Data Science for Engineers IITM
Data Science for Engineers IITM
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#47 K - Nearest Neighbors Implementation in R | Data Science for Engineers
Welcome to 'Data Science for Engineers' course !
This lecture demonstrates implementing the kNN algorithm in R using a case study. It covers:
Reading and understanding data
Implementing kNN using the knn function
Evaluating model performance using a confusion matrix
Discussing practical considerations for using kNN
NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications.
To understand various certification options for this course, please visit nptel.ac.in/courses/106106179
#kNN #RProgramming #CaseStudy #AlgorithmImplementation
มุมมอง: 9 613

วีดีโอ

#48 K - means Clustering | Data Science for Engineers
มุมมอง 10K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture introduces K-means clustering, an unsupervised learning algorithm for partitioning data into clusters: The concept of clustering and its applications The K-means algorithm and its steps Choosing the optimal number of clusters using techniques like the elbow method Interpreting clustering results NPTEL Courses permit certifications th...
#49 K - means Implementation in R | Data Science for Engineers
มุมมอง 16K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture demonstrates implementing the K-means clustering algorithm in R using a case study. It covers: Reading and understanding data Implementing K-means using the kmeans function Interpreting clustering results and visualizing clusters Practical considerations for using K-means NPTEL Courses permit certifications that can be used for Cours...
#50 Data Science for Engineers - Summary | Data Science for Engineers
มุมมอง 6K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture summarizes the key concepts covered in the course on Data Science for Engineers and suggests directions for further learning: A review of the modules covered in the course The importance of practicing and applying the learned concepts Suggestions for further exploration of machine learning algorithms and advanced data science techniq...
#46 K - Nearest Neighbors (kNN) | Data Science for Engineers
มุมมอง 12K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture introduces the k-Nearest Neighbors (kNN) algorithm, a simple yet powerful non-parametric classification technique: The concept of nearest neighbors and distance metrics Choosing the optimal value for k Feature scaling and its importance in kNN Advantages and disadvantages of kNN NPTEL Courses permit certifications that can be used fo...
#40 Multiple Linear Regression Modelling Building & Selection | Data Science for Engineers
มุมมอง 7K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture extends the concepts of linear regression to multiple independent variables. It covers: Building multiple linear regression models in R using the lm function Interpreting the model summary and identifying significant variables Model selection techniques for choosing the best subset of variables NPTEL Courses permit certifications tha...
#41 Classification | Data Science for Engineers
มุมมอง 6K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture introduces classification problems and algorithms, a key aspect of data science. It covers: The concept of classification and its applications Different types of classification algorithms (linear, non-linear, parametric, non-parametric) Characteristics and evaluation of classification models NPTEL Courses permit certifications that c...
#44 Performance Measures | Data Science for Engineers
มุมมอง 7K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture focuses on performance measures for evaluating classification models, including: The confusion matrix and its interpretation Metrics like accuracy, sensitivity, specificity, precision, and F1-score Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) Understanding the trade-offs between different performance m...
#43 Logisitic Regression | Part 2 | Data Science for Engineers
มุมมอง 5K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture continues the discussion on logistic regression, covering: Regularization techniques to prevent overfitting The concept of overfitting and its implications Evaluating model performance using a test dataset Calculating probabilities and classifying data points based on a threshold NPTEL Courses permit certifications that can be used f...
#45 Logistic Regression Implementation in R | Data Science for Engineers
มุมมอง 6K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture presents a case study on implementing logistic regression in R, covering: Reading data from CSV files using read.csv Understanding data structure and summary Building a logistic regression model using the glm function Interpreting model coefficients and evaluating performance using a confusion matrix NPTEL Courses permit certificatio...
#42 Logisitic Regression | Part 1 | Data Science for Engineers
มุมมอง 8K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture introduces logistic regression, a classification technique that models probabilities for class membership: Using a sigmoidal function to represent probabilities Estimating model parameters using maximum likelihood estimation Constructing linear and non-linear decision boundaries NPTEL Courses permit certifications that can be used fo...
#39 Cross Validation | Data Science for Engineers
มุมมอง 7K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture introduces cross-validation, a technique for model selection and hyperparameter tuning. It covers: The purpose and benefits of cross-validation Different cross-validation methods (k-fold, leave-one-out) Using cross-validation to select the best model and optimize hyperparameters NPTEL Courses permit certifications that can be used fo...
#31 Module :Predictive Modelling | Data Science for Engineers
มุมมอง 10K6 ปีที่แล้ว
#31 Module :Predictive Modelling | Data Science for Engineers
#37 Simple Linear Regression Model Assessment | Part 2 | Data Science for Engineers
มุมมอง 5K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture delves deeper into model assessment for simple linear regression, focusing on: Using residual plots to assess the linearity and equal variance assumptions Using quantile-quantile (Q-Q) plots to check the normality assumption of residuals Understanding the implications of violations of these assumptions NPTEL Courses permit certificat...
#38 Multiple Linear Regression | Data Science for Engineers
มุมมอง 7K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture explores techniques for refining simple linear regression models, including: Removing outliers or influential data points Applying transformations to variables to improve linearity or normality Re-evaluating the refined model using the techniques discussed in previous lectures NPTEL Courses permit certifications that can be used for ...
#35 Simple Linear Regression Model Building | Data Science for Engineers
มุมมอง 10K6 ปีที่แล้ว
Welcome to 'Data Science for Engineers' course ! This lecture continues the discussion on model assessment, focusing on practical aspects: Generating residual plots using R Identifying outliers and their indices using the identify function Refining the model by removing or adjusting outlier data points NPTEL Courses permit certifications that can be used for Course Credits in Indian Universitie...
#33 Model Assessment | Data Science for Engineers
มุมมอง 9K6 ปีที่แล้ว
#33 Model Assessment | Data Science for Engineers
#32 Linear Regression | Data Science for Engineers
มุมมอง 12K6 ปีที่แล้ว
#32 Linear Regression | Data Science for Engineers
#36 Simple Linear Regression Model Assessment | Part 1 | Data Science for Engineers
มุมมอง 6K6 ปีที่แล้ว
#36 Simple Linear Regression Model Assessment | Part 1 | Data Science for Engineers
#34 Diagnostics to Improve Linear Model Fit | Data Science for Engineers
มุมมอง 9K6 ปีที่แล้ว
#34 Diagnostics to Improve Linear Model Fit | Data Science for Engineers
#30 Solving Data Analysis Problems - A Guided Thought Process | Data Science for Engineers
มุมมอง 14K6 ปีที่แล้ว
#30 Solving Data Analysis Problems - A Guided Thought Process | Data Science for Engineers
#28 Multivariate Optimization With Inequality Constraints | Data Science for Engineers
มุมมอง 10K6 ปีที่แล้ว
#28 Multivariate Optimization With Inequality Constraints | Data Science for Engineers
#29 Introduction to Data Science | Data Science for Engineers
มุมมอง 10K6 ปีที่แล้ว
#29 Introduction to Data Science | Data Science for Engineers
#27 Multivariate Optimization With Equality Constraints | Data Science for Engineers
มุมมอง 11K6 ปีที่แล้ว
#27 Multivariate Optimization With Equality Constraints | Data Science for Engineers
#26 Gradient(Steepest) Descent & Learning Rule | Data Science for Engineers
มุมมอง 12K6 ปีที่แล้ว
#26 Gradient(Steepest) Descent & Learning Rule | Data Science for Engineers
#24 Unconstrained Multivariate Optimization | Part 1 | Data Science for Engineers
มุมมอง 13K6 ปีที่แล้ว
#24 Unconstrained Multivariate Optimization | Part 1 | Data Science for Engineers
#25 Unconstrained Multivariate Optimization | Part 2 | Data Science for Engineers
มุมมอง 8K6 ปีที่แล้ว
#25 Unconstrained Multivariate Optimization | Part 2 | Data Science for Engineers
#23 Optimization for Data Science | Data Science for Engineers
มุมมอง 21K6 ปีที่แล้ว
#23 Optimization for Data Science | Data Science for Engineers
#19 Statistical Modelling | Data Science for Engineers
มุมมอง 31K6 ปีที่แล้ว
#19 Statistical Modelling | Data Science for Engineers
#21 Sample Statistics | Data Science for Engineers
มุมมอง 12K6 ปีที่แล้ว
#21 Sample Statistics | Data Science for Engineers

ความคิดเห็น

  • @COMEDY_007..
    @COMEDY_007.. 7 หลายเดือนก่อน

    Ye to galat bol rha hai

  • @vasanthplays8776
    @vasanthplays8776 7 หลายเดือนก่อน

    Translations aren't accurate

  • @vasanthplays8776
    @vasanthplays8776 8 หลายเดือนก่อน

    multiple errors on slides, please correct

  • @NavnitPatellibrarian
    @NavnitPatellibrarian 8 หลายเดือนก่อน

    Thank You for the Computer Data Science Information

  • @svk0071
    @svk0071 ปีที่แล้ว

    c() function stands for 'Combine' in R.

  • @jiyabyju
    @jiyabyju ปีที่แล้ว

    thank you sir

  • @jiyabyju
    @jiyabyju ปีที่แล้ว

    best explanation

  • @karthikeya62
    @karthikeya62 ปีที่แล้ว

    More examples are needed and must be of video sharing of coding simply passing the slides is useless

  • @SSMDesignsandresearch
    @SSMDesignsandresearch ปีที่แล้ว

    Great Explanation

  • @dattajv
    @dattajv ปีที่แล้ว

    excellent! A novice can also clearly understand the concepts. Thanks to the faculty and to NPTEL

  • @krishnendubiswas4615
    @krishnendubiswas4615 ปีที่แล้ว

    Shaktimaan intro?

  • @SurajSingh-lu8ei
    @SurajSingh-lu8ei ปีที่แล้ว

    it feels like he is teaching to some experts, i didn't get anything . Very poor quality of teaching

  • @Noob-nv6bx
    @Noob-nv6bx ปีที่แล้ว

    th-cam.com/play/PLTNMv857s9WVzutwxaMb0YZKW7hoveGLS.html Thank me later

  • @020hamza2
    @020hamza2 ปีที่แล้ว

    I like other youtubers the way in which they teach which we can understand but NPTL is too long videos and boring because I not understand

  • @dancingvideo933
    @dancingvideo933 ปีที่แล้ว

    Excellent sir clear presentation and clarity

  • @mallikajay631
    @mallikajay631 ปีที่แล้ว

    Thank you so much sir!!

  • @dineshkumard2445
    @dineshkumard2445 2 ปีที่แล้ว

    Shall we complete this course and attend this course's exam through python?

  • @sabyasachimukhopadhyay6498
    @sabyasachimukhopadhyay6498 2 ปีที่แล้ว

    Great introduction.

  • @praveenjagarlapudi7891
    @praveenjagarlapudi7891 2 ปีที่แล้ว

    I heard the similar lecture from other Professor but this is the best explanation. Thank you Sir.

  • @mackmenezes4912
    @mackmenezes4912 2 ปีที่แล้ว

    Bad explanation ,should give example problems and solve them And for the programming ones they should implement and show

  • @shubhankarsharma2221
    @shubhankarsharma2221 2 ปีที่แล้ว

    poor qualtiy

  • @Alive6371
    @Alive6371 2 ปีที่แล้ว

    Pathetic !

  • @niftkislay
    @niftkislay 2 ปีที่แล้ว

    Sir. What is the geometric intuition behind the complementary slackness. Why is the multiplier positive for maximisation problems. These questions unresolved would make a student uninterested.

  • @kapilgupta5928
    @kapilgupta5928 2 ปีที่แล้ว

    I am curious why you provide the lectures with Subtitle "English NPTEL Verified" for non-English Subtitles

  • @kapilgupta5928
    @kapilgupta5928 2 ปีที่แล้ว

    for kkt conditions one may watch th-cam.com/video/uh1Dk68cfWs/w-d-xo.html

  • @kapilgupta5928
    @kapilgupta5928 2 ปีที่แล้ว

    at 12:08, the median will also get changed to 71 as after adding 50 to 55, the value goes from left half to right half, shifting the median

  • @vinitseth22
    @vinitseth22 2 ปีที่แล้ว

    Voice is too low

  • @vinitseth22
    @vinitseth22 2 ปีที่แล้ว

    Your voice is very low sir

  • @tamilselvan9942
    @tamilselvan9942 2 ปีที่แล้ว

    The whole course is crisp and exactly to the point.... Thank you sir and thank you team....

  • @wordsofsanjeev810
    @wordsofsanjeev810 2 ปีที่แล้ว

    From where can I get the sample data, to practice!?

  • @metallicafanboi4467
    @metallicafanboi4467 3 ปีที่แล้ว

    Not only engineers but other students can benefit from it as well. I became a fan of this guy.

  • @johnnovotny4286
    @johnnovotny4286 3 ปีที่แล้ว

    Excellent. Clear. Thanks.

  • @yasarahmedshaik6623
    @yasarahmedshaik6623 3 ปีที่แล้ว

    Thankyou Sir for a very nice way of explanation

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Thanks for sharing this! Those who struggle to understand this a bit can refer to following video (it heped me a lot): th-cam.com/video/sDv4f4s2SB8/w-d-xo.html&ab_channel=StatQuestwithJoshStarmer

  • @sameerpurwar4836
    @sameerpurwar4836 3 ปีที่แล้ว

    Why so many downvotes. ???

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Very well taought; thanks a ton!

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Well taught. What would have helped is defining/establishing a connection of these theoretical concepts with real world data science applications; how would these be applied or relevance of knowing this.

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Thanks, very well taught!

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Thanks a ton for doing this! :)

  • @rishisingh6111
    @rishisingh6111 3 ปีที่แล้ว

    Very well taught, thanks a ton! Not sure who are the jokers who did not like this video!

  • @mohammedsabitchowdhury7320
    @mohammedsabitchowdhury7320 3 ปีที่แล้ว

    Outside from india can enroll this course?

  • @amyzeng7130
    @amyzeng7130 3 ปีที่แล้ว

    I am a little lost, why x' is on the line of (o,n), thanks!

    • @amyzeng7130
      @amyzeng7130 3 ปีที่แล้ว

      Got it. n is orthogonal to the line, so (o,n) is orthogonal to the line of x'n+b=0. Thanks!

  • @KKumar-xn8zf
    @KKumar-xn8zf 3 ปีที่แล้ว

    I think the slides are wrong , because the transpose should be for normal vector not for the X vector , and the inequalities which are differentiating the half-space is not correct , they must not be strict .

  • @fatemeimanizade4276
    @fatemeimanizade4276 3 ปีที่แล้ว

    Thank you so much. It really helped. I wish if you could recommend a related reference about hyper-dimensional space and specifically hyperplanes.

  • @nissimarcus1376
    @nissimarcus1376 3 ปีที่แล้ว

    Can anyone share the link of the dataset

  • @rupendrajaiswal5747
    @rupendrajaiswal5747 3 ปีที่แล้ว

    sir study material provide kera dijiye

  • @prithvivasanth3114
    @prithvivasanth3114 3 ปีที่แล้ว

    Hello sir does nptel have python data science course?

  • @pittalaabhishek
    @pittalaabhishek 3 ปีที่แล้ว

    No clarity Sir

    • @johnnovotny4286
      @johnnovotny4286 3 ปีที่แล้ว

      Consider completing some examples first. You might find the lack of clarity is in your own understanding.

  • @shubhamthedeveloper
    @shubhamthedeveloper 3 ปีที่แล้ว

    now I am worried because lot of students will get confused because of poor english. And again IIT held course on theory slides for programming language. Who is poor in basics of programming , don't watch this videos. u can follows other hands on courses. after completing course come to nptel and solve assignment questions here. They are good ones.

  • @5gacademy
    @5gacademy 3 ปีที่แล้ว

    old video