機器學習 深度學習 ai

STRATEGY

戰略

• Learn theory + practical aspects.

  學習理論和實踐方面的知識。

  (At first get an overview of what you are going to learn).

  (首先獲得要學習的內容的概述)。

• Gain a good hold/insight on each concept.

  掌握/理解每個概念。

• If you are not comfortable with maths at first; just get yourself comfortable with why we needed that maths part, and what is its O/P. Then, come to understand it later. Never skip any concept forever.

  如果您剛開始對數學不滿意，可以使用 只是讓自己對我們為什么需要數學部分以及它的O / P感到滿意。 然后， 稍后再了解。 永遠不要跳過任何概念。

• PRACTICE, PRACTICE, PRACTICE and PRACTICE!!!

  實踐，實踐，實踐和實踐！！！

  (

  (

  Coding comes into this phase)

  編碼進入此階段)

• Understand boundary cases and failure concepts, to grap the concept of that topic.

  了解邊界情況和故障概念，以掌握該主題的概念。

• BELIEVE! Its easy;

  相信！ 它很簡單 ;

  Total of 150+ hours is good enough (5-10 hrs for 3-6 months)

  總共150個小時以上就足夠了(3-6個月5-10個小時)

SPECIAL TIPS

特別提示

• For those facing difficulty in maths (like me :))

  對于那些面臨數學困難的人(像我一樣：)

  You need to consider math as

  您需要考慮數學

  Poetry + Art.

  詩歌+藝術 。

• Eqns :- Read in English sentence → Poetry

  Eqns ：-用英語閱讀→詩歌

• Geometry :-Visualize (human–visualizing creature) → Art

  幾何 ：可視化(人類可視化的生物)→藝術

Steps and Guidelines

步驟和準則

You should note that, this is not the only way to approach for learning ML/DL. But this is really one of the best resource list for ML. You may have an option to pursue any certification course of your choice. It’s also good. I don’t discourage you for that. But, In case you want to save your money or you want to give ML a try and don’t want your money wasted in case you can’t continue. Then, you must follow some free available stuff online. And, trust me; you can never get a list better than this one. I have narrowed everything so precise so that you don’t get distracted elsewhere.

您應該注意，這不是學習ML / DL的唯一方法。 但這確實是ML 最好的資源列表之一。 您可以選擇繼續自己選擇的任何認證課程。 也不錯 我不勸阻你。 但是，如果您想省錢，或者想嘗試一下ML，并且不想浪費您的錢，以防萬一您無法繼續下去。 然后，您必須在線關注一些免費的可用內容。 而且，請相信我； 您再也找不到比這更好的清單了。 我已經將所有內容縮小到如此精確的程度，以使您不會在其他地方分心。

1) Programming language (Python or R)

1)編程語言(Python或R)

Book	Think python; ‘O’ reilly - Publication
Book	Learn python the Hard way; Zeads Hald
Site	www.Guru99.com/Python 3
Site	Python Tutorial, Tutorialspoint

書	想想python; 'O'reilly-出版
書	艱苦學習python; Zeads Hald
現場	www.Guru99.com/Python 3
現場	Python教程，Tutorialspoint

2) Probability and statistics

2)概率統計

Online course	Statistics & probability, Khan Academy
Blog	Basics of statistics for machine learning engineers I + II - -Joydeep Bhattacharjee
Slideshare	Probability basics for Machine learning (CSC2516) - Shenlong Wang*

在線課程	可汗學院統計與概率
博客	I + II機器學習工程師的統計基礎--Joydeep Bhattacharjee
幻燈片分享	機器學習的概率基礎(CSC2516)-Shenlong Wang *

3) Linear Algebra

3)線性代數

Online course - Linear Algebras; Khan Academy

在線課程-線性代數； 可汗學院

4) Calculus & Numeric Optimization

4)微積分與數值優化

Online course	Multivariable calculus, Khan Academy
pdf	Derivatives, Back propagation and vectorization; Justin Johnson
pdf	Vectors, matrix and Tensor derivatives; Erik–learned Miller

在線課程	可汗學院多變量微積分
pdf格式	導數，反向傳播和矢量化； 賈斯汀·約翰遜(Justin Johnson)
pdf格式	向量，矩陣和張量導數； 埃里克·米勒

5) Brief of Machine learning

5)機器學習簡介

Book	what you need to know about machine learning - (Packt publication) – Gabriel A. Canepa
YouTube	Intro topics for Machine Learning – UB Vzard
Blog	Analyticsvidhya

書	您需要了解的有關機器學習的知識-(Packet出版物)-Gabriel A. Canepa
的YouTube	機器學習入門主題– UB Vzard
博客	Analyticsvidhya

Note: At this stage, I would like to personally recommend you a free available online course: Machine Learning @ Kaggle | Learn
- This will give you a basic to intermediate level of understanding in ML. Plus; you would learn How to compete at different platform like Kaggle, or Hackerearth.

注意：在此階段，我個人想向您推薦免費的在線課程： 機器學習@ Kaggle | 學習
-這將使您對ML有了基本到中級的理解。 加; 您將學習如何在Kaggle或Hackerearth等不同平臺上競爭。

6) Classification and Regression technique

6)分類與回歸技術

Online course	Machine Learning, Andrew Ng; Course era/ YouTube
YouTube	Classification Techniques; UB Vzard
YouTube	Regression Techniques; UB Vzard
Blog	Analyticsvidhya

在線課程	機器學習，吳安德； 課程時代/ YouTube
的YouTube	分類技術； UB Vzard
的YouTube	回歸技術； UB Vzard
博客	Analyticsvidhya

7) Clustering Techniques

7)聚類技術

Same as above (6)
YouTube - Clustering techniques; UB Vzard

同上(6)
YouTube-群集技術； UB Vzard

8) Dimensionality Reduction

8)降維

Same as above
YouTube - Dimensionality Reduction Techniques; UB Vzard

同上
YouTube-降維技術； UB Vzard

9) Neural networks and deep learning

9)神經網絡和深度學習

Online courses

在線課程

• Deep learning; Kaggle | Learn; Dan.S.Becker

  深度學習； Kaggle | 學習; 丹·貝克爾

• Deep Learning, Andrew Ng; Course era/YouTube

  深度學習，吳安德； 課程時代/ YouTube

• Convolution Neural Networks; Stanford online/ YouTube (CS231n) (*If you want specifically CNN at broader scale.)

  卷積神經網絡 斯坦福在線/ YouTube(CS231n)(*如果您想更廣泛地專門使用CNN。)

• Deep Learning A-Z^TM; Udemy

  深度學習AZ ^TM ; 烏迪米

• U B Vzard

  UB Vzard

10) Problem solving

10)解決問題

• Kaggle.com - solve problems end to end

  Kaggle.com-端到端解決問題

• Hackerearth.com - Participate in contests

  Hackerearth.com-參加比賽

• Analyticsvidhya.com - compete in Data Hacks and Student Data fest

  Analyticsvidhya.com-參與數據黑客和學生數據節

Understand why a technique is working (or) not working

了解為什么某項技術有效(或無效)

• Document /code (GitHub or blog)

  文檔/代碼(GitHub或博客)

• Portfolio of 5 or more case studies

  5個或更多案例研究的組合

• Read other’s blog or code

  閱讀他人的博客或代碼

11) Youtube series – UB Vzard

11)Youtube系列– UB Vzard

12) LinkedIn – Get in touch with Data Science community professionals. They will Help you, guide you and most importantly motivate you.

12)LinkedIn –與數據科學社區專業人士聯系。 他們會幫助您，指導您，最重要的是激勵您。

Note: Of course; this awesome article series @ IncludeHelp. Stay tuned for totally aligned and simplest platform for insightful knowledge at ease.

注意：當然可以； 這個很棒的文章系列@ IncludeHelp 。 敬請關注完全一致且最簡單的平臺，以輕松獲取有見地的知識。

Conclusion

結論

At last, I would like to conclude that, don’t waste your crucial time wasting behind finding learning resources; although this is important before getting started. This bucket is really helpful and good enough to get you from Beginner to Advance Level. Find and mark out the best one and most suitable for you. And start over as soon as possible. And, always stick to that. You can take references from other resources too. A hearty apology, because U B Vzard is active on YouTube but it has not contained any ML videos yet; But, I am working on it with a leopard speed. You will have them ASAP. Don’t lose your hope. Trust me, it is easy. Catch you later in the next article. HAPPY LEARNING!

最后，我想得出一個結論，不要浪費您的關鍵時間來浪費學習資源； 盡管在開始之前這很重要。 這個存儲桶確實很有幫助，并且足以使您從初學者升到高級。 找到并標記出最適合您的一種。 并盡快重新開始。 并且，始終堅持這一點。 您也可以從其他資源中獲取參考。 致以誠摯的歉意，因為UB Vzard在YouTube上很活躍，但尚未包含任何ML視頻； 但是，我正在以更快的速度進行開發。 您將盡快擁有它們。 不要失去希望。 相信我，這很容易。 下一篇文章稍后會吸引您。 快樂的學習！

翻譯自: https://www.includehelp.com/ml-ai/how-to-learn-machine-learning-and-artificial-intelligence.aspx

機器學習 深度學習 ai

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