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Machine Learning and Deep Learning Bootcamp Series

Machine Learning and Deep Learning Bootcamp Series

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Simulate Machine Learning Classification Data | Data Simulation Technique using Python

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Simulate Machine Learning Classification Data | Data Simulation Technique using Python

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Machine Learning and Deep Learning Bootcamp Series

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  1. 1 Free Data Science Course Online - OSEMN Framework | Introduction
  2. 2 Free Data Science Course Online - OSEMN Framework | Requirement Gathering in Data Science
  3. 3 Data Science Awesome Framework (Data Extraction Primer) - Step 2 | Data Extraction in ML
  4. 4 MongoDB in 25 minutes (Machine Learning) | Extract Data from NoSQL DB MongoDB for Machine Learning
  5. 5 Hands-on with Web Scraping using Python and Beautifulsoup | Data Extraction for Machine Learning
  6. 6 How to Extract Data using API | What is an API and How exactly it works | Python Code Part 1
  7. 7 How to Extract Data using API | What is an API and How exactly it works | Python Code Part 2
  8. 8 Load or Extract Data from MySQL DB or CSV file for ML | Extract Data using Sklearn Python for ML
  9. 9 Simulate Machine Learning Classification Data | Data Simulation Technique using Python
  10. 10 Simulate Machine Learning Regression & Clustering Data | Data Simulation Technique using Python
  11. 11 Scrubbing or Cleaning Bad data in Data Science (Python) - Step 3 | Data Munging in Machine Learning
  12. 12 3.Dataset Missing Values & Imputation (Detailed Python Tutorial) | Impute Missing values in ML
  13. 13 4.One Hot Encoding to process Categorical variables (Python) | Process Categorical Features
  14. 14 5.Split data into Training and Test set in Data Science (Python) | Train Test Split function in ML
  15. 15 6.Feature Scaling in Machine Learning(Normalization & Standardization) | Feature Scaling Sklearn
  16. 16 7.Outlier Detection and Treatment using Python - Part 1 | How to Detect outliers in Machine Learning
  17. 17 8.Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning
  18. 18 9.Outlier Detection and Treatment using Python - Part 3 | How to Detect outliers in Machine Learning
  19. 19 Log Transformation for Outliers | Convert Skewed data to Normal Distribution
  20. 20 Outlier Treatment through Square Root Transformation | Convert Skewed data to Normal Distribution
  21. 21 Python Pandas Tutorial Series: Using Map, Apply and Applymap
  22. 22 Use Regular Expression to split string into Dataframe columns (Pandas)
  23. 23 Python Pandas Tutorial - Adding & Dropping columns (Machine Learning)
  24. 24 Python Pandas Tutorial - Merge Dataframes (Machine Learning)
  25. 25 Python Exploratory Data Analysis (OSEMN Framework) - Step 4
  26. 26 Univariate Analysis for Categorical Variables using Python
  27. 27 Univariate Analysis for Numerical Variables (Exploratory Data Analysis) Intuition
  28. 28 Convert Numerical variable to Categorical Intuition | BINNING
  29. 29 BINNING | Convert Numerical variable to Categorical using Python
  30. 30 How to do Bivariate Analysis of Numerical Numerical Variables
  31. 31 Chi Square Test | How to do Bivariate Analysis of Categorical Categorical Variables
  32. 32 z-test & t-test | Bivariate Analysis for Numerical-Categorical Variables
  33. 33 Bivariate Analysis for Numerical-Categorical Variables|ANOVA|Data Science EDA
  34. 34 Don't know the Difference among AI, ML and Deep Learning ?
  35. 35 Simple Linear Regression using Scikit Learn & Spark MLLib | Introduction & Intuition
  36. 36 Simple Linear Regression using Scikit Learn & Spark MLLib | Data Pre-processing | Code Part 1
  37. 37 Simple Linear Regression using Scikit Learn & Spark MLLib | Building Model | Code Part 2
  38. 38 Simple Linear Regression using Scikit Learn & Spark MLLib | Graphical Comparison | Code Part 3
  39. 39 Simple Linear Regression | Scikit Learn & Spark MLLib | Model Evaluation Techniques - Part 1
  40. 40 Simple Linear Regression | MSE RMSE & MAE | Model Evaluation Techniques - Part 2
  41. 41 Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 1 | Install Spark on Jupyter Notebook
  42. 42 Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 2 | Install Spark on Jupyter Notebook
  43. 43 Run PySpark on Google Colab for FREE! | PySpark on Jupyter
  44. 44 Simple Linear Regression using Spark MLLib | Introduction
  45. 45 Simple Linear Regression using Spark MLLib | Data Preprocessing
  46. 46 Simple Linear Regression using Spark MLLib | Build Train & Evaluate Model
  47. 47 Multiple Linear Regression using Scikit Learn | Introduction & Intuition
  48. 48 Multiple Linear Regression using Scikit Learn | Coding Part 1
  49. 49 Multiple Linear Regression using Scikit Learn | Coding Part 2
  50. 50 Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 1
  51. 51 Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 2
  52. 52 Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 3
  53. 53 Polynomial Regression using Python | Polynomial Regression Machine Learning
  54. 54 Polynomial Regression Machine Learning Python Code
  55. 55 Decision Tree Regression Introduction and Intuition
  56. 56 Complete End to End Python code for Decision Tree Regression
  57. 57 Random Forest Regression Introduction and Intuition
  58. 58 Complete End to End Python code for Random Forest Regression
  59. 59 Fantastic Explanation of Logistic Regression in Machine Learning - Part 1
  60. 60 Fantastic Explanation of Logistic Regression Model - Part 2
  61. 61 How to Develop and Train Logistic Regression model on Titanic Dataset | Python Code Part 1
  62. 62 How to Develop and Train Logistic Regression model on Titanic Dataset | Python Code Part 2
  63. 63 Best Explanation of Confusion Matrix False Positive False Negative so far
  64. 64 Precision Recall and F1-Score Explanation in Easy way
  65. 65 How to use CAP curve for Classification Model Evaluation? | What is CAP Curve?
  66. 66 Best Explanation of Evaluating Classification Model using AUC-ROC Curve
  67. 67 How to Generate AUC-ROC curve for Evaluating Logistic Regression Model?
  68. 68 Fantastic Explanation of Support Vector Machine algorithm | Support Vector Machine Intuition
  69. 69 Build Train and Evaluate Support Vector Machine Model | Train & Evaluate SVM model using python
  70. 70 Fantastic Explanation of K-Nearest Neighbor | K-Nearest Neighbor Introduction and Intuition
  71. 71 Build Train & Evaluate K-Nearest Neighbor (KNN) Model | Using Python & Scikit Learn
  72. 72 Decision Tree Classification Introduction and Intuition
  73. 73 What are the 4 Key Steps to Create a Decision Tree? | How to Create Decision tree?
  74. 74 How to Measure the Purity of Decision Tree split using GINI INDEX | How to calculate Gini Index?
  75. 75 How to Measure the Purity of Decision Tree split using Information Gain
  76. 76 Build, Train & Evaluation Decision Tree Model | Decision Tree using Scikit Learn
  77. 77 How to Train Random Forest Classifier using Python? | How does Random Forest work?
  78. 78 How to AUTOMATE Data Science Lifecycle | How to AUTOMATE Machine Learning Pipeline
  79. 79 डेटा Science Lifecycle को स्वचालित कैसे करें | How to AUTOMATE Data Science Lifecycle in Hindi

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