Udemy - Machine Learning Essentials (2023) - Master core ML conce...

Files:

• [CourseClub.Me].url (0.1 KB)
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• 1. Course Overview.mp4 (49.6 MB)
• 10. Code Repository.html (0.2 KB)
• 2. Artificial Intelligence.mp4 (48.6 MB)
• 3. Machine Learning.mp4 (67.0 MB)
• 4. Deep Learning.mp4 (54.5 MB)
• 5. Computer Vision.mp4 (43.1 MB)
• 6. Natural Language Processing.mp4 (64.4 MB)
• 7. Automatic Speech Recognition.mp4 (100.7 MB)
• 8. Reinforcement Learning.mp4 (43.9 MB)
• 9. Pre-requisites.html (0.9 KB)

• [CourseClub.Me].url (0.1 KB)
• [FreeCourseSite.com].url (0.1 KB)
• [GigaCourse.Com].url (0.0 KB)

• 1. K-Means Algorithm.mp4 (60.1 MB)
• 2. Code 01 - Data Prep.mp4 (18.6 MB)
• 3. Code 02 - Init Centers.mp4 (65.7 MB)
• 4. Code 03 - Assigning Points.mp4 (75.6 MB)
• 5. Code 04 - Updating Centroids.mp4 (59.1 MB)
• 6. Code 05 - Visualizing K-Means & Results.mp4 (81.8 MB)
11. Project - Dominant Color Extraction
• 1. Introduction.mp4 (25.1 MB)
• 2. Reading Images.mp4 (24.2 MB)
• 3. Finding Clusters.mp4 (53.9 MB)
• 4. Dominant Color Swatches.mp4 (39.7 MB)
• 5. Image in K-Colors.mp4 (71.0 MB)
12. Naive Bayes Algorithm
• 1. Bayes Theorem.mp4 (87.3 MB)
• 10. CODE - Likelihood.mp4 (166.5 MB)
• 11. CODE - Prediction.mp4 (71.4 MB)
• 12. Implementing Naive Bayes - Sklearn.mp4 (111.5 MB)
• 2. Derivation of Bayes Theorem.mp4 (74.8 MB)
• 3. Bayes Theorem Question.mp4 (145.0 MB)
• 4. Naive Bayes Algorithm.mp4 (80.7 MB)
• 5. Naive Bayes for Text Classification.mp4 (160.7 MB)
• 6. Computing Likelihood.mp4 (193.2 MB)
• 7. Understanding Golf Dataset.mp4 (218.7 MB)
• 7.1 golf.csv (0.4 KB)
• 8. CODE - Prior Probability.mp4 (61.1 MB)
• 9. CODE - Conditional Probability.mp4 (108.1 MB)
13. Multinomial Naive Bayes
• 1. Multinomial Naive Bayes.mp4 (141.1 MB)
• 2. Laplace Smoothing.mp4 (91.5 MB)
• 3. Multinomial Naive Bayes Example.mp4 (179.2 MB)
• 4. Bernoulli Naive Bayes.mp4 (204.7 MB)
• 5. Bernoulli Naive Bayes Example.mp4 (138.3 MB)
• 6. Bias Variance Tradeoff.mp4 (94.4 MB)
• 7. Gaussian Naive Bayes.mp4 (109.3 MB)
• 8. CODE - Variants of Naive Bayes.mp4 (93.9 MB)
14. PROJECT Spam Classifier
• 1. Project Overview.mp4 (87.4 MB)
• 2. Data Clearning.mp4 (157.9 MB)
• 3. WordCloud.mp4 (106.2 MB)
• 4. Text Featurization.mp4 (44.2 MB)
• 5. Model Building.mp4 (52.1 MB)
• 6. Model Evaluation.mp4 (67.9 MB)
15. Decision Trees 0. Websites you may like
• [CourseClub.Me].url (0.1 KB)
• [FreeCourseSite.com].url (0.1 KB)
• [GigaCourse.Com].url (0.0 KB)
• 1. Decision Trees Introduction.mp4 (78.0 MB)
• 2. Decision Trees Example.mp4 (137.4 MB)
• 3. Entropy.mp4 (118.4 MB)
• 4. CODE Entropy.mp4 (70.1 MB)
• 5. Information Gain.mp4 (199.5 MB)
• 6. CODE Split Data.mp4 (135.8 MB)
• 7. CODE Information Gain.mp4 (93.8 MB)
• 8. Construction of Decision Trees.mp4 (66.4 MB)
• 9. Stopping Conditions.mp4 (98.3 MB)
16. Decision Trees Implementation
• 1. CODE - Decision Tree Node.mp4 (61.2 MB)
• 10. Decision Trees for Regression.mp4 (89.5 MB)
• 11. Decision Tree Code - Sklearn.mp4 (36.7 MB)
• 2. CODE - Train Decision Tree.mp4 (119.7 MB)
• 3. CODE - Assign Target Variable to Each Node.mp4 (59.9 MB)
• 4. CODE - Stopping Conditions.mp4 (72.4 MB)
• 5. CODE - Train Child Nodes.mp4 (83.4 MB)
• 6. CODE - Explore Decision Tree Model.mp4 (102.3 MB)
• 7. CODE - Prediction.mp4 (116.4 MB)
• 8. Handling Numeric Features.mp4 (110.0 MB)
• 9. Bias Variance Tradeoff.mp4 (58.9 MB)
17. PROJECT Titanic Survival Prediction
• 1. Project Overview.mp4 (100.8 MB)
• 1.1 titanic_train.csv (58.9 KB)
• 2. Exploratory Data Analysis.mp4 (83.8 MB)
• 3. Exploratory Data Analysis - II.mp4 (79.0 MB)
• 4. Data Preparation for ML Model.mp4 (83.4 MB)
• 5. Handling Missing Values.mp4 (94.8 MB)
• 6. Decision Tree Model Building.mp4 (77.8 MB)
• 7. Visualize Decision Tree.mp4 (92.6 MB)
18. Ensemble Learning Bagging
• 1. Ensemble Learning.mp4 (69.3 MB)
• 2. Bagging Model.mp4 (128.8 MB)
• 3. Why Bagging Helps.mp4 (142.6 MB)
• 4. Random Forest Algorithm.mp4 (118.1 MB)
• 5. Bias Variance Tradeoff.mp4 (127.4 MB)
• 6. CODE Random Forest.mp4 (115.6 MB)
19. Ensemble Learning Boosting
• 1. Boosting Introduction.mp4 (120.4 MB)
• 2. Boosting Intuition.mp4 (133.5 MB)
• 3. Boosting Mathematical Formulation.mp4 (211.5 MB)
• 4. Concept of Pseudo Residuals.mp4 (152.8 MB)
• 5. GBDT Algorithm.mp4 (245.2 MB)
• 6. Bias Variance Tradeoff.mp4 (83.4 MB)
• 7. CODE - Gradient Boosting Decision Trees.mp4 (131.6 MB)
• 8. XGBoost.mp4 (119.3 MB)
• 9. Adaptive Boosting (AdaBoost).mp4 (118.9 MB)
2. Supervised vs Unsupervised Learning
• 1. Supervised Learning Introduction.mp4 (78.3 MB)
• 2. Supervised Learning Example.mp4 (198.1 MB)
• 3. Unsupervised Learning.mp4 (94.0 MB)
20. PROJECT Customer Churn Prediction
• 1. Project Overview.mp4 (122.4 MB)
• 2. Exploratory Data Analysis.mp4 (103.3 MB)
• 3. Data Visualisation.mp4 (52.5 MB)
• 4. Finding relations.mp4 (67.5 MB)
• 5. Data Preparation.mp4 (61.3 MB)
• 6. Model Building.mp4 (74.6 MB)
• 7. Hyperparameter tuning.mp4 (101.2 MB)
21. Deep Learning Introduction - Neural Network
• 1. Biological Neural Network.mp4 (28.4 MB)
• 10. CODE - Model Building.mp4 (45.8 MB)
• 11. CODE - Model Training and Testing.mp4 (85.0 MB)
• 2. A Neuron.mp4 (34.1 MB)
• 3. How does a perceptron Learns.mp4 (42.8 MB)
• 4. Gradient Descent Updates.mp4 (52.8 MB)
• 5. Neural Networks.mp4 (58.0 MB)
• 6. 3 Layer NN.mp4 (28.0 MB)
• 7. Why Neural Nets.mp4 (49.9 MB)
• 8. Tensorflow Playground.mp4 (88.7 MB)
• 9. CODE -Data Preparation.mp4 (43.8 MB)
22. PROJECT Pokemon Image Classification
• 1. Introduction.mp4 (35.8 MB)
• 1.1 Dataset Link.html (0.1 KB)
• 10. Predictions.mp4 (30.2 MB)
• 2. The Data.mp4 (48.6 MB)
• 3. Structured Data.mp4 (31.9 MB)
• 4. Data Loading.mp4 (42.8 MB)
• 5. Data Preprocessing.mp4 (50.3 MB)
• 6. Model Architecture.mp4 (33.2 MB)
• 7. Softmax Function.mp4 (18.4 MB)
• 8. Model Training.mp4 (17.3 MB)
• 9. Model evaluation.mp4 (50.2 MB)
3. Linear Regression
• 1. Introduction to Linear Regression.mp4 (26.6 MB)
• 10. Code 01 - Data Generation.mp4 (68.2 MB)
• 11. Code 02 - Data Normalisation.mp4 (170.9 MB)
• 12. Code 03 - Train Test Split.mp4 (89.3 MB)
• 13. Code 04 - Modelling.mp4 (118.1 MB)
• 14. Code 05 - Predictions.mp4 (54.1 MB)
• 15. R2 Score.mp4 (139.3 MB)
• 16. Code 06 - Evaluation.mp4 (28.8 MB)
• 17. Code 07 - Visualisation.mp4 (103.4 MB)
• 18. Code 08 - Trajectory [Optional].mp4 (93.9 MB)
• 2. Notation.mp4 (171.4 MB)
• 3. Hypothesis.mp4 (95.1 MB)
• 4. Loss Error Function.mp4 (195.4 MB)
• 5. Training Idea.mp4 (48.3 MB)
• 6. Gradient Descent Optimisation.mp4 (110.4 MB)
• 7. Gradient Descent Code.mp4 (271.3 MB)
• 8. Gradient Descent - for Linear Regression.mp4 (51.8 MB)
• 9. The Math of Training.mp4 (105.3 MB)
4. Linear Regression - Multiple Features 0. Websites you may like
• [CourseClub.Me].url (0.1 KB)
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• 1. Introduction.mp4 (88.2 MB)
• 10. A Note about Shapes.mp4 (30.1 MB)
• 11. Code 06 - Evaluation.mp4 (50.9 MB)
• 12. Linear Regression using Sk-Learn.mp4 (35.4 MB)
• 2. Hypothesis.mp4 (28.8 MB)
• 3. Loss Function.mp4 (33.2 MB)
• 4. Training & Gradient Updates.mp4 (43.3 MB)
• 5. Code 01 - Data Prep.mp4 (104.3 MB)
• 6. Code 02 - Hypothesis.mp4 (78.5 MB)
• 7. Code 03 - Loss Function.mp4 (22.5 MB)
• 8. Code 04 - Gradient Computation.mp4 (222.3 MB)
• 9. Code 05 - Training Loop.mp4 (86.7 MB)
5. Logistic Regression
• 1. Binary Classification Introduction.mp4 (85.5 MB)
• 10. Code 05 - Training Loop.mp4 (61.6 MB)
• 11. Code 06 - Visualise Decision Boundary.mp4 (43.1 MB)
• 12. Code 07 - Predictions & Accuracy.mp4 (55.5 MB)
• 13. Logistic Regression using Sk-Learn.mp4 (29.5 MB)
• 14. Multiclass Classification One Vs Rest.mp4 (72.4 MB)
• 15. Multiclass Classification One Vs One.mp4 (33.5 MB)
• 2. Notation.mp4 (105.3 MB)
• 3. Hypothesis Function.mp4 (272.3 MB)
• 4. Binary Cross-Entropy Loss Function.mp4 (90.8 MB)
• 5. Gradient Update Rule.mp4 (146.6 MB)
• 6. Code 01 - Data Prep.mp4 (79.9 MB)
• 7. Code 02 - Hypothesis Logit Model.mp4 (34.1 MB)
• 8. Code 03 - Binary Cross Entropy Loss.mp4 (19.4 MB)
• 9. Code 04 - Gradient Computation.mp4 (45.2 MB)
6. Dimensionality Reduction Feature Selection
• 1. Curse of Dimensionality.mp4 (17.0 MB)
• 2. Feature Selection Vs. Feature Extraction.mp4 (15.1 MB)
• 3. Filter Method.mp4 (23.5 MB)
• 4. Wrapper Method.mp4 (23.0 MB)
• 5. Embedded Method.mp4 (12.8 MB)
• 6. Feature Selection - Code.mp4 (63.6 MB)
• 6.1 train.csv (119.5 KB)
7. Principal Component Analysis (PCA)
• 1. Introduction to PCA.mp4 (63.4 MB)
• 2. Conceptual Overview of PCA.mp4 (140.9 MB)
• 3. Maximising Variance.mp4 (178.0 MB)
• 4. Minimising Distances.mp4 (95.3 MB)
• 5. Eigen Values & Eigen Vectors.mp4 (48.5 MB)
• 6. PCA Summary.mp4 (18.3 MB)
• 7. Understanding Eigen Values.mp4 (44.6 MB)
• 8. PCA Code.mp4 (50.6 MB)
• 9. Choosing the right dimensions.mp4 (45.4 MB)
8. K-Nearest Neigbours
• 1. Introduction.mp4 (45.0 MB)
• 2. KNN Idea.mp4 (34.5 MB)
• 3. KNN Data Prep.mp4 (29.2 MB)
• 4. KNN Algorithm Code.mp4 (90.8 MB)
• 5. Euclidean and Manhattan Distance.mp4 (14.9 MB)
• 6. Deciding value of K.mp4 (6.8 MB)
• 7. KNN and Data Standardisation.mp4 (15.2 MB)
• 8. KNN Pros and Cons.mp4 (53.8 MB)
• 9. KNN using Sk-Learn.html (0.4 KB)
9. PROJECT - Face Recognition
• 1. OpenCV - Working with Images.mp4 (34.0 MB)
• 2. OpenCV - Video Input from WebCam.mp4 (34.2 MB)
• 3. Object Detection using Haarcascades.mp4 (79.6 MB)
• 4. Face Detection in Images.mp4 (78.7 MB)
• 5. Face Detection in Live Video.mp4 (49.3 MB)
• 6. Face Recognition Project Intro.mp4 (15.2 MB)
• 7. Face Recognition 01 - Data Collection.mp4 (198.0 MB)
• 8. Face Recognition 02 - Loading Data.mp4 (71.7 MB)
• 9. Face Recognition 03 - Predictions using KNN.mp4 (99.6 MB)