-29T11:33:39","modified_gmt":"2023-06-29T11:33:39","slug":"essential-beginners-q-a-for-machine-learning-data-science-part-1","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/essential-beginners-q-a-for-machine-learning-data-science-part-1\/","title":{"rendered":"Essential beginners\u2019 Q\/A for machine learning\/data science: Part I"},"content":{"rendered":"

Ready to learn Machine Learning? Browse courses<\/a>\u00a0like\u00a0Machine Learning Foundations: Supervised Learning<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

We discuss some useful advice and Q\/A for machine learning\/data science \u2018starters\u2019. We cover key books, courses, foundation knowledge, mathematics, and programming tools needed to kickstart the\u00a0journey.<\/p><\/blockquote>\n

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General Q\/A from beginners<\/h4>\n

\u201cI\u00a0am a student of computer science\/engineering. How do I get into field of machine learning\/deep learning\/AI?\u201d<\/em><\/strong><\/p>\n

\u2014 You are lucky. When I was student, this field was still kind of \u2018chilly\u2019 and I never heard of it\u00a0\ud83d\ude41 No matter what your main area of study is, you can read about and acquire knowledge about machine learning. And please, DO NOT go straight to register for a course just because somebody gushed about it on internet or you saw the certificate on your friend\u2019s LinkedIn profile.<\/p>\n

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  1. Start with some cool videos on YouTube. Read couple of good books or articles. For example, have you read \u201cThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World<\/a>\u201d. And I can guarantee you to fall in love with\u00a0this cool interactive page about machine learning?<\/a><\/li>\n
  2. Learn to clearly differentiate between buzzwords first \u2014 machine learning, artificial intelligence, deep learning, data science, computer vision, robotics<\/em>. Read or listen to the talks, given by experts, on each of them.\u00a0Watch this amazing video by Brandon Rohrer<\/a>, an influential data scientist. Or this\u00a0video about the clear definition and difference of various roles\u00a0<\/a>associated with data science.<\/li>\n
  3. Have your goal clearly set about what you want to learn. And then, go and take a course offered by Experfy. Make sure you are comfortable with MATLAB before starting that course and you know by heart how to do a matrix multiplication.\u00a0 And please, do not take the deeplearning.ai courses before taking the foundational course.<\/li>\n
  4. Follow some good blogs<\/strong>:\u00a0KDnuggets<\/a>,\u00a0Mark Meloon\u2019s blog about data science career,\u00a0Brandon Rohrer\u2019s blog,\u00a0Open AI\u2019s blog about their research<\/a>,<\/li>\n
  5. Most of all, develop a feel for it. Join some good social forums but\u00a0resist the temptations to latch onto sensationalized headlines and news bytes<\/strong>\u00a0posted. Do your own reading, understand what it is and what it is not, where it might go, and what possibilities it can open up. Then sit back and think how you can apply machine learning or imbue data science principles into your daily work. Build a simple regression model to predict the cost of your next lunch or download your electricity usage data from your energy provider and do a simple time-series plot in Excel to discover some pattern of usage.\u00a0If you can get down to that kind of personal level, then the love affair is natural and guaranteed<\/em><\/strong>. And after you are thoroughly enamored with machine learning, you can watch this video<\/li>\n<\/ol>\n

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    https:\/\/youtu.be\/IpGxLWOIZy4<\/a><\/p>\n

    \u201cWhat are some good books to read on AI\/machine learning\/deep learning?\u201d<\/em><\/strong><\/p>\n

    \u2014 There seem to be plenty of solid answers on this question. Instead of stacking up my own bias, I am inclined to point you to some top links which show most useful curated collections,<\/p>\n

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    1. 10 free must-read books for machine learning and data science<\/a>\u00a0by\u00a0Matthew Mayo<\/a>.<\/li>\n
    2. Must Read Books for Beginners on Machine Learning and Artificial Intelligence<\/a> \u2014 by AnalyticsVidhya.<\/li>\n
    3. This awesome GitHub repo<\/a>.<\/li>\n
    4. A solid guide for all levels of practitioners<\/a>\u00a0from machinelearningmastery.com.<\/li>\n<\/ol>\n

      \u201cNow tell me about some best online courses for beginners\u201d<\/em><\/strong><\/p>\n

      \u2014 Again, couple of links and some words of advice. First the links,<\/p>\n

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      1. Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey.<\/a><\/li>\n
      2. 15 minute guide to choose effective courses for machine learning and data science<\/a>.<\/li>\n
      3. The Best Machine Learning Courses \u2014 Class Central Career Guides<\/a><\/li>\n<\/ol>\n

        The advice is that only you can decide the right sequence and pace of study through these MOOCs<\/strong>. Some people are highly attuned to take online courses and can keep themselves motivated enough to download all the programing material and practice them. Some others passionate but cannot keep awake in online video lectures. And there are plenty in between. You can get great amount of knowledge and build solid foundation as a beginner, from online courses but you have to pace them and approach them right.<\/p>\n

        \u201cOK, so what are your special tips for taking online courses? Are paid courses\/certificates worth it?\u201d<\/em><\/strong><\/p>\n

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        1. Study the curated lists of best courses (above) and visit them all. Should not cost you anything.\u00a0List down the requirements and difficult level and the breadth of topics<\/strong>\u00a0covered.<\/li>\n
        2. Always\u00a0keep a personal roadmap of things you want to learn. It should be almost like a time-series<\/strong>\u00a0i.e. definite goals to be achieved on definite time point.<\/li>\n
        3. Bring on your data scientist hat and try to fit the course with this time-series<\/strong>. Does it match i.e. teach you valuable concepts and tools that you want to acquire in near future? Does it go deeper into the framework and specializations you already know but want to polish further? Score the courses weighted on their utility and your personal requirement.<\/li>\n
        4. Make a list of top 5 courses for a quarter.\u00a0I have found it useful to think in terms of quarter.<\/strong><\/li>\n
        5. Now, look for cost implications and read reviews of past students on those top 5. Try to quickly judge which reviewer\u2019s personal situation is most like yours and weight that review highly.<\/li>\n
        6. I have mostly taken audited courses\/free option. I paid only for the courses\/certificates which I thought will give me a boost in terms of credibility in this field. Quizzes are not that important but programming assignments\/Jupyter notebooks are.\u00a0Most audited courses will let you download the Notebooks for free. That\u2019s all you need<\/strong>. Trust me, you can make those notebooks much better than the instructor provided versions if you seriously work on them diligently.<\/li>\n
        7. If you have some budget for spending, I would suggest look for\u00a0local universities offering certificates or boot camp style courses with classroom options<\/strong>. This works for working professionals too as they are mostly delivered in the evenings or on weekends.\u00a0Spend money only when you get a highly credible certificate out of it <\/strong>\u2014 preferably from good university affiliated camps or programs.<\/li>\n
        8. If you need to brush up programming language skills from scratch, there are few focused and easy-paced courses on Experfy<\/strong>. Generally, these courses are designed to teach you essentials of programming in a language of your choice without the burden of deeper subject matters.<\/li>\n
        9. Internet is one big university and library rolled into one. Consider and utilize any online resource \u2014 whole or piecemeal – as you need<\/strong>. If you don\u2019t know anything about Gits, look for specific short courses on them. If you need to know quick web-scrapping techniques with a specific language, just watch lectures on those from a bigger course. If you really think that basic data structures are mysterious, go and take a freshman level computer science course.\u00a0Harvad\u2019s CS50<\/a>\u00a0is an excellent one. Or there is even a\u00a0version of it for business professionals<\/a>\u00a0with a different flavor.<\/li>\n<\/ol>\n

          Copying Leo Tolstoy but making an inverse of it, \u201cevery bad course is similar, every great course has an unique element of greatness about it<\/em><\/strong>\u201d. Develop an eye for discovering that greatness in the top courses.<\/p>\n

          \u201cSo, what are the absolute bedrock knowledge needed to start the journey?\u201d<\/em><\/strong><\/p>\n

          \u2014 Nothing but a curiosity to learn new things and a passion to work hard for it. You have to acquire knowledge, practice, and internalize concepts as you go. But even then, some general points can be made for a structured learning approach. Again, this is from my very personal experience and therefore is subject to complete re-tuning based on your personal situation and goal.<\/p>\n

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          \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 By Camelia.boban (Own work), via Wikimedia Commons <\/p>\n<\/figcaption><\/figure>\n