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Scaler Data Science and Machine Learning with Python

Scaler Data Science and Machine Learning with Python#

Course Information#

Scaler Data Science and Machine Learning with Python

  • Instructor: Scaler

  • Duration: 10 weeks

  • Level: Intermediate

Table of Contents#

  1. DA ML 1Problem Solving

  2. DA ML1 Interview Preparation

  3. DA ML_ Intro to Clustering, k-Means

  4. DA K-means ++, Hierarchical

  5. Business Case _ Ola Review, Scaler Intro

  6. DA Anomaly_Outlier_Novelty Detection

  7. DA High Dimension viz, PCA, t-SNE, UMAP

  8. DA ML, Recommender Systems-1

  9. DA ML, Recommender Systems-2

  10. DA ML, Recommender Systems-3

  11. Business Case _ Scaler Review, Zee Intro

  12. DA ML, Time Series Analysis - 1

  13. DA ML, Time Series Analysis - 2

  14. DA ML, Regular Problem Solving

  15. DA ML, Time Series Analysis - 3

  16. Business Case_ Zee Review, Ad Ease Intro

  17. DA ML, Time Series Analysis - Use Cases

  18. DA ML 2, Neural Network, deep learning -Intro

  19. DA ML 2, Neural Network 1

  20. DA ML 2, Neural Network 2

  21. Adv SQL _ Getting Started (Intro to GCP, interface, Refresher)

  22. Adv SQL _ Writing efficient queries

  23. Adv SQL _ Indexes and Partitioning

  24. Adv SQL_ Interview Questions Part 1

  25. Adv SQL_ Interview Questions Part 2

  26. Adv SQL Capstone_ Customer Journey Discussion

  27. Adv SQL Capstone_ Customer Journey Review 1

  28. Adv SQL Capstone_ Customer Journey Review 2

  29. Adv SQL _ Business Intelligence using Dataiku (DSS)

  30. Adv SQL _ Business Intelligence using Dataiku (DSS) 2

  31. Product Strategy & Business Acumen

  32. Product Metrics - 1

  33. Product Metrics - 2

  34. Root Cause Analysis - 1

  35. Root Cause Analysis - 2

  36. CRM Analytics - RFM model

  37. Customer Segmentation

  38. A_B Testing & Launch Recommendation

  39. Guess Estimate - 1

  40. Guess Estimate - 2

  41. Flight Overbooking problem

  42. dare to dream

  43. flowcharts

  44. pusedocode

  45. python introduction

  46. python intro ii

  47. programming fundamentals i

  48. programming fundamentals ii

  49. programming fundamentals iii

  50. functions and methods lecture

  51. ds list

  52. ds collection tuple set dict

  53. strings

  54. maths i

  55. python maths ii

  56. problem solving

  57. OOPS

  58. sorting and searching

  59. problem solving

  60. time complexity i

  61. time complexity ii

  62. time complexity follow up

  63. introduction to arrays

  64. arrays prefix sum

  65. arrays carry forward

  66. arrays subarrays

  67. arrays 2d matrices

  68. arrays interview problems

  69. bit manipulation i

  70. bit manipulation ii

  71. module arithmetic

  72. arrays and maths

  73. sorting

  74. strings

  75. strings and doubts

  76. hashing i

  77. hashing ii

  78. subsequence and subsets

  79. recursion i

  80. recursion ii

  81. recursion follow up

  82. ll basics

  83. python refresher

  84. stacks and queues

  85. python refresher ii

  86. python refresher iii

  87. oops

  88. modules and exceptions

  89. exception handling

  90. numpy i

  91. numpy ii

  92. numpy iii

  93. numpy iii remedial

  94. pandas i

  95. pandas ii

  96. pandas 2b

  97. pandas iii

  98. matplotlib

  99. business case introduction

  100. seaborn

  101. python regular expression

  102. probability and statistics i

  103. probability and statistics ii

  104. probability distribution i

  105. problem solving

  106. probability distribution ii

  107. Netflix

  108. probability distribution iii

  109. probability distribution iiii

  110. problem solving i numpy

  111. sampling statistics

  112. clt confidence

  113. clt problem solving

  114. walmart clt

  115. hypothesis testing

  116. remedial 1

  117. remedial ii

  118. advanced distribution

  119. walmart and yulu studies

  120. qq plots

  121. hypothesis testing i

  122. hypothesis testing ii

  123. hypothesis testing iii

  124. anova

  125. correlation tests

  126. feature engineering i

  127. feature engineering ii

  128. intro to sql

  129. yulu and delhivery case study

  130. basic sql

  131. filtering and subqueries

  132. sql group by and aggregation

  133. sql windows functions

  134. date and time functions

  135. sql advanced constructs

  136. Business Case_ Delhivery Review, Target SQL Intro

  137. DSML Adv _ Web API and Data Scraping .mp4

  138. DSML Adv _ Web API and Data Scraping Contd.

  139. DSML Adv _ Managing Data Sources and Visualizations

  140. DSML Adv _ Basic Visual Analytics

  141. DSML Adv _ Calculations in Tableau

  142. DSML Adv _ Linear Algebra 1 - The ML context

  143. DSML Adv_ Linear Algebra 2 - Dot products and hyperplanes

  144. DSML Adv_ Linear Algebra 3 - Halfspaces and distance

  145. Linear Algebra 4 - Loss minimization in classification

  146. Linear Algebra 5 - Problem solving

  147. ML_ Intro to (Classical) Machine Learning

  148. ML_ Cars24 + Linear Regression-1

  149. ML_ Linear Regression-2

  150. ML_ Linear Regression-3

  151. ML_ Bias-Variance, Regularisation, Cross-Validation

  152. MLIntro-LinReg-[1,2,3] Assessments Doubts

  153. ML_ Logistic Regression-1

  154. ML_ Logistic Regression-2

  155. ML_ Classification Metrics

  156. ML_ Multiclass Classificaton + kNN

  157. ML_ kNN cont. + Imbalanced Data

  158. ML_ Decision Trees-1

  159. Business Case_ Jamboree Review, LoanTap Intro

  160. ML_ Decision Trees-2

  161. ML_ Bagging and RF

  162. ML_ Boosting - GBDT

  163. ML_ XGBoost + Cascading

  164. ML _ Boosting continued

  165. Business Case _ Loantap Review, Ola Intro

  166. ML_ Naive Bayes

  167. ML_ MLE, MAP & Bayesian Inference __ SVM -1

  168. ML_ SVM

Added videos#

  1. Business Case _ Yulu Review, Delhivery Intro

  2. Introduction to excel and formulas

  3. Pivot table, charts & statistical function

  4. Macro and automation in Google sheets

  5. Dashboarding in Excel

  6. Tableau refresher 1

  7. Tableau refresher 2

  8. Advanced visual analytics

  9. Advanced Visaul Analytics Cont And Advanced charts

Price#

  • Cost: : β‚Ή1,50,000

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