From bf165a631995bf501d1559b0481f99a1bc0fe502 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 10:13:20 +0530 Subject: [PATCH 01/42] Updated R lesson hyperlinks Changed from .ipynb to .Rmd links --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6c1c877dc..4bb342f65 100644 --- a/README.md +++ b/README.md @@ -100,11 +100,11 @@ By ensuring that the content aligns with projects, the process is made more enga | 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model |
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zPd#;+1z1SG{Qu{hN@=;1b<{ ttU2}{YMPdh0Kr_fNV7!QrrFVo;RY#ANnN1Rl z;j9Bt;8gR~Q&;DJJHh??YQ4QP3Orn}-~!-FivU v zNZ1Ohr92Y=GM2Tt3ai9`d%!dk+oFIIX=bU5uRRm8AK@I%khv^w@YVwz$F%^MlXY!l zpKF(sfR%u2CH%8Gn7yVfg=~qp{ZA*@l;l5Mh<}hJ=Fo&EX7 C_8tex zA1O@02>Eb2oPwF~!N)5O#KWlmoh{&7m8g|(0Y8YJ|KH~al-&R8sc28SAfM7rt??t# RDIy;9N?u*A?4?EU{{rORkKh0R From 08330411ccdbd7de2bd09f6d19c764000ddaaacf Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:32:24 +0530 Subject: [PATCH 18/42] Updated formula Changed the gif formula to an image --- 8-Reinforcement/1-QLearning/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index ae8a4b5da..38b8e3111 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -186,7 +186,7 @@ Suppose we are now at the state *s*, and we want to move to the next state *s'*. This gives the **Bellman formula** for calculating the value of the Q-Table at state *s*, given action *a*: - +
Here γ is the so-called **discount factor** that determines to which extent you should prefer the current reward over the future reward and vice versa. From d09c0d81969f7630508438b79f77b06827f8fd76 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:33:32 +0530 Subject: [PATCH 19/42] Formatting fixes --- 8-Reinforcement/1-QLearning/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index 38b8e3111..9535b5019 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -316,4 +316,5 @@ Overall, it is important to remember that the success and quality of the learnin ## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/) -## Assignment [A More Realistic World](assignment.md) +## Assignment +[A More Realistic World](assignment.md) From 77a9aaa99951c3c65e96f4aa1d80a3dbd9a9c072 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:34:13 +0530 Subject: [PATCH 20/42] Formatting fixes --- 8-Reinforcement/2-Gym/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index de3eac373..8564c2058 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -331,10 +331,11 @@ You should see something like this: ## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/) -## Assignment: [Train a Mountain Car](assignment.md) +## Assignment +[Train a Mountain Car](assignment.md) ## Conclusion We have now learned how to train agents to achieve good results just by providing them a reward function that defines the desired state of the game, and by giving them an opportunity to intelligently explore the search space. We have successfully applied the Q-Learning algorithm in the cases of discrete and continuous environments, but with discrete actions. -It's important to also study situations where action state is also continuous, and when observation space is much more complex, such as the image from the Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of our forthcoming more advanced AI course. \ No newline at end of file +It's important to also study situations where action state is also continuous, and when observation space is much more complex, such as the image from the Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of our forthcoming more advanced AI course. From c0d0ef65f436cab675309e969270881a0194ed60 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:34:44 +0530 Subject: [PATCH 21/42] Removed broken link --- 8-Reinforcement/2-Gym/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index 8564c2058..b5e4237a6 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -1,7 +1,7 @@ # CartPole Skating The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**. -https://white-water-09ec41f0f.azurestaticapps.net/ + ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/) ## Introduction From 517d835c7947c152cded267ae5db380a2b69b2b8 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:37:44 +0530 Subject: [PATCH 22/42] Changed links to references --- 9-Real-World/1-Applications/README.md | 48 +++++++++------------------ 1 file changed, 16 insertions(+), 32 deletions(-) diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index 3cf3e16d2..36643172c 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -19,16 +19,14 @@ The finance sector offers many opportunities for machine learning. Many problems We learned about [k-means clustering](../../5-Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud? K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity or if something unusual is going on. As shown in the paper linked below, you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate the riskiest clusters for fraudulent versus legitimate transactions. - -https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf +[Reference](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf) ### Wealth management In wealth management, an individual or firm handles investments on behalf of their clients. Their job is to sustain and grow wealth in the long-term, so it is essential to choose investments that perform well. One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](../../2-Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression. - -http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/ +[Reference](http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/) ## 🎓 Education @@ -37,14 +35,12 @@ The educational sector is also a very interesting area where ML can be applied. ### Predicting student behavior [Coursera](https://coursera.com), an online open course provider, has a great tech blog where they discuss many engineering decisions. In this case study, they plotted a regression line to try to explore any correlation between a low NPS (Net Promoter Score) rating and course retention or drop-off. - -https://medium.com/coursera-engineering/controlled-regression-quantifying-the-impact-of-course-quality-on-learner-retention-31f956bd592a +[Reference](https://medium.com/coursera-engineering/controlled-regression-quantifying-the-impact-of-course-quality-on-learner-retention-31f956bd592a) ### Mitigating bias [Grammarly](https://grammarly.com), a writing assistant that checks for spelling and grammar errors, uses sophisticated [natural language processing systems](../../6-NLP/README.md) throughout its products. They published an interesting case study in their tech blog about how they dealt with gender bias in machine learning, which you learned about in our [introductory fairness lesson](../../1-Introduction/3-fairness/README.md). - -https://www.grammarly.com/blog/engineering/mitigating-gender-bias-in-autocorrect/ +[Reference](https://www.grammarly.com/blog/engineering/mitigating-gender-bias-in-autocorrect/) ## 👜 Retail @@ -53,14 +49,12 @@ The retail sector can definitely benefit from the use of ML, with everything fro ### Personalizing the customer journey At Wayfair, a company that sells home goods like furniture, helping customers find the right products for their taste and needs is paramount. In this article, engineers from the company describe how they use ML and NLP to "surface the right results for customers". Notably, their Query Intent Engine has been built to use entity extraction, classifier training, asset and opinion extraction, and sentiment tagging on customer reviews. This is a classic use case of how NLP works in online retail. - -https://www.aboutwayfair.com/tech-innovation/how-we-use-machine-learning-and-natural-language-processing-to-empower-search +[Reference](https://www.aboutwayfair.com/tech-innovation/how-we-use-machine-learning-and-natural-language-processing-to-empower-search) ### Inventory management Innovative, nimble companies like [StitchFix](https://stitchfix.com), a box service that ships clothing to consumers, rely heavily on ML for recommendations and inventory management. Their styling teams work together with their merchandising teams, in fact: "one of our data scientists tinkered with a genetic algorithm and applied it to apparel to predict what would be a successful piece of clothing that doesn't exist today. We brought that to the merchandise team and now they can use that as a tool." - -https://www.zdnet.com/article/how-stitch-fix-uses-machine-learning-to-master-the-science-of-styling/ +[Reference](https://www.zdnet.com/article/how-stitch-fix-uses-machine-learning-to-master-the-science-of-styling/) ## 🏥 Health Care @@ -69,20 +63,17 @@ The health care sector can leverage ML to optimize research tasks and also logis ### Managing clinical trials Toxicity in clinical trials is a major concern to drug makers. How much toxicity is tolerable? In this study, analyzing various clinical trial methods led to the development of a new approach for predicting the odds of clinical trial outcomes. Specifically, they were able to use random forest to produce a [classifier](../../4-Classification/README.md) that is able to distinguish between groups of drugs. - -https://www.sciencedirect.com/science/article/pii/S2451945616302914 +[Reference](https://www.sciencedirect.com/science/article/pii/S2451945616302914) ### Hospital readmission management Hospital care is costly, especially when patients have to be readmitted. This paper discusses a company that uses ML to predict readmission potential using [clustering](../../5-Clustering/README.md) algorithms. These clusters help analysts to "discover groups of readmissions that may share a common cause". - -https://healthmanagement.org/c/healthmanagement/issuearticle/hospital-readmissions-and-machine-learning +[Reference](https://healthmanagement.org/c/healthmanagement/issuearticle/hospital-readmissions-and-machine-learning) ### Disease management The recent pandemic has shone a bright light on the ways that machine learning can aid in stopping the spread of disease. In this article, you'll recognize the use of ARIMA, logistic curves, linear regression, and SARIMA. "This work is an attempt to calculate the rate of spread of this virus and thus to predict the deaths, recoveries, and confirmed cases, so that it may help us to prepare better and survive." - -https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979218/ +[Reference](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979218/) ## 🌲 Ecology and Green Tech @@ -93,22 +84,19 @@ Nature and ecology consists of many sensitive systems where the interplay betwee You learned about [Reinforcement Learning](../../8-Reinforcement/README.md) in previous lessons. It can be very useful when trying to predict patterns in nature. In particular, it can be used to track ecological problems like forest fires and the spread of invasive species. In Canada, a group of researchers used Reinforcement Learning to build forest wildfire dynamics models from satellite images. Using an innovative "spatially spreading process (SSP)", they envisioned a forest fire as "the agent at any cell in the landscape." "The set of actions the fire can take from a location at any point in time includes spreading north, south, east, or west or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfire spread." Read more about the classic algorithms used by this group at the link below. - -https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full +[Reference](https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full) ### Motion sensing of animals While deep learning has created a revolution in visually tracking animal movements (you can build your own [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-77952-leestott) here), classic ML still has a place in this task. Sensors to track movements of farm animals and IoT make use of this type of visual processing, but more basic ML techniques are useful to preprocess data. For example, in this paper, sheep postures were monitored and analyzed using various classifier algorithms. You might recognize the ROC curve on page 335. - -https://druckhaus-hofmann.de/gallery/31-wj-feb-2020.pdf +[Reference](https://druckhaus-hofmann.de/gallery/31-wj-feb-2020.pdf) ### ⚡️ Energy Management In our lessons on [time series forecasting](../../7-TimeSeries/README.md), we invoked the concept of smart parking meters to generate revenue for a town based on understanding supply and demand. This article discusses in detail how clustering, regression and time series forecasting combined to help predict future energy use in Ireland, based off of smart metering. - -https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf +[Reference](https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf) ## 💼 Insurance @@ -117,8 +105,7 @@ The insurance sector is another sector that uses ML to construct and optimize vi ### Volatility Management MetLife, a life insurance provider, is forthcoming with the way they analyze and mitigate volatility in their financial models. In this article you'll notice binary and ordinal classification visualizations. You'll also discover forecasting visualizations. - -https://investments.metlife.com/content/dam/metlifecom/us/investments/insights/research-topics/macro-strategy/pdf/MetLifeInvestmentManagement_MachineLearnedRanking_070920.pdf +[Reference](https://investments.metlife.com/content/dam/metlifecom/us/investments/insights/research-topics/macro-strategy/pdf/MetLifeInvestmentManagement_MachineLearnedRanking_070920.pdf) ## 🎨 Arts, Culture, and Literature @@ -127,8 +114,7 @@ In the arts, for example in journalism, there are many interesting problems. Det ### Fake news detection Detecting fake news has become a game of cat and mouse in today's media. In this article, researchers suggest that a system combining several of the ML techniques we have studied can be tested and the best model deployed: "This system is based on natural language processing to extract features from the data and then these features are used for the training of machine learning classifiers such as Naive Bayes, Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD), and Logistic Regression(LR)." - -https://www.irjet.net/archives/V7/i6/IRJET-V7I6688.pdf +[Reference](https://www.irjet.net/archives/V7/i6/IRJET-V7I6688.pdf) This article shows how combining different ML domains can produce interesting results that can help stop fake news from spreading and creating real damage; in this case, the impetus was the spread of rumors about COVID treatments that incited mob violence. @@ -137,16 +123,14 @@ This article shows how combining different ML domains can produce interesting re Museums are at the cusp of an AI revolution in which cataloging and digitizing collections and finding links between artifacts is becoming easier as technology advances. Projects such as [In Codice Ratio](https://www.sciencedirect.com/science/article/abs/pii/S0306457321001035#:~:text=1.,studies%20over%20large%20historical%20sources.) are helping unlock the mysteries of inaccessible collections such as the Vatican Archives. But, the business aspect of museums benefits from ML models as well. For example, the Art Institute of Chicago built models to predict what audiences are interested in and when they will attend expositions. The goal is to create individualized and optimized visitor experiences each time the user visits the museum. "During fiscal 2017, the model predicted attendance and admissions within 1 percent of accuracy, says Andrew Simnick, senior vice president at the Art Institute." - -https://www.chicagobusiness.com/article/20180518/ISSUE01/180519840/art-institute-of-chicago-uses-data-to-make-exhibit-choices +[Reference](https://www.chicagobusiness.com/article/20180518/ISSUE01/180519840/art-institute-of-chicago-uses-data-to-make-exhibit-choices) ## 🏷 Marketing ### Customer segmentation The most effective marketing strategies target customers in different ways based on various groupings. In this article, the uses of Clustering algorithms are discussed to support differentiated marketing. Differentiated marketing helps companies improve brand recognition, reach more customers, and make more money. - -https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/ +[Reference](https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/) ## 🚀 Challenge From f52a669646ee324c8256ac21bfa51583a2d8a148 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:52:38 +0530 Subject: [PATCH 23/42] Changed from Rmd to md format --- .../solution/R/{lesson_14.Rmd => lesson_14.md} | 12 ------------ 1 file changed, 12 deletions(-) rename 5-Clustering/1-Visualize/solution/R/{lesson_14.Rmd => lesson_14.md} (98%) diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.md similarity index 98% rename from 5-Clustering/1-Visualize/solution/R/lesson_14.Rmd rename to 5-Clustering/1-Visualize/solution/R/lesson_14.md index f2b5551f3..a6f45562a 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd +++ b/5-Clustering/1-Visualize/solution/R/lesson_14.md @@ -1,15 +1,3 @@ ---- -title: 'Introduction to clustering: Clean, prep and visualize your data' -output: - html_document: - df_print: paged - theme: flatly - highlight: breezedark - toc: yes - toc_float: yes - code_download: yes ---- - ## **Nigerian Music scraped from Spotify - an analysis** Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. From 5735ac7351ec61b093e72b33ac05239e2cb403c7 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:53:58 +0530 Subject: [PATCH 24/42] Changed from Rmd format to md format --- .../solution/R/{lesson_15.Rmd => lesson_15.md} | 13 ------------- 1 file changed, 13 deletions(-) rename 5-Clustering/2-K-Means/solution/R/{lesson_15.Rmd => lesson_15.md} (98%) diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.md similarity index 98% rename from 5-Clustering/2-K-Means/solution/R/lesson_15.Rmd rename to 5-Clustering/2-K-Means/solution/R/lesson_15.md index 61f7869a8..239ecbba6 100644 --- a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd +++ b/5-Clustering/2-K-Means/solution/R/lesson_15.md @@ -1,16 +1,3 @@ ---- -title: 'K-Means Clustering using Tidymodels and friends' -output: - html_document: - #css: style_7.css - df_print: paged - theme: flatly - highlight: breezedark - toc: yes - toc_float: yes - code_download: yes ---- - ## Explore K-Means clustering using R and Tidy data principles. ### [**Pre-lecture quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/) From 688959f773d277ea7f6a56fae9457662bc2428b2 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:56:06 +0530 Subject: [PATCH 25/42] Changed from Rmd to md format --- .../solution/R/{lesson_11.Rmd => lesson_11.md} | 12 ------------ 1 file changed, 12 deletions(-) rename 4-Classification/2-Classifiers-1/solution/R/{lesson_11.Rmd => lesson_11.md} (98%) diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.md similarity index 98% rename from 4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd rename to 4-Classification/2-Classifiers-1/solution/R/lesson_11.md index 695ac7e55..06dac0a0f 100644 --- a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.md @@ -1,15 +1,3 @@ ---- -title: 'Build a classification model: Delicious Asian and Indian Cuisines' -output: - html_document: - df_print: paged - theme: flatly - highlight: breezedark - toc: yes - toc_float: yes - code_download: yes ---- - ## Cuisine classifiers 1 In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks. From ad2b612fbb2055748d71c155b4a15a65f3f613cf Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:56:41 +0530 Subject: [PATCH 26/42] Changed from Rmd to md format --- .../solution/R/{lesson_12.Rmd => lesson_12.md} | 12 ------------ 1 file changed, 12 deletions(-) rename 4-Classification/3-Classifiers-2/solution/R/{lesson_12.Rmd => lesson_12.md} (98%) diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd b/4-Classification/3-Classifiers-2/solution/R/lesson_12.md similarity index 98% rename from 4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd rename to 4-Classification/3-Classifiers-2/solution/R/lesson_12.md index 28a348af3..9505b22cb 100644 --- a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd +++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.md @@ -1,15 +1,3 @@ ---- -title: 'Build a classification model: Delicious Asian and Indian Cuisines' -output: - html_document: - df_print: paged - theme: flatly - highlight: breezedark - toc: yes - toc_float: yes - code_download: yes ---- - ## Cuisine classifiers 2 In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other. From 88edf95b13f4bfff414300af531eb95fe015d506 Mon Sep 17 00:00:00 2001 From: Vidushi Gupta <55969597+Vidushi-Gupta@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:57:32 +0530 Subject: [PATCH 27/42] Changed Rmd files to md files --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 4bb342f65..a510eca84 100644 --- a/README.md +++ b/README.md @@ -100,11 +100,11 @@ By ensuring that the content aligns with projects, the process is made more enga | 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model |