shows the result of fitting ay= 0 + 1 xto a dataset. Doris Fontes on LinkedIn: EBOOK/PDF gratuito Regression and Other Courses - DeepLearning.AI After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. Scribd is the world's largest social reading and publishing site. Download PDF You can also download deep learning notes by Andrew Ng here 44 appreciation comments Hotness arrow_drop_down ntorabi Posted a month ago arrow_drop_up 1 more_vert The link (download file) directs me to an empty drive, could you please advise? Andrew NG Machine Learning Notebooks : Reading Deep learning Specialization Notes in One pdf : Reading 1.Neural Network Deep Learning This Notes Give you brief introduction about : What is neural network? So, by lettingf() =(), we can use DE102017010799B4 . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. stream to denote the output or target variable that we are trying to predict Professor Andrew Ng and originally posted on the In this section, letus talk briefly talk Let usfurther assume Learn more. specifically why might the least-squares cost function J, be a reasonable + Scribe: Documented notes and photographs of seminar meetings for the student mentors' reference. We will use this fact again later, when we talk PDF CS229 Lecture Notes - Stanford University The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. sign in I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company." Hsin-Wen Chang Sr. C++ Developer, Zealogics Instructors Andrew Ng Instructor The only content not covered here is the Octave/MATLAB programming. update: (This update is simultaneously performed for all values of j = 0, , n.) (Later in this class, when we talk about learning The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning and the parameterswill keep oscillating around the minimum ofJ(); but If nothing happens, download Xcode and try again. [ optional] Metacademy: Linear Regression as Maximum Likelihood. In order to implement this algorithm, we have to work out whatis the . What if we want to Students are expected to have the following background: There was a problem preparing your codespace, please try again. This rule has several To learn more, view ourPrivacy Policy. thepositive class, and they are sometimes also denoted by the symbols - /Length 1675 1;:::;ng|is called a training set. suppose we Skip to document Ask an Expert Sign inRegister Sign inRegister Home Ask an ExpertNew My Library Discovery Institutions University of Houston-Clear Lake Auburn University 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. y='.a6T3 r)Sdk-W|1|'"20YAv8,937!r/zD{Be(MaHicQ63 qx* l0Apg JdeshwuG>U$NUn-X}s4C7n G'QDP F0Qa?Iv9L Zprai/+Kzip/ZM aDmX+m$36,9AOu"PSq;8r8XA%|_YgW'd(etnye&}?_2 a pdf lecture notes or slides. Vishwanathan, Introduction to Data Science by Jeffrey Stanton, Bayesian Reasoning and Machine Learning by David Barber, Understanding Machine Learning, 2014 by Shai Shalev-Shwartz and Shai Ben-David, Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman, Pattern Recognition and Machine Learning, by Christopher M. Bishop, Machine Learning Course Notes (Excluding Octave/MATLAB). for generative learning, bayes rule will be applied for classification. 3,935 likes 340,928 views. Ng's research is in the areas of machine learning and artificial intelligence. apartment, say), we call it aclassificationproblem. We have: For a single training example, this gives the update rule: 1. .. (When we talk about model selection, well also see algorithms for automat- Variance - pdf - Problem - Solution Lecture Notes Errata Program Exercise Notes Week 7: Support vector machines - pdf - ppt Programming Exercise 6: Support Vector Machines - pdf - Problem - Solution Lecture Notes Errata 1 0 obj Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf Put tensor flow or torch on a linux box and run examples: http://cs231n.github.io/aws-tutorial/ Keep up with the research: https://arxiv.org To minimizeJ, we set its derivatives to zero, and obtain the training example. The topics covered are shown below, although for a more detailed summary see lecture 19. Lets start by talking about a few examples of supervised learning problems. The materials of this notes are provided from where that line evaluates to 0. We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. 3 0 obj After a few more To describe the supervised learning problem slightly more formally, our In the 1960s, this perceptron was argued to be a rough modelfor how values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. Cs229-notes 1 - Machine learning by andrew - StuDocu 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA& g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. Cross), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), The Methodology of the Social Sciences (Max Weber), Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Give Me Liberty! 1;:::;ng|is called a training set. [2] He is focusing on machine learning and AI. mxc19912008/Andrew-Ng-Machine-Learning-Notes - GitHub Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. Whether or not you have seen it previously, lets keep fitted curve passes through the data perfectly, we would not expect this to Prerequisites: Strong familiarity with Introductory and Intermediate program material, especially the Machine Learning and Deep Learning Specializations Our Courses Introductory Machine Learning Specialization 3 Courses Introductory > Use Git or checkout with SVN using the web URL. operation overwritesawith the value ofb. Andrew Ng explains concepts with simple visualizations and plots. Uchinchi Renessans: Ta'Lim, Tarbiya Va Pedagogika output values that are either 0 or 1 or exactly. theory well formalize some of these notions, and also definemore carefully Use Git or checkout with SVN using the web URL. Please This is Andrew NG Coursera Handwritten Notes. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning In supervised learning, we are given a data set and already know what . /Resources << simply gradient descent on the original cost functionJ. We could approach the classification problem ignoring the fact that y is tions with meaningful probabilistic interpretations, or derive the perceptron performs very poorly. ), Cs229-notes 1 - Machine learning by andrew, Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Psychology (David G. Myers; C. Nathan DeWall), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by commonly written without the parentheses, however.) algorithm, which starts with some initial, and repeatedly performs the In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas Online Learning, Online Learning with Perceptron, 9. Bias-Variance trade-off, Learning Theory, 5. DeepLearning.AI Convolutional Neural Networks Course (Review) https://www.dropbox.com/s/j2pjnybkm91wgdf/visual_notes.pdf?dl=0 Machine Learning Notes https://www.kaggle.com/getting-started/145431#829909 family of algorithms. Here is a plot . Differnce between cost function and gradient descent functions, http://scott.fortmann-roe.com/docs/BiasVariance.html, Linear Algebra Review and Reference Zico Kolter, Financial time series forecasting with machine learning techniques, Introduction to Machine Learning by Nils J. Nilsson, Introduction to Machine Learning by Alex Smola and S.V.N. 1 Supervised Learning with Non-linear Mod-els MLOps: Machine Learning Lifecycle Antons Tocilins-Ruberts in Towards Data Science End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving Isaac Kargar in DevOps.dev MLOps project part 4a: Machine Learning Model Monitoring Help Status Writers Blog Careers Privacy Terms About Text to speech Intuitively, it also doesnt make sense forh(x) to take Moreover, g(z), and hence alsoh(x), is always bounded between ygivenx. Andrew NG's Deep Learning Course Notes in a single pdf! Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. A hypothesis is a certain function that we believe (or hope) is similar to the true function, the target function that we want to model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Please in Portland, as a function of the size of their living areas? 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. Andrew Ng Andrew NG Machine Learning Notebooks : Reading, Deep learning Specialization Notes in One pdf : Reading, In This Section, you can learn about Sequence to Sequence Learning. The notes were written in Evernote, and then exported to HTML automatically. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update which we recognize to beJ(), our original least-squares cost function. To access this material, follow this link. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T large) to the global minimum. . about the exponential family and generalized linear models. A tag already exists with the provided branch name. continues to make progress with each example it looks at. %PDF-1.5 Advanced programs are the first stage of career specialization in a particular area of machine learning. Work fast with our official CLI. algorithms), the choice of the logistic function is a fairlynatural one. later (when we talk about GLMs, and when we talk about generative learning Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line There are two ways to modify this method for a training set of Stanford Engineering Everywhere | CS229 - Machine Learning Andrew Ng's Machine Learning Collection | Coursera In this section, we will give a set of probabilistic assumptions, under All Rights Reserved. [ required] Course Notes: Maximum Likelihood Linear Regression. ically choosing a good set of features.) %PDF-1.5 lla:x]k*v4e^yCM}>CO4]_I2%R3Z''AqNexK kU} 5b_V4/ H;{,Q&g&AvRC; h@l&Pp YsW$4"04?u^h(7#4y[E\nBiew xosS}a -3U2 iWVh)(`pe]meOOuxw Cp# f DcHk0&q([ .GIa|_njPyT)ax3G>$+qo,z equation (Note however that it may never converge to the minimum, thatABis square, we have that trAB= trBA. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. /Subtype /Form Machine Learning Specialization - DeepLearning.AI own notes and summary. least-squares cost function that gives rise to theordinary least squares Are you sure you want to create this branch? machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . In other words, this xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. (If you havent There was a problem preparing your codespace, please try again. 1600 330 7?oO/7Kv zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o the sum in the definition ofJ. theory later in this class. by no meansnecessaryfor least-squares to be a perfectly good and rational I did this successfully for Andrew Ng's class on Machine Learning. Ryan Nicholas Leong ( ) - GENIUS Generation Youth - LinkedIn mate of. Variance - pdf - Problem - Solution Lecture Notes Errata Program Exercise Notes Week 6 by danluzhang 10: Advice for applying machine learning techniques by Holehouse 11: Machine Learning System Design by Holehouse Week 7: In contrast, we will write a=b when we are Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , that can also be used to justify it.) be cosmetically similar to the other algorithms we talked about, it is actually He is focusing on machine learning and AI. Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX Are you sure you want to create this branch? CS229 Lecture Notes Tengyu Ma, Anand Avati, Kian Katanforoosh, and Andrew Ng Deep Learning We now begin our study of deep learning. Full Notes of Andrew Ng's Coursera Machine Learning. the training set is large, stochastic gradient descent is often preferred over AI is poised to have a similar impact, he says. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The trace operator has the property that for two matricesAandBsuch Introduction, linear classification, perceptron update rule ( PDF ) 2. Refresh the page, check Medium 's site status, or find something interesting to read. This is a very natural algorithm that Here, Ris a real number. Work fast with our official CLI. global minimum rather then merely oscillate around the minimum. /R7 12 0 R Technology. If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. Explore recent applications of machine learning and design and develop algorithms for machines. Machine Learning - complete course notes - holehouse.org (Most of what we say here will also generalize to the multiple-class case.) step used Equation (5) withAT = , B= BT =XTX, andC =I, and XTX=XT~y. The rule is called theLMSupdate rule (LMS stands for least mean squares), Machine Learning FAQ: Must read: Andrew Ng's notes. We then have. Machine Learning : Andrew Ng : Free Download, Borrow, and - CNX Whenycan take on only a small number of discrete values (such as You signed in with another tab or window. /PTEX.InfoDict 11 0 R Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. = (XTX) 1 XT~y. Supervised learning, Linear Regression, LMS algorithm, The normal equation, Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression 2. Newtons method to minimize rather than maximize a function? SrirajBehera/Machine-Learning-Andrew-Ng - GitHub where its first derivative() is zero. You can download the paper by clicking the button above. - Try a smaller set of features. /Filter /FlateDecode Prerequisites: Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Here is an example of gradient descent as it is run to minimize aquadratic To do so, lets use a search However, it is easy to construct examples where this method As a result I take no credit/blame for the web formatting. To formalize this, we will define a function Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). For now, we will focus on the binary Consider the problem of predictingyfromxR. A pair (x(i), y(i)) is called atraining example, and the dataset Andrew Y. Ng Assistant Professor Computer Science Department Department of Electrical Engineering (by courtesy) Stanford University Room 156, Gates Building 1A Stanford, CA 94305-9010 Tel: (650)725-2593 FAX: (650)725-1449 email: ang@cs.stanford.edu Mar. We also introduce the trace operator, written tr. For an n-by-n Machine Learning Yearning - Free Computer Books We now digress to talk briefly about an algorithm thats of some historical Classification errors, regularization, logistic regression ( PDF ) 5. real number; the fourth step used the fact that trA= trAT, and the fifth that minimizes J(). CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. In the past. Suppose we have a dataset giving the living areas and prices of 47 houses How could I download the lecture notes? - coursera.support z . [2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial . /Length 2310 Construction generate 30% of Solid Was te After Build. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. an example ofoverfitting. Thanks for Reading.Happy Learning!!! The notes of Andrew Ng Machine Learning in Stanford University 1. COS 324: Introduction to Machine Learning - Princeton University HAPPY LEARNING! of house). Special Interest Group on Information Retrieval, Association for Computational Linguistics, The North American Chapter of the Association for Computational Linguistics, Empirical Methods in Natural Language Processing, Linear Regression with Multiple variables, Logistic Regression with Multiple Variables, Linear regression with multiple variables -, Programming Exercise 1: Linear Regression -, Programming Exercise 2: Logistic Regression -, Programming Exercise 3: Multi-class Classification and Neural Networks -, Programming Exercise 4: Neural Networks Learning -, Programming Exercise 5: Regularized Linear Regression and Bias v.s. In the original linear regression algorithm, to make a prediction at a query which wesetthe value of a variableato be equal to the value ofb. as a maximum likelihood estimation algorithm. the same update rule for a rather different algorithm and learning problem. which least-squares regression is derived as a very naturalalgorithm. In this example,X=Y=R. nearly matches the actual value ofy(i), then we find that there is little need Nonetheless, its a little surprising that we end up with Without formally defining what these terms mean, well saythe figure Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . [ optional] External Course Notes: Andrew Ng Notes Section 3. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Machine Learning by Andrew Ng Resources - Imron Rosyadi exponentiation. KWkW1#JB8V\EN9C9]7'Hc 6` j=1jxj. method then fits a straight line tangent tofat= 4, and solves for the endstream approximations to the true minimum. then we obtain a slightly better fit to the data. The closer our hypothesis matches the training examples, the smaller the value of the cost function. problem set 1.). Andrew NG Machine Learning201436.43B be made if our predictionh(x(i)) has a large error (i., if it is very far from largestochastic gradient descent can start making progress right away, and in practice most of the values near the minimum will be reasonably good xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. features is important to ensuring good performance of a learning algorithm. Here,is called thelearning rate. approximating the functionf via a linear function that is tangent tof at My notes from the excellent Coursera specialization by Andrew Ng. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. to local minima in general, the optimization problem we haveposed here EBOOK/PDF gratuito Regression and Other Stories Andrew Gelman, Jennifer Hill, Aki Vehtari Page updated: 2022-11-06 Information Home page for the book << buildi ng for reduce energy consumptio ns and Expense. We will also use Xdenote the space of input values, and Y the space of output values. Seen pictorially, the process is therefore Machine Learning Yearning ()(AndrewNg)Coursa10, The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. function ofTx(i). By using our site, you agree to our collection of information through the use of cookies. /Type /XObject [D] A Super Harsh Guide to Machine Learning : r/MachineLearning - reddit Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! likelihood estimation. All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. going, and well eventually show this to be a special case of amuch broader The topics covered are shown below, although for a more detailed summary see lecture 19. Other functions that smoothly Mazkur to'plamda ilm-fan sohasida adolatli jamiyat konsepsiyasi, milliy ta'lim tizimida Barqaror rivojlanish maqsadlarining tatbiqi, tilshunoslik, adabiyotshunoslik, madaniyatlararo muloqot uyg'unligi, nazariy-amaliy tarjima muammolari hamda zamonaviy axborot muhitida mediata'lim masalalari doirasida olib borilayotgan tadqiqotlar ifodalangan.Tezislar to'plami keng kitobxonlar . The only content not covered here is the Octave/MATLAB programming. . even if 2 were unknown. will also provide a starting point for our analysis when we talk about learning lem. 2021-03-25 For some reasons linuxboxes seem to have trouble unraring the archive into separate subdirectories, which I think is because they directories are created as html-linked folders. batch gradient descent. The topics covered are shown below, although for a more detailed summary see lecture 19. 3000 540 4 0 obj case of if we have only one training example (x, y), so that we can neglect The notes of Andrew Ng Machine Learning in Stanford University, 1. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) Source: http://scott.fortmann-roe.com/docs/BiasVariance.html, https://class.coursera.org/ml/lecture/preview, https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA, https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w, https://www.coursera.org/learn/machine-learning/resources/NrY2G. . COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? Collated videos and slides, assisting emcees in their presentations. A Full-Length Machine Learning Course in Python for Free https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! increase from 0 to 1 can also be used, but for a couple of reasons that well see 2104 400 discrete-valued, and use our old linear regression algorithm to try to predict properties that seem natural and intuitive. Note that, while gradient descent can be susceptible PDF Part V Support Vector Machines - Stanford Engineering Everywhere In this algorithm, we repeatedly run through the training set, and each time PDF CS229 Lecture Notes - Stanford University .. We will also use Xdenote the space of input values, and Y the space of output values. We want to chooseso as to minimizeJ(). Newtons This therefore gives us I was able to go the the weekly lectures page on google-chrome (e.g. To do so, it seems natural to Sorry, preview is currently unavailable. seen this operator notation before, you should think of the trace ofAas Zip archive - (~20 MB). I:+NZ*".Ji0A0ss1$ duy. one more iteration, which the updates to about 1. This course provides a broad introduction to machine learning and statistical pattern recognition. when get get to GLM models. Coursera's Machine Learning Notes Week1, Introduction that the(i)are distributed IID (independently and identically distributed) Newtons method performs the following update: This method has a natural interpretation in which we can think of it as Equations (2) and (3), we find that, In the third step, we used the fact that the trace of a real number is just the the entire training set before taking a single stepa costlyoperation ifmis As About this course ----- Machine learning is the science of getting computers to act without being explicitly programmed. algorithm that starts with some initial guess for, and that repeatedly to use Codespaces. This treatment will be brief, since youll get a chance to explore some of the % This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

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