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Coders Camp

Machine Learning Algorithms & Models Explained with Python

All Machine Learning Algorithms and Models Explained with Python programming language.

All Machine Learning Algorithms & Models with Python

  1. ARIMA for Time Series Forecasting
  2. Understand How Neural Network Works
  3. Assumptions of Machine Learning Algorithms
  4. Multiclass Classification Algorithms
  5. Binary Classification Algorithms
  6. Most Important Python Libraries for Data Science
  7. Best Approaches for Time Series Analysis
  8. Best Approaches for Sentiment Analysis
  9. Giving Inputs to a Machine Learning Model
  10. Adding Labels to a Dataset for Sentiment Analysis
  11. Process of Natural Language Processing
  12. Sentence and Word Tokenization
  13. Clustering Algorithms
  14. AlexNet Architecture
  15. Activation Functions
  16. LeNet-5 Architecture
  17. Visualizing a Machine Learning Algorithm
  18. Introduction and Approaches to build Recommendation Systems
  19. Mean-Shift Clustering
  20. Performance Evaluation Metrics
  21. Part of Speech Tagging
  22. Mini-Batch K-Means Clustering
  23. Multinomial Naive Bayes
  24. Bernoulli Naive Bayes
  25. Agglomerative Clustering
  26. VisualKeras for Visualizing a Neural Network
  27. Stochastic Gradient Descent
  28. Explained Variance
  29. F-Beta Score
  30. Classification Report
  31. Passive Aggressive Regression
  32. R2 Score
  33. Lazy Predict
  34. FLAML
  35. Missing Values Calculation
  36. t-SNE Algorithm
  37. AutoKeras Tutorial
  38. Bias and Variance
  39. Perceptron
  40. Class Balancing Techniques
  41. One vs All & One vs One
  42. Polynomial Regression
  43. BIRCH Clustering
  44. Independent Component Analysis
  45. Kernel PCA
  46. Sparse PCA
  47. Non Negative Matrix Factorization
  48. Neural Networks Tutorial
  49. PyCaret
  50. Scikit-learn Tutorial
  51. NLTK Tutorial
  52. TextBlob Tutorial
  53. Streamlit Tutorial
  54. DBSCAN Clustering
  55. Naive Bayes
  56. Passive Aggressive Classifier
  57. Logistic Regression
  58. Linear Regression
  59. K-Means Clustering
  60. Dimensionality Reduction
  61. Principal Component Analysis
  62. Automatic EDA
  63. Feature Scaling
  64. Apriori Algorithm
  65. K Nearest Neighbor
  66. CatBoost
  67. SMOTE
  68. Content-Based Filtering
  69. Collaborative Filtering
  70. Cosine Similarity
  71. Tf-Idf Vectorization
  72. Cross-Validation
  73. Confusion Matrix
  74. Ridge and Lasso Regression
  75. StandardScaler
  76. SARIMA
  77. ARIMA
  78. Auc and ROC Curve
  79. XGBoost Algorithm
  80. Long Short Term Memory (LSTM)
  81. One Hot Encoding
  82. Bidirectional Encoder Representations from Transformers (BERT)
  83. Facebook Prophet
  84. NeuralProphet
  85. AdaBoost Algorithm
  86. Random Forest Algorithm
  87. H2O AutoML
  88. Polynomial Regression
  89. Gradient Descent Algorithm
  90. Grid Search Algorithm
  91. Manifold Learning
  92. Decision Trees
  93. Support Vector Machines
  94. Neural Networks
  95. FastAI
  96. LightGBM
  97. Pyforest Tutorial
  98. Machine Learning Models You Should Know

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