[FreeCourseSite.com] Udemy - Machine Learning Essentials (2023) - Master core ML concepts

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[FreeCourseSite.com] Udemy - Machine Learning Essentials (2023) - Master core ML concepts

磁力链接/BT种子简介

种子哈希：dff3f9fa09449dc2c837c358f8debb0414345afb
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收录时间：2024-03-16
最近下载：2024-11-06

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文件列表

  • 5. Logistic Regression/3. Hypothesis Function.mp4 285.5 MB
  • 3. Linear Regression/7. Gradient Descent Code.mp4 284.5 MB
  • 19. Ensemble Learning Boosting/5. GBDT Algorithm.mp4 257.1 MB
  • 4. Linear Regression - Multiple Features/8. Code 04 - Gradient Computation.mp4 233.1 MB
  • 12. Naive Bayes Algorithm/7. Understanding Golf Dataset.mp4 229.4 MB
  • 19. Ensemble Learning Boosting/3. Boosting Mathematical Formulation.mp4 221.8 MB
  • 13. Multinomial Naive Bayes/4. Bernoulli Naive Bayes.mp4 214.7 MB
  • 15. Decision Trees/5. Information Gain.mp4 209.2 MB
  • 2. Supervised vs Unsupervised Learning/2. Supervised Learning Example.mp4 207.7 MB
  • 9. PROJECT - Face Recognition/7. Face Recognition 01 - Data Collection.mp4 207.6 MB
  • 3. Linear Regression/4. Loss Error Function.mp4 204.9 MB
  • 12. Naive Bayes Algorithm/6. Computing Likelihood.mp4 202.5 MB
  • 13. Multinomial Naive Bayes/3. Multinomial Naive Bayes Example.mp4 187.9 MB
  • 7. Principal Component Analysis (PCA)/3. Maximising Variance.mp4 186.6 MB
  • 3. Linear Regression/2. Notation.mp4 179.7 MB
  • 3. Linear Regression/11. Code 02 - Data Normalisation.mp4 179.2 MB
  • 12. Naive Bayes Algorithm/10. CODE - Likelihood.mp4 174.6 MB
  • 12. Naive Bayes Algorithm/5. Naive Bayes for Text Classification.mp4 168.5 MB
  • 14. PROJECT Spam Classifier/2. Data Clearning.mp4 165.6 MB
  • 19. Ensemble Learning Boosting/4. Concept of Pseudo Residuals.mp4 160.2 MB
  • 5. Logistic Regression/5. Gradient Update Rule.mp4 153.7 MB
  • 12. Naive Bayes Algorithm/3. Bayes Theorem Question.mp4 152.0 MB
  • 18. Ensemble Learning Bagging/3. Why Bagging Helps.mp4 149.6 MB
  • 13. Multinomial Naive Bayes/1. Multinomial Naive Bayes.mp4 148.0 MB
  • 7. Principal Component Analysis (PCA)/2. Conceptual Overview of PCA.mp4 147.7 MB
  • 3. Linear Regression/15. R2 Score.mp4 146.1 MB
  • 13. Multinomial Naive Bayes/5. Bernoulli Naive Bayes Example.mp4 145.0 MB
  • 15. Decision Trees/2. Decision Trees Example.mp4 144.0 MB
  • 15. Decision Trees/6. CODE Split Data.mp4 142.3 MB
  • 19. Ensemble Learning Boosting/2. Boosting Intuition.mp4 140.0 MB
  • 19. Ensemble Learning Boosting/7. CODE - Gradient Boosting Decision Trees.mp4 138.0 MB
  • 18. Ensemble Learning Bagging/2. Bagging Model.mp4 135.1 MB
  • 18. Ensemble Learning Bagging/5. Bias Variance Tradeoff.mp4 133.6 MB
  • 20. PROJECT Customer Churn Prediction/1. Project Overview.mp4 128.3 MB
  • 19. Ensemble Learning Boosting/1. Boosting Introduction.mp4 126.2 MB
  • 16. Decision Trees Implementation/2. CODE - Train Decision Tree.mp4 125.6 MB
  • 19. Ensemble Learning Boosting/8. XGBoost.mp4 125.1 MB
  • 19. Ensemble Learning Boosting/9. Adaptive Boosting (AdaBoost).mp4 124.6 MB
  • 15. Decision Trees/3. Entropy.mp4 124.2 MB
  • 3. Linear Regression/13. Code 04 - Modelling.mp4 123.8 MB
  • 18. Ensemble Learning Bagging/4. Random Forest Algorithm.mp4 123.8 MB
  • 16. Decision Trees Implementation/7. CODE - Prediction.mp4 122.0 MB
  • 18. Ensemble Learning Bagging/6. CODE Random Forest.mp4 121.2 MB
  • 12. Naive Bayes Algorithm/12. Implementing Naive Bayes - Sklearn.mp4 116.9 MB
  • 3. Linear Regression/6. Gradient Descent Optimisation.mp4 115.7 MB
  • 16. Decision Trees Implementation/8. Handling Numeric Features.mp4 115.3 MB
  • 13. Multinomial Naive Bayes/7. Gaussian Naive Bayes.mp4 114.7 MB
  • 12. Naive Bayes Algorithm/9. CODE - Conditional Probability.mp4 113.3 MB
  • 14. PROJECT Spam Classifier/3. WordCloud.mp4 111.4 MB
  • 5. Logistic Regression/2. Notation.mp4 110.4 MB
  • 3. Linear Regression/9. The Math of Training.mp4 110.4 MB
  • 4. Linear Regression - Multiple Features/5. Code 01 - Data Prep.mp4 109.3 MB
  • 3. Linear Regression/17. Code 07 - Visualisation.mp4 108.5 MB
  • 20. PROJECT Customer Churn Prediction/2. Exploratory Data Analysis.mp4 108.3 MB
  • 16. Decision Trees Implementation/6. CODE - Explore Decision Tree Model.mp4 107.3 MB
  • 20. PROJECT Customer Churn Prediction/7. Hyperparameter tuning.mp4 106.1 MB
  • 17. PROJECT Titanic Survival Prediction/1. Project Overview.mp4 105.7 MB
  • 1. Introduction/7. Automatic Speech Recognition.mp4 105.6 MB
  • 9. PROJECT - Face Recognition/9. Face Recognition 03 - Predictions using KNN.mp4 104.5 MB
  • 15. Decision Trees/9. Stopping Conditions.mp4 103.0 MB
  • 7. Principal Component Analysis (PCA)/4. Minimising Distances.mp4 99.9 MB
  • 3. Linear Regression/3. Hypothesis.mp4 99.7 MB
  • 17. PROJECT Titanic Survival Prediction/5. Handling Missing Values.mp4 99.4 MB
  • 13. Multinomial Naive Bayes/6. Bias Variance Tradeoff.mp4 99.0 MB
  • 2. Supervised vs Unsupervised Learning/3. Unsupervised Learning.mp4 98.5 MB
  • 3. Linear Regression/18. Code 08 - Trajectory [Optional].mp4 98.5 MB
  • 13. Multinomial Naive Bayes/8. CODE - Variants of Naive Bayes.mp4 98.5 MB
  • 15. Decision Trees/7. CODE Information Gain.mp4 98.3 MB
  • 17. PROJECT Titanic Survival Prediction/7. Visualize Decision Tree.mp4 97.1 MB
  • 13. Multinomial Naive Bayes/2. Laplace Smoothing.mp4 96.0 MB
  • 5. Logistic Regression/4. Binary Cross-Entropy Loss Function.mp4 95.2 MB
  • 8. K-Nearest Neigbours/4. KNN Algorithm Code.mp4 95.2 MB
  • 16. Decision Trees Implementation/10. Decision Trees for Regression.mp4 93.8 MB
  • 3. Linear Regression/12. Code 03 - Train Test Split.mp4 93.6 MB
  • 21. Deep Learning Introduction - Neural Network/8. Tensorflow Playground.mp4 93.0 MB
  • 4. Linear Regression - Multiple Features/1. Introduction.mp4 92.5 MB
  • 14. PROJECT Spam Classifier/1. Project Overview.mp4 91.7 MB
  • 12. Naive Bayes Algorithm/1. Bayes Theorem.mp4 91.5 MB
  • 4. Linear Regression - Multiple Features/9. Code 05 - Training Loop.mp4 91.0 MB
  • 5. Logistic Regression/1. Binary Classification Introduction.mp4 89.6 MB
  • 21. Deep Learning Introduction - Neural Network/11. CODE - Model Training and Testing.mp4 89.1 MB
  • 17. PROJECT Titanic Survival Prediction/2. Exploratory Data Analysis.mp4 87.9 MB
  • 16. Decision Trees Implementation/5. CODE - Train Child Nodes.mp4 87.4 MB
  • 19. Ensemble Learning Boosting/6. Bias Variance Tradeoff.mp4 87.4 MB
  • 17. PROJECT Titanic Survival Prediction/4. Data Preparation for ML Model.mp4 87.4 MB
  • 10. K-Means/6. Code 05 - Visualizing K-Means & Results.mp4 85.7 MB
  • 12. Naive Bayes Algorithm/4. Naive Bayes Algorithm.mp4 84.7 MB
  • 5. Logistic Regression/6. Code 01 - Data Prep.mp4 83.7 MB
  • 9. PROJECT - Face Recognition/3. Object Detection using Haarcascades.mp4 83.5 MB
  • 17. PROJECT Titanic Survival Prediction/3. Exploratory Data Analysis - II.mp4 82.9 MB
  • 9. PROJECT - Face Recognition/4. Face Detection in Images.mp4 82.5 MB
  • 4. Linear Regression - Multiple Features/6. Code 02 - Hypothesis.mp4 82.3 MB
  • 2. Supervised vs Unsupervised Learning/1. Supervised Learning Introduction.mp4 82.1 MB
  • 15. Decision Trees/1. Decision Trees Introduction.mp4 81.8 MB
  • 17. PROJECT Titanic Survival Prediction/6. Decision Tree Model Building.mp4 81.6 MB
  • 10. K-Means/4. Code 03 - Assigning Points.mp4 79.3 MB
  • 12. Naive Bayes Algorithm/2. Derivation of Bayes Theorem.mp4 78.5 MB
  • 20. PROJECT Customer Churn Prediction/6. Model Building.mp4 78.3 MB
  • 5. Logistic Regression/14. Multiclass Classification One Vs Rest.mp4 75.9 MB
  • 16. Decision Trees Implementation/4. CODE - Stopping Conditions.mp4 75.9 MB
  • 9. PROJECT - Face Recognition/8. Face Recognition 02 - Loading Data.mp4 75.2 MB
  • 12. Naive Bayes Algorithm/11. CODE - Prediction.mp4 74.9 MB
  • 11. Project - Dominant Color Extraction/5. Image in K-Colors.mp4 74.5 MB
  • 15. Decision Trees/4. CODE Entropy.mp4 73.5 MB
  • 18. Ensemble Learning Bagging/1. Ensemble Learning.mp4 72.7 MB
  • 3. Linear Regression/10. Code 01 - Data Generation.mp4 71.5 MB
  • 14. PROJECT Spam Classifier/6. Model Evaluation.mp4 71.2 MB
  • 20. PROJECT Customer Churn Prediction/4. Finding relations.mp4 70.7 MB
  • 1. Introduction/3. Machine Learning.mp4 70.2 MB
  • 15. Decision Trees/8. Construction of Decision Trees.mp4 69.6 MB
  • 10. K-Means/3. Code 02 - Init Centers.mp4 68.9 MB
  • 1. Introduction/6. Natural Language Processing.mp4 67.6 MB
  • 6. Dimensionality Reduction Feature Selection/6. Feature Selection - Code.mp4 66.7 MB
  • 7. Principal Component Analysis (PCA)/1. Introduction to PCA.mp4 66.4 MB
  • 5. Logistic Regression/10. Code 05 - Training Loop.mp4 64.6 MB
  • 20. PROJECT Customer Churn Prediction/5. Data Preparation.mp4 64.3 MB
  • 16. Decision Trees Implementation/1. CODE - Decision Tree Node.mp4 64.1 MB
  • 12. Naive Bayes Algorithm/8. CODE - Prior Probability.mp4 64.1 MB
  • 10. K-Means/1. K-Means Algorithm.mp4 63.1 MB
  • 16. Decision Trees Implementation/3. CODE - Assign Target Variable to Each Node.mp4 62.8 MB
  • 10. K-Means/5. Code 04 - Updating Centroids.mp4 61.9 MB
  • 16. Decision Trees Implementation/9. Bias Variance Tradeoff.mp4 61.8 MB
  • 21. Deep Learning Introduction - Neural Network/5. Neural Networks.mp4 60.8 MB
  • 5. Logistic Regression/12. Code 07 - Predictions & Accuracy.mp4 58.2 MB
  • 1. Introduction/4. Deep Learning.mp4 57.1 MB
  • 3. Linear Regression/14. Code 05 - Predictions.mp4 56.7 MB
  • 11. Project - Dominant Color Extraction/3. Finding Clusters.mp4 56.5 MB
  • 8. K-Nearest Neigbours/8. KNN Pros and Cons.mp4 56.4 MB
  • 21. Deep Learning Introduction - Neural Network/4. Gradient Descent Updates.mp4 55.3 MB
  • 20. PROJECT Customer Churn Prediction/3. Data Visualisation.mp4 55.1 MB
  • 14. PROJECT Spam Classifier/5. Model Building.mp4 54.6 MB
  • 3. Linear Regression/8. Gradient Descent - for Linear Regression.mp4 54.3 MB
  • 4. Linear Regression - Multiple Features/11. Code 06 - Evaluation.mp4 53.4 MB
  • 7. Principal Component Analysis (PCA)/8. PCA Code.mp4 53.0 MB
  • 22. PROJECT Pokemon Image Classification/5. Data Preprocessing.mp4 52.7 MB
  • 22. PROJECT Pokemon Image Classification/9. Model evaluation.mp4 52.7 MB
  • 21. Deep Learning Introduction - Neural Network/7. Why Neural Nets.mp4 52.3 MB
  • 1. Introduction/1. Course Overview.mp4 52.0 MB
  • 9. PROJECT - Face Recognition/5. Face Detection in Live Video.mp4 51.7 MB
  • 22. PROJECT Pokemon Image Classification/2. The Data.mp4 51.0 MB
  • 1. Introduction/2. Artificial Intelligence.mp4 51.0 MB
  • 7. Principal Component Analysis (PCA)/5. Eigen Values & Eigen Vectors.mp4 50.8 MB
  • 3. Linear Regression/5. Training Idea.mp4 50.7 MB
  • 21. Deep Learning Introduction - Neural Network/10. CODE - Model Building.mp4 48.0 MB
  • 7. Principal Component Analysis (PCA)/9. Choosing the right dimensions.mp4 47.6 MB
  • 5. Logistic Regression/9. Code 04 - Gradient Computation.mp4 47.4 MB
  • 8. K-Nearest Neigbours/1. Introduction.mp4 47.2 MB
  • 7. Principal Component Analysis (PCA)/7. Understanding Eigen Values.mp4 46.8 MB
  • 14. PROJECT Spam Classifier/4. Text Featurization.mp4 46.3 MB
  • 1. Introduction/8. Reinforcement Learning.mp4 46.0 MB
  • 21. Deep Learning Introduction - Neural Network/9. CODE -Data Preparation.mp4 45.9 MB
  • 4. Linear Regression - Multiple Features/4. Training & Gradient Updates.mp4 45.4 MB
  • 5. Logistic Regression/11. Code 06 - Visualise Decision Boundary.mp4 45.2 MB
  • 1. Introduction/5. Computer Vision.mp4 45.2 MB
  • 22. PROJECT Pokemon Image Classification/4. Data Loading.mp4 44.8 MB
  • 21. Deep Learning Introduction - Neural Network/3. How does a perceptron Learns.mp4 44.8 MB
  • 11. Project - Dominant Color Extraction/4. Dominant Color Swatches.mp4 41.7 MB
  • 16. Decision Trees Implementation/11. Decision Tree Code - Sklearn.mp4 38.5 MB
  • 22. PROJECT Pokemon Image Classification/1. Introduction.mp4 37.5 MB
  • 4. Linear Regression - Multiple Features/12. Linear Regression using Sk-Learn.mp4 37.2 MB
  • 8. K-Nearest Neigbours/2. KNN Idea.mp4 36.2 MB
  • 9. PROJECT - Face Recognition/2. OpenCV - Video Input from WebCam.mp4 35.9 MB
  • 5. Logistic Regression/7. Code 02 - Hypothesis Logit Model.mp4 35.8 MB
  • 21. Deep Learning Introduction - Neural Network/2. A Neuron.mp4 35.8 MB
  • 9. PROJECT - Face Recognition/1. OpenCV - Working with Images.mp4 35.6 MB
  • 5. Logistic Regression/15. Multiclass Classification One Vs One.mp4 35.1 MB
  • 22. PROJECT Pokemon Image Classification/6. Model Architecture.mp4 34.9 MB
  • 4. Linear Regression - Multiple Features/3. Loss Function.mp4 34.8 MB
  • 22. PROJECT Pokemon Image Classification/3. Structured Data.mp4 33.4 MB
  • 22. PROJECT Pokemon Image Classification/10. Predictions.mp4 31.7 MB
  • 4. Linear Regression - Multiple Features/10. A Note about Shapes.mp4 31.6 MB
  • 5. Logistic Regression/13. Logistic Regression using Sk-Learn.mp4 30.9 MB
  • 8. K-Nearest Neigbours/3. KNN Data Prep.mp4 30.6 MB
  • 3. Linear Regression/16. Code 06 - Evaluation.mp4 30.2 MB
  • 4. Linear Regression - Multiple Features/2. Hypothesis.mp4 30.2 MB
  • 21. Deep Learning Introduction - Neural Network/1. Biological Neural Network.mp4 29.8 MB
  • 21. Deep Learning Introduction - Neural Network/6. 3 Layer NN.mp4 29.4 MB
  • 3. Linear Regression/1. Introduction to Linear Regression.mp4 27.9 MB
  • 11. Project - Dominant Color Extraction/1. Introduction.mp4 26.4 MB
  • 11. Project - Dominant Color Extraction/2. Reading Images.mp4 25.3 MB
  • 6. Dimensionality Reduction Feature Selection/3. Filter Method.mp4 24.6 MB
  • 6. Dimensionality Reduction Feature Selection/4. Wrapper Method.mp4 24.1 MB
  • 4. Linear Regression - Multiple Features/7. Code 03 - Loss Function.mp4 23.6 MB
  • 5. Logistic Regression/8. Code 03 - Binary Cross Entropy Loss.mp4 20.4 MB
  • 10. K-Means/2. Code 01 - Data Prep.mp4 19.5 MB
  • 22. PROJECT Pokemon Image Classification/7. Softmax Function.mp4 19.3 MB
  • 7. Principal Component Analysis (PCA)/6. PCA Summary.mp4 19.2 MB
  • 22. PROJECT Pokemon Image Classification/8. Model Training.mp4 18.2 MB
  • 6. Dimensionality Reduction Feature Selection/1. Curse of Dimensionality.mp4 17.8 MB
  • 8. K-Nearest Neigbours/7. KNN and Data Standardisation.mp4 16.0 MB
  • 9. PROJECT - Face Recognition/6. Face Recognition Project Intro.mp4 15.9 MB
  • 6. Dimensionality Reduction Feature Selection/2. Feature Selection Vs. Feature Extraction.mp4 15.8 MB
  • 8. K-Nearest Neigbours/5. Euclidean and Manhattan Distance.mp4 15.6 MB
  • 6. Dimensionality Reduction Feature Selection/5. Embedded Method.mp4 13.4 MB
  • 8. K-Nearest Neigbours/6. Deciding value of K.mp4 7.1 MB
  • 6. Dimensionality Reduction Feature Selection/6.1 train.csv 122.4 kB
  • 17. PROJECT Titanic Survival Prediction/1.1 titanic_train.csv 60.3 kB
  • 1. Introduction/9. Pre-requisites.html 889 Bytes
  • 12. Naive Bayes Algorithm/7.1 golf.csv 430 Bytes
  • 8. K-Nearest Neigbours/9. KNN using Sk-Learn.html 405 Bytes
  • 1. Introduction/10. Code Repository.html 236 Bytes
  • 22. PROJECT Pokemon Image Classification/1.1 Dataset Link.html 129 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 10. K-Means/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 15. Decision Trees/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 4. Linear Regression - Multiple Features/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 10. K-Means/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 15. Decision Trees/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 4. Linear Regression - Multiple Features/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 10. K-Means/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 15. Decision Trees/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 4. Linear Regression - Multiple Features/0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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