Machine Learning
$69.00
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Machine Learning Introduction
- ML01_01_Machine Learning Introduction and Defination
- Ml02_01_ETP_Defimation
- ML03_01_Applications of ML
- ML04_01_Types of Machine Learning and Supervised Learning Introduction
- ML05_01_UnSupervised Learning Introduction
- ML06_01_reading _sklearn_ml_package_help_document part 1
- ML07_01_reading _sklearn_ml_package_help_document part 2
- ML08_01_Test Your Understanding
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Explore Toy-Datasets
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k nearest neighbor algorithm Maths
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KNN Estimator from Scratch
- ML20_04_KNN Estimator from Scratch
- ML21_04_Write code to Locate the most similar neighbors
- ML22_04_Write code to Make a classification prediction with neighbors
- ML23_04_High level End to End ML project Steps
- ML24_04_Load csv file and Understand X and y Data
- ML25_04_Split Data for training and testing
- ML26_04_Train or fit the model
- ML27_04_Predict labels of test data
- ML28_04_Accuracy_of_the_Clasification_model
- ML29_04_Hyper_Parameter_tunning
- ML30_04_k means cross validation
- ML31_04_GridSearchCV Hyper Parameter Tunning
- ML32_04_RandomizedSearchCV Hyper Parameter Tunning
- ML33_04_Save The model
- ML34_04_Load The model
- ML35_04_Home_Work
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Linear Regression Maths
- ML36_05_Linear Regression Maths
- ML37_05_Find weight of the baby based on age data understanding
- ML38_05_Ordinary Least Squares
- ML39_05_Find parameters using Ordinary Least Squares Function
- ML40_05_Find parameters using sklearn
- ML41_05_Find parameters using Scipy
- ML42_05_Find parameters using covar and var
- ML43_05_Multivariate Linear Regression
- ML44_05_Linear_regression_to find life span based on number of fertilities part 1
- ML45_05_Linear_regression_to find life span based on number of fertilities part 2
- ML46_05_Supervised_Regression_Metric_R2_score
- ML47_05_Supervised_Regression_Metrics_RMSE
- ML48_05_Life Span Predication
- ML49_05_Linear Regression with Cross Validation or K-Fold
- ML50_05_Linear Regression with Boston dataset
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Logistic Regression
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Support Vector Machines
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Pre-processing of machine learning
- ML_62_08_Pre-processing of machine learning data Outliers
- ML_63_08_Pre-processing of machine learning data Delete Outliers
- ML_64_08_Pre-processing Categorical Features
- ML_65_08_Pre-processing Categorical Features part2
- ML_66_08_Regression with categorical features using ridge algorithm
- ML_67_08_Dropping Missing Data
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ML_Pipeline with feature_selection and SVC
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Trees Entropy and Gini Maths Introduction
- ML_70_10_Trees Entropy and Gini Maths Introduction
- ML_71_10_Entropy Calculation using weather dataset
- ML_72_10_Entropy Calculation using weather dataset part 2
- ML_73_10_Entropy Calculation using weather dataset part 3
- ML_74_10_Entropy Calculation using weather dataset part 4
- ML_75_10_Decision_Tree
- ML_76_10_Predict_Tumor_is_cancer_not_a_cancer
- ML_77_10_Tree_Graph_Visulization
- ML_78_10_Logistic_regression_and_Decision_tree_accuracy_compare
- ML_79_10_Gini_Vs_Entropy
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BIAS_Variance_Tradeoff_part
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Ensemble_Learning_Introduction
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Bagging_Classifier_Introduction
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Random_Forests_Introduction
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AdaBoost_Introduction
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ML_102_16_UnSupervised_Learning_Introduction
