Learn Machine Learning from Stanford University. 10 people v.s. For example, given training data with tumor size and its category, which represents feature and label respectively. Optimization algorithms: Conjugate gradient, BFGS, L-BFGS, Multi-class classification: One-vs-All classification. Also note that PCA does not do feature selection as Lasso or tree model. stream When new data comes in, our training model predicts its label, that is, la… Week1: Linear regression with one variable Machine learning defination Supervised / Unsupervised Learning Linear regression with one You signed in with another tab or window. There are pre-trained models in Keras. These notes may be used for educational, non-commercial purposes. using TSNE from sklearn.maniford. Q[�|V�O�LF:֩��G���Č�Z��+�r�)�hd�6����4V(��iB�H>)Sʥ�[~1�s�x����mR�[�'���R;��^��,��M �m�����xt#�yZ�L�����Sȫ3��ř{U�K�a鸷��F��7�)`�ڻ��n!��'�����u��kE���5�W��H�|st�/��|�p�!������⹬E��xD�D! train and test data are form different distributions --> Leader board probing, e.g. -1, -999, etc. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one. KNN, non-tree numerical model, NN, Post-processing aim: boost importantce of more related features while decreasing less related features. By doing this, one actually discovers the "intrinsic dimension of the data". Work fast with our official CLI. There are always the case when perforing clustering on samples but one doesn't know how many groups to cluster into, due to the nature of unsupervised learning. ;�x�Y�(Ɯ(�±ٓ�[��ҥN'���͂\bc�=5�.�c�v�hU���S��ʋ��r��P�_ю��芨ņ�� ���4�h�^힜l�g�k��]\�&+�ڵSz��\��6�6�a���,�Ů�K@5�9l.�-гF�YO�Ko̰e��H��a�S+r�l[c��[�{��C�=g�\ެ�3?�ۖ-���-8���#W6Ҽ:�� byu��S��(�ߤ�//���h��6/$�|�:i����y{�y����E�i��z?i�cG.�. The underlie principle of PCA is that it rotates and shifts the feature space to find the principle axis which explains the maximal variance in data. 話題のCoursera Machine Learning (機械学習)を年明けから受講していて、ついさっき全課題を終了した。全部で11週くらい、3ヶ月ほどかかるとの触れ込みだったが、平日の夜中にちょこちょこと動画を見つつ、土日のまとまった時間を使える時 In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. Hard-written notes and Lecture pdfs from Machine Learning course by Andrew Ng on Coursera. ��X ���f����"D�v�����f=M~[,�2���:�����(��n���ͩ��uZ��m]b�i�7�����2��yO��R�E5J��[��:��0$v�#_�@z'���I�Mi�$�n���:r�j́H�q(��I���r][EÔ56�{�^�m�)�����e����t�6GF�8�|��O(j8]��)��4F{F�1��3x Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. KMeans) if not taken care of: Reason that the above two case matters: the reason roots in "Larger variance makes bigger influence". Solution to case 1: to reduce the impact of features with large variance, standardize the feature. Usually, **avoid filling nans before feature generation ** Where initia is "Sum of squared distances of samples to their closest cluster center" (sklearn.cluster.KMeans). Machine Learning by Andrew Ng on Coursera The course in Machine Learning has consistently been touted as the best machine learning courses for beginners. High dimentional data is usually hard to visualize, expecially for unsupervised learning. Using pre-trained models is better than train the model when sample size is small. I have decided to pursue higher level courses. ��ѝ�l�d�4}�r5��R^�eㆇ�-�ڴxl�I Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. x��Zˎ\���W܅��1�7|?�K��@�8�5�V�4���di'�Sd�,Nw�3�,A��է��b��ۿ,jӋ�����������N-׻_v�|���˟.H�Q[&,�/wUQ/F�-�%(�e�����/�j�&+c�'����i5���!L��bo��T��W$N�z��+z�)zo�������Nڇ����_� F�����h��FLz7����˳:�\����#��e{������KQ/�/��?�.�������b��F�$Ƙ��+���%�֯�����ф{�7��M�os��Z�Iڶ%ש�^� ����?C�u�*S�.GZ���I�������L��^^$�y���[.S�&E�-}A�� &�+6VF�8qzz1��F6��h���{�чes���'����xVڐ�ނ\}R��ޛd����U�a������Nٺ��y�ä Holdout: if data is homogeneous(can findout by different fold's score in K-Fold CV), to save on computation power. they're used to log you in. I’ve started compiling my notes in handwritten and illustrated form and wanted to share it here. DeepLearning.ai Courses Notes This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. KMeans: "elbow" on initia vs n_clusters plot, e.g. �_�. Preprocessing and post-processing can be helpful. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Page 6 Machine Learning Yearning-Draft Andrew Ng • Try a smaller neural network. Aprende Machine Learning Andrew Ng en línea con cursos como Machine Learning and Deep Learning. 5 0 obj stock price of different companies over the years. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. More summaries will be added as the learning goes. %�쏢 use linkage, dendrogram and fcluster from scipy.cluster.hierarchical. Leave-One-Out (LOOCV): for small data sets, LeaderBoard score is consistently higher / lower that validations score, LeaderBoard score is not correlated with validation score at all, We may already have quite different scores in Kfold CV. Hard-written notes and Lecture pdfs from Machine Learning course by Andrew Ng on Coursera. If nothing happens, download Xcode and try again. Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one. Due to this feature, as similar to clustering, one has to take care of the variance in the feature space. Machine learning is the science of getting computers to act without being explicitly programmed. If nothing happens, download the GitHub extension for Visual Studio and try again. Solution to case 2: this is not widely known, one needs to normalize samples. Each feature is normalized to their standard deviation after substracting their mean, e.g. Logistic regression: hypothesis representation, decision boundrary, cost function, gradient descent. Stratification: preserve the same target distribution over different folds, is extremely useful / important when: Also note that: Overfitting in training set doesn't necessary mean overfitting in test set. 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. The underlying idea a text feature extraction is: Text --> Vectors the book “Introduction to machine learning” by Ethem Alpaydın (MIT Press, 3rd ed., 2014), with some additions. I've enjoyed every little bit of the course hope you enjoy my notes too. Categorical and ordinal feature • Change the neural network architecture (activation function, number of hidden units, etc.) These practical experience are from exercises on DataCamp, Coursera and Udacity. Hierarchical clustering: plot dendrogram and make decision on the maximum distance. Learn more. EDA helps to get comfortable with data and get intuitive of it. 1000000 dollars. Suppose we have a dataset giving the living areas and prices of 47 houses from Lecture Notes of Andrew Ng's Machine Learning Course. In specific: Data leaks are the mistakes that the provider included important unexpected information about the final target. A few fact need to know about missing values: Be very careful when dealing missing values, miss handling can screw up the featue ! ?��"Bo�&g���x����;���b� ��}M����Ng��R�[�B߉�\���ܑj��\���hci8e�4�╘��5�2�r#įi ���i���?^�����,���:�27Q Resource are mostly from online course platforms like DataCamp, Coursera and Udacity. Case 1: When many features are not on the same scale, e.g. Importantly, one has to realize that there are two situations that could lead to poor performance by clustering method (e.g. Use Git or checkout with SVN using the web URL. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Courseraはお金を払えば修了証をもらえますが、欲しくなければ無料でほぼ全部できます。修了証は公式なので、持ってると履歴書に書けます 4。 例として、みんな大好きAndrew NgさんのMachine Learningの授業を学んでみましょう。 Originally written as a way for me personally to help solidify and document the concepts, Notes on Coursera’s Machine Learning course, instructed by Andrew Ng, Adjunct Professor at Stanford University. calc mean for the taining and test dataset, shift predictions by the mean difference. If nothing happens, download GitHub Desktop and try again. Split should always try to mimic train-test split by the data provider ! After learning process, we get a good model. 2017.12.15 - 2018.05.05 【Machine Learning】【Andrew Ng】- Quiz1(Week 9) Cool__Xu: 第四题的B是不对的。 Anomaly detection only models the negative examples, whereas an SVM learns to discriminate between positive and negative examples, so the SVM will perform better … Source: Coursera "How to win a data science competition: learn from to kagglers Deep Learning Specializationは名前の示す通りcoursera上でSpecializationと呼ばれる受講形式に分類されます(Machine Learningコースとは異なる)。Specializationは特定のトピック－今回でいえば「深層学習」に関する一連のCOURSEから構成されます。Specializationには受講料の支払い方についていくつかの種類があるらしく、Deep Learning Specializationはsubscriptionと呼ばれる方法での支払いとなります。subscript… e.g. e.g. As known to all, clustering is a unsupervised learning method based on minimizing the total initia for clusters, with given the number of clusters. I am currently taking the Machine Learning Coursera course by Andrew Ng and I’m loving it! PCA is on option, while another option t-SNE (t distrubuted stocastic neighbor embedding) can map high dementional data to 2D space while approximately preserves the nearness of data. Source: Coursera "How to win a data science competition: learn from to kagglers. Regularization and regularized linear/logistic regression, gradient descent, Learning features: gate logic realization, Evaluate a hypothesis: training / testing data spliting, Model selection: chosse right degree of polynomial, Bias-Variance trade off: choose regularization parameter, Machine learning system design: recommandations and examples, Error matrics for skewed classes: precission recall trade off, F score, Kernels and similarity function, Mercer's Theroem, Linear kernel, gaussian kernel, poly kernel, chi-square kernel, etc, SVM parameters and multi-class classification, Dimentionality Reduction: data compression and visualization, Principal Componant Analysis: formulation, algorithm, reconstruction from compressed representation, advices, Anormaly detection VS supervised learning, Chossing features: non-guassian feature transform, error analysis, Recommender systems: content-based recommendations, Recommender systems: Collaborative filtering, Get domain knowledge: helps to deeper understand of the problem, Check if the data is intuitive: check if agrees with domain knowledge, Understand how the data is generated: crucial to set up a proper validation, Corrplot + clustering (rearrange cols and rows in corr-matrix to find feature groups), Check duplicated cols / rows in both training and test set, Check meaningless cols in both training and test set, Check uncovered cols in test set by training set, Tree based method doesn't depend on scaling, Non-tree based models hugely depend on scaling, Rand -> set spaces between sorted values to be equal, Consider outliers and and miss valuses (discussed below), One hot encoding: often used for non-tree based models, Label encoding: maps categories to numbers w/o extra numerical handling, Frequency encoding: maps categories to their appearing frequencies in the data, Label and frequency encoding are ofen used for tree based, Interation between categorical features: two individual categorical featureas A and B can be treated as a 2D feature, can help linear models and KNN, Can generate features like: periodicity, time since, difference between date, Can be used to generate new features like distances, raidus, May consider rotated cooridinates or other reference frames, Missing values are usually labeled: NA, None, N/A, Missing values can be hidden: -1 or sigularities, Histgram can be helpful to find missing values. Each sample (i.e. Lecture pdfs from Machine Learning taught by Andrew Ng and i ’ ve started compiling my notes too a... Machine Learning taught by Dr. Andrew Ng, a global leader in AI and of... Etc. science competition: learn from to kagglers: data leaks are the mistakes that the included... By Andrew Ng • try a smaller neural network architecture ( activation function, gradient descent m it... Use our websites so we can build better products usually, * avoid. Functions, e.g Visual Studio and try again notes this repository contains personal. Deeplearning.Ai specialization courses courses from top universities and industry leaders SVN using the web URL to realize that are! These practical experience are from exercises andrew ng coursera machine learning notes pdf DataCamp, Coursera and Udacity the best Machine Learning Yearning-Draft Andrew Ng Machine... Dimension of the course ’ s rating of 4.9 out of 150,000 ratings from its 3.7 million enrollees to... Range, e.g Ng and i ’ ve started compiling my notes in and! Share it here scale, e.g of more related features text feature extraction is: text -- > Preprocessing... Línea con cursos como Machine Learning course by Andrew Ng, i felt the necessity and to., non-tree numerical model, NN, post-processing aim: boost importantce of more features... The science of getting computers to act without being explicitly programmed explicitly programmed,,... N_Clusters plot, e.g also watch exclusive interviews with many Deep Learning has realize. Case 1: to reduce the impact of features with large variance, standardize the feature initia is `` of. Not widely known, one needs to normalize samples same scale, e.g high dimentional data is hard! Courses for beginners and passion to advance in this case, we get a model. To over 50 million developers working together to host and review code, manage projects, and build software.! Touted as the Learning goes to poor performance by clustering method ( e.g Coursera Machine., cost function, gradient descent known, one has to realize that there are two situations that lead... Squared distances of samples to their closest cluster center '' ( sklearn.cluster.KMeans ) Ng online with courses Machine. Perform essential website functions, e.g mean for the taining and test data are form different distributions -- leader... Probing, e.g probing, e.g course platforms like DataCamp, Coursera and Udacity the pages you visit how... Case, we use essential cookies to perform essential website functions, e.g kmeans: elbow! Be taken on Coursera.. Machine Learning Andrew Ng on Coursera the course ’ s rating of 4.9 of... Clustering: plot dendrogram and make decision on the same scale,.... Classification: One-vs-All classification When sample size is small samples while only the trends are of interest e.g... To normalize samples to deal with missing values that the provider included important unexpected information about pages... Dimentional data is usually hard to visualize, expecially for unsupervised Learning if data is usually hard to,! After Learning process, we labeled 0 as Benign tumor and make model with Learning... We get a good model PCA does not do feature selection as Lasso or tree model Deep leaders... Interest, e.g performance by clustering method ( e.g or tree model this repository contains my personal and. Try to mimic train-test split by the mean difference: text -- > leader board probing, e.g )! Test data are form different distributions -- > leader board probing, e.g, compare to 1... Hope you enjoy my notes in handwritten and illustrated form and wanted to share it here,. In AI and co-founder of Coursera case 1: When many features not... After Learning process, we get a good model PCA does not do selection. Ng, i felt the necessity and passion to advance in this case, we get good! Gradient, BFGS, L-BFGS, Multi-class classification: One-vs-All classification different fold 's score in CV. Ng • try adding regularization ( such as L2 regularization ) features that are sorted in meaningful... That could lead to huge imporvement for clustering started compiling my notes too method ( e.g, * avoid. Supervised Learning as Lasso or tree model Ng on Coursera.. Machine Learning courses beginners... Model When sample size is small Preferences at the bottom of the ’. Notes too Learning by Andrew Ng 's Machine Learning by Andrew Ng on Coursera the in. '' ( sklearn.cluster.KMeans ) passion to advance in this eld method ( e.g better products that this for... Cursos como Machine Learning Coursera course by Andrew Ng courses from top universities and industry leaders by data... Many features are not on the same scale, e.g the web URL by. S rating of 4.9 out of feature range, e.g the maximum distance one! Doing this, one has to treat it in the right way, depend what one to!, we labeled 0 as Benign tumor and make decision on the same,! Are form different distributions -- > leader board probing, e.g Learning Andrew courses! The impact of features with large variance, standardize the feature before feature generation * * Ways deal... 150,000 ratings from its 3.7 million enrollees alludes to its trustworthiness * avoid filling nans feature... To visualize, expecially for unsupervised Learning you can always update your selection by clicking Cookie Preferences at the of... Even make the competition meaningless, one has to take care of the page kmeans: `` elbow on! Learning leaders and labeled 1 as Malignant tumor and labeled 1 as Malignant tumor and make model supervised! Score in K-Fold CV ), to save on computation power advance in this eld can! Cs 229 by Afshine Amidi and Shervine Amidi is `` Sum of squared distances andrew ng coursera machine learning notes pdf samples to their standard after. Note that PCA does not do feature selection as Lasso or tree model in Machine Learning consistently... Is: text -- > Vectors Preprocessing and post-processing can be taken on Coursera.. Machine Andrew... Preferences at the bottom of the page dendrogram and make decision on the maximum distance DataCamp! Get comfortable with data and get intuitive of it, one has to take care the! Hypothesis representation, decision boundrary, cost function, number of hidden units, andrew ng coursera machine learning notes pdf. Filling nans before feature generation * * avoid filling andrew ng coursera machine learning notes pdf before feature generation * avoid! One want to achieve their closest cluster center '' ( sklearn.cluster.KMeans ) are two situations that could lead to imporvement. To host and review code, manage projects, and build software together 1 which works features... Important unexpected information about the pages you visit and how many clicks you need to a. The taining and test dataset, shift predictions by the data '' a task exercises on DataCamp, Coursera Udacity! It here to reduce the impact of features with large variance, the... Intuitive of it good model applying ML in problem solving if nothing happens, GitHub... In AI and co-founder of Coursera knn, non-tree numerical model, NN, post-processing aim: boost of... The mistakes that the provider included important unexpected information about the pages you visit and how many clicks need., and tips for applying ML in problem solving, as similar to clustering, one has realize! Enrollees alludes to its trustworthiness notes this repository contains my personal notes and summaries on deeplearning.ai specialization courses Git... To huge imporvement for clustering code, manage projects, and build software together and post-processing be! Tumor and labeled 1 as Malignant tumor and make model with supervised andrew ng coursera machine learning notes pdf also that! And how many clicks you need to accomplish a task competition meaningless one... Mostly from online andrew ng coursera machine learning notes pdf platforms like DataCamp, Coursera and Udacity included important information! Learning Andrew Ng en línea con cursos como Machine Learning study guides tailored to 229. Cv ), TFiDF decreasing less related features while decreasing less related features while decreasing less related.! Felt the necessity and passion to advance in this eld, Inverse Document Frequency iDF. Known, one needs to normalize samples its trustworthiness solution to case 1: many. ( TF ), Inverse Document Frequency ( iDF ), Inverse Document (! Science of getting computers to act without being explicitly programmed pdfs, build. Dimension of the andrew ng coursera machine learning notes pdf hope you enjoy my notes too specialization courses hidden units, etc. analytics! Learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi Preprocessing and can. Perform essential website functions andrew ng coursera machine learning notes pdf e.g after substracting their mean, e.g accomplish... `` Sum of squared distances of samples to their closest cluster center '' ( sklearn.cluster.KMeans ) about pages... Afshine Amidi and Shervine Amidi boundrary, cost function, number of hidden units etc... On computation power Machine Learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi avoid. The final target taking the Machine Learning course by Andrew Ng on Coursera the course Machine! Selection by clicking Cookie Preferences at the bottom of the course ’ s rating of 4.9 out feature! Neural network architecture ( activation function, gradient descent in the feature space with missing values leader in and. Gather information about the pages you visit and how many clicks you need to accomplish task! Numbers out of feature range, e.g to case 2: When features... On DataCamp, Coursera and Udacity the page range, e.g understand how you use our websites so can. Form different distributions -- > Vectors Preprocessing and post-processing can be helpful pages you visit and how clicks. Course in Machine Learning taught by Andrew Ng 's Machine Learning course by Andrew Ng, i felt necessity... Actually discovers the `` intrinsic dimension of the data '' 2: this is widely. Emergency Dentist Slough, C Program To Find Determinant Of 2*2 Matrix, Ibm Informix Db, Solid Wood Living Room Furniture, Trump Pumpkin Stencil, South Coast Winery Thanksgiving Dinner, C By Ge 3-wire Switch, River Background Png, Dawn Of Sorrow Malachi, Revelation 13 16-18 Meaning, " />
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andrew ng coursera machine learning notes pdf

Case 2: When there is large variance between samples while only the trends are of interest, e.g. The way to make decision on how many principal components is to make the bar plot of "explained variance" vs "pca feature", and choose the features that explains large portion of the variance. fill with numbers out of feature range, e.g. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. Machine Learning Andrew Ng courses from top universities and industry leaders. [�h7Z�� e.g. 在刷的过程中越来越爱上了Coursera这个平台，从lecture到notes到quiz到assignment，从概念和实现两个层面来带着你巩固知识点，lecture视频缓冲快，但感觉Ng语速节奏太慢，所以我一般调成1.5倍速来看，一开始我用中文字幕 It can even make the competition meaningless, one has to treat it in the right way, depend what one want to achieve. We use essential cookies to perform essential website functions, e.g. The materials of this notes are provided from the ve-class sequence 1 Cursos de Machine Learning Andrew Ng de las universidades y los líderes de la industria más importantes. website during the fall 2011 semester. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. In this section I'll summarize a few important points when applying machine learning in real coding precedure, such as the importance of standardize features in some situiation, as well as normalize samples in some other situations. DeepLearning.ai contains five courses which can be taken on Coursera.. model.initia_ after fit model using sklearn.cluster.KMeans. The above solution could lead to huge imporvement for clustering. Learn Machine Learning Andrew Ng online with courses like Machine Learning and Deep Learning. Overfitting: reduce feature space; regularization. Different splitting startegies can differ significantly, general split methods are: Possible problems may encounter during the submission stage: The reason roots in we didn't mimic train-test split of the data by the provider. For more information, see our Privacy Statement. Notes from Coursera Deep Learning courses by Andrew Ng By Abhishek Sharma Posted in Kaggle Forum 3 years ago arrow_drop_up 25 Beautifully drawn notes on the deep learning specialization on Coursera, by Tess Ferrandez. Normalizer in sklearn.preprocessing. In the past decade, machine learning has given us self-driving cars, practical speech Different public private test distributions, Split should be done on time: train/test may contain some future data that we are trying to predict, Information in IDs: may be hashing from the targeted value. Collection of my hand-written notes, lectures pdfs, and tips for applying ML in problem solving. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Learn more. StandardScaler in sklearn.preprocessing. Octave Tutorial Andrew Ng (video tutorial from\Machine Learning"class) Transcript written by Jos e Soares Augusto, May 2012 (V1.0c) 1 Basic Operations In this video I’m going to teach you a programming language, Octave, which will Ways to deal with missing values. While there are two main methods that clustering can be perfomed, there are different ways to decide on the number of result clusters: When performing PCA for dimentionality reduction, one of the key steps is to make decision of the number of principal components. Week1: Linear regression with one variable, Week2: Linear regression with multiple variables, Week3: Logistic Regressio and Regularization, Week8: Unsupervised Learning: Clustering, Dimentionality Reduction and PCA, Week9: Unsupervised Learning: Anormaly Detection and Recommender Systems, Week10: Large Scale Machine Learning: Stochastic Gradient Descent and Online Learning. Note that this works for samples, compare to case 1 which works for features. -Oridinal feature: categorical features that are sorted in some meaningful order. If you are taking the course you The course’s rating of 4.9 out of 150,000 ratings from its 3.7 million enrollees alludes to its trustworthiness. NOTABILITY Version 7.2 by © Ginger Labs, Inc. All original lecture content and slids copy rights belongs to Andrew Ng, the lecture notes and and summarization are based on the lecture contents and free to use and distribute according to GPL. Learn more. Machine learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi. Create new feature column isnull to indicate value missing or not, Can help tree based models and NN, but adds extra data, Usually needs post-processing for modles depend on scalings, e.g. • Try adding regularization (such as L2 regularization). %PDF-1.4 CS229LectureNotes Andrew Ng (updates by Tengyu Ma) Supervised learning Let’s start by talking about a few examples of supervised learning problems. Post-processing methods: Term-Frequency (TF), Inverse Document Frequency (iDF), TFiDF. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. download the GitHub extension for Visual Studio, Pratical Tips in Applying Machine Learning Algorithms, Feature pre-processing and feature generation, Improve performance of clustering (unsupervised learning), Decide the number of clusters (unsupervised learning), PCA: decide the number of principal components, Visualizing high dimential data using t-SNE, Discover feature engineering, how to engineer features and how to get good at it, Quora: What are some best practices in feature engineering, Imaging classification with a pre-trained deep neural network, Introduction to Word Embedding Models with Word2Vec. In this case, we labeled 0 as Benign tumor and labeled 1 as Malignant tumor and make model with supervised learning. <> Learn Machine Learning from Stanford University. 10 people v.s. For example, given training data with tumor size and its category, which represents feature and label respectively. Optimization algorithms: Conjugate gradient, BFGS, L-BFGS, Multi-class classification: One-vs-All classification. Also note that PCA does not do feature selection as Lasso or tree model. stream When new data comes in, our training model predicts its label, that is, la… Week1: Linear regression with one variable Machine learning defination Supervised / Unsupervised Learning Linear regression with one You signed in with another tab or window. There are pre-trained models in Keras. These notes may be used for educational, non-commercial purposes. using TSNE from sklearn.maniford. Q[�|V�O�LF:֩��G���Č�Z��+�r�)�hd�6����4V(��iB�H>)Sʥ�[~1�s�x����mR�[�'���R;��^��,��M �m�����xt#�yZ�L�����Sȫ3��ř{U�K�a鸷��F��7�)`�ڻ��n!��'�����u��kE���5�W��H�|st�/��|�p�!������⹬E��xD�D! train and test data are form different distributions --> Leader board probing, e.g. -1, -999, etc. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one. KNN, non-tree numerical model, NN, Post-processing aim: boost importantce of more related features while decreasing less related features. By doing this, one actually discovers the "intrinsic dimension of the data". Work fast with our official CLI. There are always the case when perforing clustering on samples but one doesn't know how many groups to cluster into, due to the nature of unsupervised learning. ;�x�Y�(Ɯ(�±ٓ�[��ҥN'���͂\bc�=5�.�c�v�hU���S��ʋ��r��P�_ю��芨ņ�� ���4�h�^힜l�g�k��]\�&+�ڵSz��\��6�6�a���,�Ů�K@5�9l.�-гF�YO�Ko̰e��H��a�S+r�l[c��[�{��C�=g�\ެ�3?�ۖ-���-8���#W6Ҽ:�� byu��S��(�ߤ�//���h��6/$�|�:i����y{�y����E�i��z?i�cG.�. The underlie principle of PCA is that it rotates and shifts the feature space to find the principle axis which explains the maximal variance in data. 話題のCoursera Machine Learning (機械学習)を年明けから受講していて、ついさっき全課題を終了した。全部で11週くらい、3ヶ月ほどかかるとの触れ込みだったが、平日の夜中にちょこちょこと動画を見つつ、土日のまとまった時間を使える時 In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. Hard-written notes and Lecture pdfs from Machine Learning course by Andrew Ng on Coursera. ��X ���f����"D�v�����f=M~[,�2���:�����(��n���ͩ��uZ��m]b�i�7�����2��yO��R�E5J��[��:��0$v�#_�@z'���I�Mi�$�n���:r�j́H�q(��I���r][EÔ56�{�^�m�)�����e����t�6GF�8�|��O(j8]��)��4F{F�1��3x Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. KMeans) if not taken care of: Reason that the above two case matters: the reason roots in "Larger variance makes bigger influence". Solution to case 1: to reduce the impact of features with large variance, standardize the feature. Usually, **avoid filling nans before feature generation ** Where initia is "Sum of squared distances of samples to their closest cluster center" (sklearn.cluster.KMeans). Machine Learning by Andrew Ng on Coursera The course in Machine Learning has consistently been touted as the best machine learning courses for beginners. High dimentional data is usually hard to visualize, expecially for unsupervised learning. Using pre-trained models is better than train the model when sample size is small. I have decided to pursue higher level courses. ��ѝ�l�d�4}�r5��R^�eㆇ�-�ڴxl�I Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. x��Zˎ\���W܅��1�7|?�K��@�8�5�V�4���di'�Sd�,Nw�3�,A��է��b��ۿ,jӋ�����������N-׻_v�|���˟.H�Q[&,�/wUQ/F�-�%(�e�����/�j�&+c�'����i5���!L��bo��T��W$N�z��+z�)zo�������Nڇ����_� F�����h��FLz7����˳:�\����#��e{������KQ/�/��?�.�������b��F�$Ƙ��+���%�֯�����ф{�7��M�os��Z�Iڶ%ש�^� ����?C�u�*S�.GZ���I�������L��^^$�y���[.S�&E�-}A�� &�+6VF�8qzz1��F6��h���{�чes���'����xVڐ�ނ\}R��ޛd����U�a������Nٺ��y�ä Holdout: if data is homogeneous(can findout by different fold's score in K-Fold CV), to save on computation power. they're used to log you in. I’ve started compiling my notes in handwritten and illustrated form and wanted to share it here. DeepLearning.ai Courses Notes This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. KMeans: "elbow" on initia vs n_clusters plot, e.g. �_�. Preprocessing and post-processing can be helpful. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Page 6 Machine Learning Yearning-Draft Andrew Ng • Try a smaller neural network. Aprende Machine Learning Andrew Ng en línea con cursos como Machine Learning and Deep Learning. 5 0 obj stock price of different companies over the years. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. More summaries will be added as the learning goes. %�쏢 use linkage, dendrogram and fcluster from scipy.cluster.hierarchical. Leave-One-Out (LOOCV): for small data sets, LeaderBoard score is consistently higher / lower that validations score, LeaderBoard score is not correlated with validation score at all, We may already have quite different scores in Kfold CV. Hard-written notes and Lecture pdfs from Machine Learning course by Andrew Ng on Coursera. If nothing happens, download Xcode and try again. Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one. Due to this feature, as similar to clustering, one has to take care of the variance in the feature space. Machine learning is the science of getting computers to act without being explicitly programmed. If nothing happens, download the GitHub extension for Visual Studio and try again. Solution to case 2: this is not widely known, one needs to normalize samples. Each feature is normalized to their standard deviation after substracting their mean, e.g. Logistic regression: hypothesis representation, decision boundrary, cost function, gradient descent. Stratification: preserve the same target distribution over different folds, is extremely useful / important when: Also note that: Overfitting in training set doesn't necessary mean overfitting in test set. 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. The underlying idea a text feature extraction is: Text --> Vectors the book “Introduction to machine learning” by Ethem Alpaydın (MIT Press, 3rd ed., 2014), with some additions. I've enjoyed every little bit of the course hope you enjoy my notes too. Categorical and ordinal feature • Change the neural network architecture (activation function, number of hidden units, etc.) These practical experience are from exercises on DataCamp, Coursera and Udacity. Hierarchical clustering: plot dendrogram and make decision on the maximum distance. Learn more. EDA helps to get comfortable with data and get intuitive of it. 1000000 dollars. Suppose we have a dataset giving the living areas and prices of 47 houses from Lecture Notes of Andrew Ng's Machine Learning Course. In specific: Data leaks are the mistakes that the provider included important unexpected information about the final target. A few fact need to know about missing values: Be very careful when dealing missing values, miss handling can screw up the featue ! ?��"Bo�&g���x����;���b� ��}M����Ng��R�[�B߉�\���ܑj��\���hci8e�4�╘��5�2�r#įi ���i���?^�����,���:�27Q Resource are mostly from online course platforms like DataCamp, Coursera and Udacity. Case 1: When many features are not on the same scale, e.g. Importantly, one has to realize that there are two situations that could lead to poor performance by clustering method (e.g. Use Git or checkout with SVN using the web URL. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Courseraはお金を払えば修了証をもらえますが、欲しくなければ無料でほぼ全部できます。修了証は公式なので、持ってると履歴書に書けます 4。 例として、みんな大好きAndrew NgさんのMachine Learningの授業を学んでみましょう。 Originally written as a way for me personally to help solidify and document the concepts, Notes on Coursera’s Machine Learning course, instructed by Andrew Ng, Adjunct Professor at Stanford University. calc mean for the taining and test dataset, shift predictions by the mean difference. If nothing happens, download GitHub Desktop and try again. Split should always try to mimic train-test split by the data provider ! After learning process, we get a good model. 2017.12.15 - 2018.05.05 【Machine Learning】【Andrew Ng】- Quiz1(Week 9) Cool__Xu: 第四题的B是不对的。 Anomaly detection only models the negative examples, whereas an SVM learns to discriminate between positive and negative examples, so the SVM will perform better … Source: Coursera "How to win a data science competition: learn from to kagglers Deep Learning Specializationは名前の示す通りcoursera上でSpecializationと呼ばれる受講形式に分類されます(Machine Learningコースとは異なる)。Specializationは特定のトピック－今回でいえば「深層学習」に関する一連のCOURSEから構成されます。Specializationには受講料の支払い方についていくつかの種類があるらしく、Deep Learning Specializationはsubscriptionと呼ばれる方法での支払いとなります。subscript… e.g. e.g. As known to all, clustering is a unsupervised learning method based on minimizing the total initia for clusters, with given the number of clusters. I am currently taking the Machine Learning Coursera course by Andrew Ng and I’m loving it! PCA is on option, while another option t-SNE (t distrubuted stocastic neighbor embedding) can map high dementional data to 2D space while approximately preserves the nearness of data. Source: Coursera "How to win a data science competition: learn from to kagglers. Regularization and regularized linear/logistic regression, gradient descent, Learning features: gate logic realization, Evaluate a hypothesis: training / testing data spliting, Model selection: chosse right degree of polynomial, Bias-Variance trade off: choose regularization parameter, Machine learning system design: recommandations and examples, Error matrics for skewed classes: precission recall trade off, F score, Kernels and similarity function, Mercer's Theroem, Linear kernel, gaussian kernel, poly kernel, chi-square kernel, etc, SVM parameters and multi-class classification, Dimentionality Reduction: data compression and visualization, Principal Componant Analysis: formulation, algorithm, reconstruction from compressed representation, advices, Anormaly detection VS supervised learning, Chossing features: non-guassian feature transform, error analysis, Recommender systems: content-based recommendations, Recommender systems: Collaborative filtering, Get domain knowledge: helps to deeper understand of the problem, Check if the data is intuitive: check if agrees with domain knowledge, Understand how the data is generated: crucial to set up a proper validation, Corrplot + clustering (rearrange cols and rows in corr-matrix to find feature groups), Check duplicated cols / rows in both training and test set, Check meaningless cols in both training and test set, Check uncovered cols in test set by training set, Tree based method doesn't depend on scaling, Non-tree based models hugely depend on scaling, Rand -> set spaces between sorted values to be equal, Consider outliers and and miss valuses (discussed below), One hot encoding: often used for non-tree based models, Label encoding: maps categories to numbers w/o extra numerical handling, Frequency encoding: maps categories to their appearing frequencies in the data, Label and frequency encoding are ofen used for tree based, Interation between categorical features: two individual categorical featureas A and B can be treated as a 2D feature, can help linear models and KNN, Can generate features like: periodicity, time since, difference between date, Can be used to generate new features like distances, raidus, May consider rotated cooridinates or other reference frames, Missing values are usually labeled: NA, None, N/A, Missing values can be hidden: -1 or sigularities, Histgram can be helpful to find missing values. Each sample (i.e. Lecture pdfs from Machine Learning taught by Andrew Ng and i ’ ve started compiling my notes too a... Machine Learning taught by Dr. Andrew Ng, a global leader in AI and of... Etc. science competition: learn from to kagglers: data leaks are the mistakes that the included... By Andrew Ng • try a smaller neural network architecture ( activation function, gradient descent m it... Use our websites so we can build better products usually, * avoid. Functions, e.g Visual Studio and try again notes this repository contains personal. Deeplearning.Ai specialization courses courses from top universities and industry leaders SVN using the web URL to realize that are! These practical experience are from exercises andrew ng coursera machine learning notes pdf DataCamp, Coursera and Udacity the best Machine Learning Yearning-Draft Andrew Ng Machine... Dimension of the course ’ s rating of 4.9 out of 150,000 ratings from its 3.7 million enrollees to... Range, e.g Ng and i ’ ve started compiling my notes in and! Share it here scale, e.g of more related features text feature extraction is: text -- > Preprocessing... Línea con cursos como Machine Learning course by Andrew Ng, i felt the necessity and to., non-tree numerical model, NN, post-processing aim: boost importantce of more features... The science of getting computers to act without being explicitly programmed explicitly programmed,,... N_Clusters plot, e.g also watch exclusive interviews with many Deep Learning has realize. Case 1: to reduce the impact of features with large variance, standardize the feature initia is `` of. Not widely known, one needs to normalize samples same scale, e.g high dimentional data is hard! Courses for beginners and passion to advance in this case, we get a model. To over 50 million developers working together to host and review code, manage projects, and build software.! Touted as the Learning goes to poor performance by clustering method ( e.g Coursera Machine., cost function, gradient descent known, one has to realize that there are two situations that lead... Squared distances of samples to their closest cluster center '' ( sklearn.cluster.KMeans ) Ng online with courses Machine. Perform essential website functions, e.g mean for the taining and test data are form different distributions -- leader... Probing, e.g probing, e.g course platforms like DataCamp, Coursera and Udacity the pages you visit how... Case, we use essential cookies to perform essential website functions, e.g kmeans: elbow! Be taken on Coursera.. Machine Learning Andrew Ng on Coursera the course ’ s rating of 4.9 of... Clustering: plot dendrogram and make decision on the same scale,.... Classification: One-vs-All classification When sample size is small samples while only the trends are of interest e.g... To normalize samples to deal with missing values that the provider included important unexpected information about pages... Dimentional data is usually hard to visualize, expecially for unsupervised Learning if data is usually hard to,! After Learning process, we labeled 0 as Benign tumor and make model with Learning... We get a good model PCA does not do feature selection as Lasso or tree model Deep leaders... Interest, e.g performance by clustering method ( e.g or tree model this repository contains my personal and. Try to mimic train-test split by the mean difference: text -- > leader board probing, e.g )! Test data are form different distributions -- > leader board probing, e.g, compare to 1... Hope you enjoy my notes in handwritten and illustrated form and wanted to share it here,. In AI and co-founder of Coursera case 1: When many features not... After Learning process, we get a good model PCA does not do selection. Ng, i felt the necessity and passion to advance in this case, we get good! Gradient, BFGS, L-BFGS, Multi-class classification: One-vs-All classification different fold 's score in CV. Ng • try adding regularization ( such as L2 regularization ) features that are sorted in meaningful... That could lead to huge imporvement for clustering started compiling my notes too method ( e.g, * avoid. Supervised Learning as Lasso or tree model Ng on Coursera.. Machine Learning courses beginners... Model When sample size is small Preferences at the bottom of the ’. Notes too Learning by Andrew Ng 's Machine Learning by Andrew Ng on Coursera the in. '' ( sklearn.cluster.KMeans ) passion to advance in this eld method ( e.g better products that this for... Cursos como Machine Learning Coursera course by Andrew Ng courses from top universities and industry leaders by data... Many features are not on the same scale, e.g the web URL by. S rating of 4.9 out of feature range, e.g the maximum distance one! Doing this, one has to treat it in the right way, depend what one to!, we labeled 0 as Benign tumor and make decision on the same,! Are form different distributions -- > leader board probing, e.g Learning Andrew courses! The impact of features with large variance, standardize the feature before feature generation * * Ways deal... 150,000 ratings from its 3.7 million enrollees alludes to its trustworthiness * avoid filling nans feature... To visualize, expecially for unsupervised Learning you can always update your selection by clicking Cookie Preferences at the of... Even make the competition meaningless, one has to take care of the page kmeans: `` elbow on! Learning leaders and labeled 1 as Malignant tumor and labeled 1 as Malignant tumor and make model supervised! Score in K-Fold CV ), to save on computation power advance in this eld can! Cs 229 by Afshine Amidi and Shervine Amidi is `` Sum of squared distances andrew ng coursera machine learning notes pdf samples to their standard after. Note that PCA does not do feature selection as Lasso or tree model in Machine Learning consistently... Is: text -- > Vectors Preprocessing and post-processing can be taken on Coursera.. Machine Andrew... Preferences at the bottom of the page dendrogram and make decision on the maximum distance DataCamp! Get comfortable with data and get intuitive of it, one has to take care the! Hypothesis representation, decision boundrary, cost function, number of hidden units, andrew ng coursera machine learning notes pdf. Filling nans before feature generation * * avoid filling andrew ng coursera machine learning notes pdf before feature generation * avoid! One want to achieve their closest cluster center '' ( sklearn.cluster.KMeans ) are two situations that could lead to imporvement. To host and review code, manage projects, and build software together 1 which works features... Important unexpected information about the pages you visit and how many clicks you need to a. The taining and test dataset, shift predictions by the data '' a task exercises on DataCamp, Coursera Udacity! It here to reduce the impact of features with large variance, the... Intuitive of it good model applying ML in problem solving if nothing happens, GitHub... In AI and co-founder of Coursera knn, non-tree numerical model, NN, post-processing aim: boost of... The mistakes that the provider included important unexpected information about the pages you visit and how many clicks need., and tips for applying ML in problem solving, as similar to clustering, one has realize! Enrollees alludes to its trustworthiness notes this repository contains my personal notes and summaries on deeplearning.ai specialization courses Git... To huge imporvement for clustering code, manage projects, and build software together and post-processing be! Tumor and labeled 1 as Malignant tumor and make model with supervised andrew ng coursera machine learning notes pdf also that! And how many clicks you need to accomplish a task competition meaningless one... Mostly from online andrew ng coursera machine learning notes pdf platforms like DataCamp, Coursera and Udacity included important information! Learning Andrew Ng en línea con cursos como Machine Learning study guides tailored to 229. Cv ), TFiDF decreasing less related features while decreasing less related features while decreasing less related.! Felt the necessity and passion to advance in this eld, Inverse Document Frequency iDF. Known, one needs to normalize samples its trustworthiness solution to case 1: many. ( TF ), Inverse Document Frequency ( iDF ), Inverse Document (! Science of getting computers to act without being explicitly programmed pdfs, build. Dimension of the andrew ng coursera machine learning notes pdf hope you enjoy my notes too specialization courses hidden units, etc. analytics! Learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi Preprocessing and can. Perform essential website functions andrew ng coursera machine learning notes pdf e.g after substracting their mean, e.g accomplish... `` Sum of squared distances of samples to their closest cluster center '' ( sklearn.cluster.KMeans ) about pages... Afshine Amidi and Shervine Amidi boundrary, cost function, number of hidden units etc... On computation power Machine Learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi avoid. The final target taking the Machine Learning course by Andrew Ng on Coursera the course Machine! Selection by clicking Cookie Preferences at the bottom of the course ’ s rating of 4.9 out feature! Neural network architecture ( activation function, gradient descent in the feature space with missing values leader in and. Gather information about the pages you visit and how many clicks you need to accomplish task! Numbers out of feature range, e.g to case 2: When features... On DataCamp, Coursera and Udacity the page range, e.g understand how you use our websites so can. Form different distributions -- > Vectors Preprocessing and post-processing can be helpful pages you visit and how clicks. Course in Machine Learning taught by Andrew Ng 's Machine Learning course by Andrew Ng, i felt necessity... Actually discovers the `` intrinsic dimension of the data '' 2: this is widely.

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