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