Machine Learning

What is machine learning

Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Here on our website, you’ll get to read various posts created by a team of experts.

Here you go
  1. Soft Margin Classification | Machine Learning from Scratch
  2. 100 Statistics Interview Questions
  3. ROC and AUC in Evaluating Classification Models
  4. Support Vector Machines (SVM) Algorithms
  5. What is early stopping? | Machine Learning from Scratch
  6. Information Gain in Machine Learning
  7. Extracting Financial Year from Date in Pandas and PySpark DataFrames
  8. What is Lasso Regression? | Machine Learning from Scratch
  9. Regularized Linear Models(Ridge Regression) | Machine Learning from Scratch
  10. Understanding and Implementing Batch Gradient Descent for Linear Regression in Python
  11. Learning Curves | Machine Learning from Scratch
  12. Polynomial Regression | Machine Learning from Scratch
  13. What is Mini-batch Gradient Descent? Machine Learning from Scratch
  14. What is Stochastic Gradient Descent? Machine Learning from Scratch
  15. What is Gradient Descent ? Machine Learning from scratch
  16. What is Linear Regression? Learn from Scratch
  17. A Detailed Introducton to Poisson Regression
  18. How does backpropagation works in case of sigmoid activation function?
  19. How data science can help Insurance sector?
  20. What is MultiLabel classification?
  21. How to analyze error in classification models in machine learning?
  22. What is Multiclass Classification?
  23. What is ROC Curve and how to Interpret it?
  24. Performance Measures(Precision and Recall) in Classification Models Part-3
  25. Performance Measures(Confusion Matrix) in Classification Models Part-2
  26. Performance Measures in Classification Models Part-1
  27. What is Classification in Machine Learning?
  28. Finding Stop words in Text: A Python Approach
  29. What are the top computer vision applications for AI in the next 5 years?
  30. How can you adapt web-scraped data to your natural language processing tools?
  31. The Significance of Gaussian Distributions in Machine Learning
  32. Questions asked in Data Scientist Interviews Part 1
  33. What do eigenvalues and eigenvectors mean in PCA?
  34. What is Skewness in Data?
  35. Machine learning interview questions on Regularization
  36. Machine Learning interview on optimizer (Gradient Descent)
  37. How Haversine distance is being used in machine learning?
  38. What are different types of Distance Metrics ?
  39. ML-Based Water Portability Prediction
  40. Performance Metrics for Classification and Regression Algorithms
  41. What is Data Imputation and it’s different techniques
  42. How to do Ensembling in machine learning?
  43. Hierarchical Clustering
  44. How to handle categorical data in machine learning
  45. Random Forest Algorithm
  46. Decision Trees
  47. Support Vector Machine
  48. Steps to Create a Tensorflow Model
  49. How to deal with outliers?
  50. What is data leakage in Machine Learning
  51. How to do Feature Encoding and Exploratory Data Analysis
  52. 8 Essential Machine Learning Terms You must Know
  53. What are Bias and Variance in Machine Learning
  54. Which one to use – RandomForest vs SVM vs KNN?
  55. Clustering & Visualization of Clusters using PCA
  56. What is the difference between artificial and convolutional neural networks?
  57. What is the VGG 19 neural network?
  58. What is clustering in machine learning?
  59. What is the root mean square error?
  60. What is Digital image processing in simple terms?
  61. Stationarity Analysis in Time Series Data
  62. How to create Movie Recommendation System
  63. How to Predict Movie will be Flop or Hit and it’s Revenue?
  64. How to create Movie Recommendation System
  65. Top 10 Machine Learning Tools You need to Know
  66. How to create a simple movie recommendation System
  67. Why machine learning became popular recently, if most theories and algorithms have existed for so long
  68. What is Dimensionality Reduction? Overview, Objectives, and Popular Techniques
  69. Interpreting ACF and PACF | Time Series
  70. Predictive Analysis with different approaches
  71. All Cheat Sheets related to Machine Learning
  72. Feature engineering and SGDReg with Regularization With Students Performance Data
  73. Analysis on campus recruitment data
  74. What is Overfitting?
  75. Outliers and Various methods of Detection
  76. Precision and Recall with Scikit Learn
  77. How to build Machine Learning Models
  78. What is Supervised Learning
  79. Web Scraping For COVID-19
  80. Gradient Descent Algorithm in Machine Learning
  81. Data Science Framework: To Achieve 99% Accuracy :Part 1
  82. A Data Science Framework: To Achieve 99% Accuracy :Part 2
  83. Data Science Framework: To Achieve 99% Accuracy :Part 3
  84. Introduction to Ensembling /Stacking in Python | Part 1
  85. Introduction to Ensembling /Stacking in Python | Part 2
  86. How to evaluate readability using Python?
  87. Introduction to CNN Keras – Acc 0.997 (top 8%)
  88. Forecasting Stock Prices Using Stocker
  89. How to convert Emojis to Text
  90. Outlier detection using Local Outlier Factor (LOF)
  91. 7 Must-Know Data Wrangling Operations with Python Pandas
  92. Heart Disease? Explaining the ML Model | Part 1
  93. Heart Disease? Explaining the ML Model | Part 2
  94. Marketing strategy EDA and Prediction with 97% Accuracy
  95. Deep Learning for Time Series Forecasting
  96. How to List Modules, Search Path, Loaded Modules in Python
  97. Stroke Prediction-EDA-Classification-Models Python
  98. Decision Statements in Python
  99. Exploratory Data Analysis(EDA) With Python
  100. Datasets Importing and exporting in Python.
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