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| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [FreeCourseSite.com].url |
127б |
| [FreeCourseSite.com].url |
127б |
| [FreeCourseSite.com].url |
127б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
49б |
| 1.1 Dataset Link.html |
129б |
| 1.1 titanic_train.csv |
58.89Кб |
| 1. Bayes Theorem.mp4 |
87.29Мб |
| 1. Binary Classification Introduction.mp4 |
85.48Мб |
| 1. Biological Neural Network.mp4 |
28.40Мб |
| 1. Boosting Introduction.mp4 |
120.37Мб |
| 1. CODE - Decision Tree Node.mp4 |
61.16Мб |
| 1. Course Overview.mp4 |
49.63Мб |
| 1. Curse of Dimensionality.mp4 |
17.01Мб |
| 1. Decision Trees Introduction.mp4 |
77.97Мб |
| 1. Ensemble Learning.mp4 |
69.31Мб |
| 1. Introduction.mp4 |
25.13Мб |
| 1. Introduction.mp4 |
35.78Мб |
| 1. Introduction.mp4 |
88.23Мб |
| 1. Introduction.mp4 |
45.01Мб |
| 1. Introduction to Linear Regression.mp4 |
26.62Мб |
| 1. Introduction to PCA.mp4 |
63.37Мб |
| 1. K-Means Algorithm.mp4 |
60.14Мб |
| 1. Multinomial Naive Bayes.mp4 |
141.13Мб |
| 1. OpenCV - Working with Images.mp4 |
33.96Мб |
| 1. Project Overview.mp4 |
87.44Мб |
| 1. Project Overview.mp4 |
100.82Мб |
| 1. Project Overview.mp4 |
122.36Мб |
| 1. Supervised Learning Introduction.mp4 |
78.34Мб |
| 10. A Note about Shapes.mp4 |
30.11Мб |
| 10. Code 01 - Data Generation.mp4 |
68.16Мб |
| 10. Code 05 - Training Loop.mp4 |
61.59Мб |
| 10. CODE - Likelihood.mp4 |
166.48Мб |
| 10. CODE - Model Building.mp4 |
45.77Мб |
| 10. Code Repository.html |
236б |
| 10. Decision Trees for Regression.mp4 |
89.47Мб |
| 10. Predictions.mp4 |
30.23Мб |
| 11. Code 02 - Data Normalisation.mp4 |
170.86Мб |
| 11. Code 06 - Evaluation.mp4 |
50.89Мб |
| 11. Code 06 - Visualise Decision Boundary.mp4 |
43.12Мб |
| 11. CODE - Model Training and Testing.mp4 |
84.95Мб |
| 11. CODE - Prediction.mp4 |
71.40Мб |
| 11. Decision Tree Code - Sklearn.mp4 |
36.74Мб |
| 12. Code 03 - Train Test Split.mp4 |
89.26Мб |
| 12. Code 07 - Predictions & Accuracy.mp4 |
55.52Мб |
| 12. Implementing Naive Bayes - Sklearn.mp4 |
111.53Мб |
| 12. Linear Regression using Sk-Learn.mp4 |
35.44Мб |
| 13. Code 04 - Modelling.mp4 |
118.10Мб |
| 13. Logistic Regression using Sk-Learn.mp4 |
29.51Мб |
| 14. Code 05 - Predictions.mp4 |
54.10Мб |
| 14. Multiclass Classification One Vs Rest.mp4 |
72.43Мб |
| 15. Multiclass Classification One Vs One.mp4 |
33.49Мб |
| 15. R2 Score.mp4 |
139.34Мб |
| 16. Code 06 - Evaluation.mp4 |
28.80Мб |
| 17. Code 07 - Visualisation.mp4 |
103.43Мб |
| 18. Code 08 - Trajectory [Optional].mp4 |
93.94Мб |
| 2. A Neuron.mp4 |
34.11Мб |
| 2. Artificial Intelligence.mp4 |
48.59Мб |
| 2. Bagging Model.mp4 |
128.81Мб |
| 2. Boosting Intuition.mp4 |
133.52Мб |
| 2. Code 01 - Data Prep.mp4 |
18.59Мб |
| 2. CODE - Train Decision Tree.mp4 |
119.74Мб |
| 2. Conceptual Overview of PCA.mp4 |
140.86Мб |
| 2. Data Clearning.mp4 |
157.94Мб |
| 2. Decision Trees Example.mp4 |
137.37Мб |
| 2. Derivation of Bayes Theorem.mp4 |
74.83Мб |
| 2. Exploratory Data Analysis.mp4 |
83.80Мб |
| 2. Exploratory Data Analysis.mp4 |
103.26Мб |
| 2. Feature Selection Vs. Feature Extraction.mp4 |
15.11Мб |
| 2. Hypothesis.mp4 |
28.79Мб |
| 2. KNN Idea.mp4 |
34.52Мб |
| 2. Laplace Smoothing.mp4 |
91.51Мб |
| 2. Notation.mp4 |
171.35Мб |
| 2. Notation.mp4 |
105.31Мб |
| 2. OpenCV - Video Input from WebCam.mp4 |
34.22Мб |
| 2. Reading Images.mp4 |
24.16Мб |
| 2. Supervised Learning Example.mp4 |
198.07Мб |
| 2. The Data.mp4 |
48.60Мб |
| 3. Bayes Theorem Question.mp4 |
144.97Мб |
| 3. Boosting Mathematical Formulation.mp4 |
211.50Мб |
| 3. Code 02 - Init Centers.mp4 |
65.72Мб |
| 3. CODE - Assign Target Variable to Each Node.mp4 |
59.92Мб |
| 3. Data Visualisation.mp4 |
52.50Мб |
| 3. Entropy.mp4 |
118.43Мб |
| 3. Exploratory Data Analysis - II.mp4 |
79.02Мб |
| 3. Filter Method.mp4 |
23.48Мб |
| 3. Finding Clusters.mp4 |
53.86Мб |
| 3. How does a perceptron Learns.mp4 |
42.76Мб |
| 3. Hypothesis.mp4 |
95.10Мб |
| 3. Hypothesis Function.mp4 |
272.27Мб |
| 3. KNN Data Prep.mp4 |
29.22Мб |
| 3. Loss Function.mp4 |
33.18Мб |
| 3. Machine Learning.mp4 |
66.98Мб |
| 3. Maximising Variance.mp4 |
177.98Мб |
| 3. Multinomial Naive Bayes Example.mp4 |
179.20Мб |
| 3. Object Detection using Haarcascades.mp4 |
79.62Мб |
| 3. Structured Data.mp4 |
31.89Мб |
| 3. Unsupervised Learning.mp4 |
93.96Мб |
| 3. Why Bagging Helps.mp4 |
142.64Мб |
| 3. WordCloud.mp4 |
106.22Мб |
| 4. Bernoulli Naive Bayes.mp4 |
204.73Мб |
| 4. Binary Cross-Entropy Loss Function.mp4 |
90.80Мб |
| 4. Code 03 - Assigning Points.mp4 |
75.64Мб |
| 4. CODE Entropy.mp4 |
70.11Мб |
| 4. CODE - Stopping Conditions.mp4 |
72.38Мб |
| 4. Concept of Pseudo Residuals.mp4 |
152.80Мб |
| 4. Data Loading.mp4 |
42.77Мб |
| 4. Data Preparation for ML Model.mp4 |
83.35Мб |
| 4. Deep Learning.mp4 |
54.49Мб |
| 4. Dominant Color Swatches.mp4 |
39.75Мб |
| 4. Face Detection in Images.mp4 |
78.72Мб |
| 4. Finding relations.mp4 |
67.46Мб |
| 4. Gradient Descent Updates.mp4 |
52.76Мб |
| 4. KNN Algorithm Code.mp4 |
90.78Мб |
| 4. Loss Error Function.mp4 |
195.40Мб |
| 4. Minimising Distances.mp4 |
95.26Мб |
| 4. Naive Bayes Algorithm.mp4 |
80.75Мб |
| 4. Random Forest Algorithm.mp4 |
118.06Мб |
| 4. Text Featurization.mp4 |
44.18Мб |
| 4. Training & Gradient Updates.mp4 |
43.29Мб |
| 4. Wrapper Method.mp4 |
23.03Мб |
| 5. Bernoulli Naive Bayes Example.mp4 |
138.28Мб |
| 5. Bias Variance Tradeoff.mp4 |
127.40Мб |
| 5. Code 01 - Data Prep.mp4 |
104.28Мб |
| 5. Code 04 - Updating Centroids.mp4 |
59.08Мб |
| 5. CODE - Train Child Nodes.mp4 |
83.39Мб |
| 5. Computer Vision.mp4 |
43.10Мб |
| 5. Data Preparation.mp4 |
61.32Мб |
| 5. Data Preprocessing.mp4 |
50.25Мб |
| 5. Eigen Values & Eigen Vectors.mp4 |
48.45Мб |
| 5. Embedded Method.mp4 |
12.81Мб |
| 5. Euclidean and Manhattan Distance.mp4 |
14.88Мб |
| 5. Face Detection in Live Video.mp4 |
49.28Мб |
| 5. GBDT Algorithm.mp4 |
245.24Мб |
| 5. Gradient Update Rule.mp4 |
146.56Мб |
| 5. Handling Missing Values.mp4 |
94.81Мб |
| 5. Image in K-Colors.mp4 |
71.05Мб |
| 5. Information Gain.mp4 |
199.50Мб |
| 5. Model Building.mp4 |
52.09Мб |
| 5. Naive Bayes for Text Classification.mp4 |
160.71Мб |
| 5. Neural Networks.mp4 |
57.96Мб |
| 5. Training Idea.mp4 |
48.32Мб |
| 6.1 train.csv |
119.53Кб |
| 6. 3 Layer NN.mp4 |
27.99Мб |
| 6. Bias Variance Tradeoff.mp4 |
94.41Мб |
| 6. Bias Variance Tradeoff.mp4 |
83.36Мб |
| 6. Code 01 - Data Prep.mp4 |
79.86Мб |
| 6. Code 02 - Hypothesis.mp4 |
78.53Мб |
| 6. Code 05 - Visualizing K-Means & Results.mp4 |
81.76Мб |
| 6. CODE - Explore Decision Tree Model.mp4 |
102.29Мб |
| 6. CODE Random Forest.mp4 |
115.59Мб |
| 6. CODE Split Data.mp4 |
135.75Мб |
| 6. Computing Likelihood.mp4 |
193.16Мб |
| 6. Deciding value of K.mp4 |
6.77Мб |
| 6. Decision Tree Model Building.mp4 |
77.82Мб |
| 6. Face Recognition Project Intro.mp4 |
15.16Мб |
| 6. Feature Selection - Code.mp4 |
63.58Мб |
| 6. Gradient Descent Optimisation.mp4 |
110.37Мб |
| 6. Model Architecture.mp4 |
33.24Мб |
| 6. Model Building.mp4 |
74.64Мб |
| 6. Model Evaluation.mp4 |
67.87Мб |
| 6. Natural Language Processing.mp4 |
64.43Мб |
| 6. PCA Summary.mp4 |
18.32Мб |
| 7.1 golf.csv |
430б |
| 7. Automatic Speech Recognition.mp4 |
100.73Мб |
| 7. Code 02 - Hypothesis Logit Model.mp4 |
34.12Мб |
| 7. Code 03 - Loss Function.mp4 |
22.55Мб |
| 7. CODE - Gradient Boosting Decision Trees.mp4 |
131.61Мб |
| 7. CODE Information Gain.mp4 |
93.78Мб |
| 7. CODE - Prediction.mp4 |
116.39Мб |
| 7. Face Recognition 01 - Data Collection.mp4 |
197.98Мб |
| 7. Gaussian Naive Bayes.mp4 |
109.34Мб |
| 7. Gradient Descent Code.mp4 |
271.34Мб |
| 7. Hyperparameter tuning.mp4 |
101.19Мб |
| 7. KNN and Data Standardisation.mp4 |
15.24Мб |
| 7. Softmax Function.mp4 |
18.41Мб |
| 7. Understanding Eigen Values.mp4 |
44.64Мб |
| 7. Understanding Golf Dataset.mp4 |
218.74Мб |
| 7. Visualize Decision Tree.mp4 |
92.64Мб |
| 7. Why Neural Nets.mp4 |
49.85Мб |
| 8. Code 03 - Binary Cross Entropy Loss.mp4 |
19.41Мб |
| 8. Code 04 - Gradient Computation.mp4 |
222.29Мб |
| 8. CODE - Prior Probability.mp4 |
61.12Мб |
| 8. CODE - Variants of Naive Bayes.mp4 |
93.93Мб |
| 8. Construction of Decision Trees.mp4 |
66.41Мб |
| 8. Face Recognition 02 - Loading Data.mp4 |
71.69Мб |
| 8. Gradient Descent - for Linear Regression.mp4 |
51.80Мб |
| 8. Handling Numeric Features.mp4 |
110.00Мб |
| 8. KNN Pros and Cons.mp4 |
53.75Мб |
| 8. Model Training.mp4 |
17.34Мб |
| 8. PCA Code.mp4 |
50.59Мб |
| 8. Reinforcement Learning.mp4 |
43.88Мб |
| 8. Tensorflow Playground.mp4 |
88.69Мб |
| 8. XGBoost.mp4 |
119.31Мб |
| 9. Adaptive Boosting (AdaBoost).mp4 |
118.85Мб |
| 9. Bias Variance Tradeoff.mp4 |
58.92Мб |
| 9. Choosing the right dimensions.mp4 |
45.42Мб |
| 9. Code 04 - Gradient Computation.mp4 |
45.25Мб |
| 9. Code 05 - Training Loop.mp4 |
86.74Мб |
| 9. CODE - Conditional Probability.mp4 |
108.07Мб |
| 9. CODE -Data Preparation.mp4 |
43.75Мб |
| 9. Face Recognition 03 - Predictions using KNN.mp4 |
99.65Мб |
| 9. KNN using Sk-Learn.html |
405б |
| 9. Model evaluation.mp4 |
50.24Мб |
| 9. Pre-requisites.html |
889б |
| 9. Stopping Conditions.mp4 |
98.27Мб |
| 9. The Math of Training.mp4 |
105.27Мб |