[FreeCourseSite.Com] Udemy - Machine Learning Essentials (2023) - Master Core ML Concepts

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[FreeCourseSite.com] Udemy - Machine Learning Essentials <span style=color:#777>(2023)</span> - Master core ML concepts[FreeCourseSite.com] Udemy - Machine Learning Essentials <span style=color:#777>(2023)</span> - Master core ML concepts

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

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

Created by Mohit Uniyal, Prateek Narang
Last updated 5/2023
English
English [Auto]

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Files in this torrent

FILENAMESIZE
1. Introduction/1. Course Overview.mp449.6 MB
1. Introduction/10. Code Repository.html236 B
1. Introduction/2. Artificial Intelligence.mp448.6 MB
1. Introduction/3. Machine Learning.mp467 MB
1. Introduction/4. Deep Learning.mp454.5 MB
1. Introduction/5. Computer Vision.mp443.1 MB
1. Introduction/6. Natural Language Processing.mp464.4 MB
1. Introduction/7. Automatic Speech Recognition.mp4100.7 MB
1. Introduction/8. Reinforcement Learning.mp443.9 MB
1. Introduction/9. Pre-requisites.html889 B
10. K-Means/1. K-Means Algorithm.mp460.1 MB
10. K-Means/2. Code 01 - Data Prep.mp418.6 MB
10. K-Means/3. Code 02 - Init Centers.mp465.7 MB
10. K-Means/4. Code 03 - Assigning Points.mp475.6 MB
10. K-Means/5. Code 04 - Updating Centroids.mp459.1 MB
10. K-Means/6. Code 05 - Visualizing K-Means & Results.mp481.8 MB
11. Project - Dominant Color Extraction/1. Introduction.mp425.1 MB
11. Project - Dominant Color Extraction/2. Reading Images.mp424.2 MB
11. Project - Dominant Color Extraction/3. Finding Clusters.mp453.9 MB
11. Project - Dominant Color Extraction/4. Dominant Color Swatches.mp439.7 MB
11. Project - Dominant Color Extraction/5. Image in K-Colors.mp471 MB
12. Naive Bayes Algorithm/1. Bayes Theorem.mp487.3 MB
12. Naive Bayes Algorithm/10. CODE - Likelihood.mp4166.5 MB
12. Naive Bayes Algorithm/11. CODE - Prediction.mp471.4 MB
12. Naive Bayes Algorithm/12. Implementing Naive Bayes - Sklearn.mp4111.5 MB
12. Naive Bayes Algorithm/2. Derivation of Bayes Theorem.mp474.8 MB
12. Naive Bayes Algorithm/3. Bayes Theorem Question.mp4145 MB
12. Naive Bayes Algorithm/4. Naive Bayes Algorithm.mp480.7 MB
12. Naive Bayes Algorithm/5. Naive Bayes for Text Classification.mp4160.7 MB
12. Naive Bayes Algorithm/6. Computing Likelihood.mp4193.2 MB
12. Naive Bayes Algorithm/7. Understanding Golf Dataset.mp4218.7 MB
12. Naive Bayes Algorithm/7.1 golf.csv430 B
12. Naive Bayes Algorithm/8. CODE - Prior Probability.mp461.1 MB
12. Naive Bayes Algorithm/9. CODE - Conditional Probability.mp4108.1 MB
13. Multinomial Naive Bayes/1. Multinomial Naive Bayes.mp4141.1 MB
13. Multinomial Naive Bayes/2. Laplace Smoothing.mp491.5 MB
13. Multinomial Naive Bayes/3. Multinomial Naive Bayes Example.mp4179.2 MB
13. Multinomial Naive Bayes/4. Bernoulli Naive Bayes.mp4204.7 MB
13. Multinomial Naive Bayes/5. Bernoulli Naive Bayes Example.mp4138.3 MB
13. Multinomial Naive Bayes/6. Bias Variance Tradeoff.mp494.4 MB
13. Multinomial Naive Bayes/7. Gaussian Naive Bayes.mp4109.3 MB
13. Multinomial Naive Bayes/8. CODE - Variants of Naive Bayes.mp493.9 MB
14. PROJECT Spam Classifier/1. Project Overview.mp487.4 MB
14. PROJECT Spam Classifier/2. Data Clearning.mp4157.9 MB
14. PROJECT Spam Classifier/3. WordCloud.mp4106.2 MB
14. PROJECT Spam Classifier/4. Text Featurization.mp444.2 MB
14. PROJECT Spam Classifier/5. Model Building.mp452.1 MB
14. PROJECT Spam Classifier/6. Model Evaluation.mp467.9 MB
15. Decision Trees/1. Decision Trees Introduction.mp478 MB
15. Decision Trees/2. Decision Trees Example.mp4137.4 MB
15. Decision Trees/3. Entropy.mp4118.4 MB
15. Decision Trees/4. CODE Entropy.mp470.1 MB
15. Decision Trees/5. Information Gain.mp4199.5 MB
15. Decision Trees/6. CODE Split Data.mp4135.8 MB
15. Decision Trees/7. CODE Information Gain.mp493.8 MB
15. Decision Trees/8. Construction of Decision Trees.mp466.4 MB
15. Decision Trees/9. Stopping Conditions.mp498.3 MB
16. Decision Trees Implementation/1. CODE - Decision Tree Node.mp461.2 MB
16. Decision Trees Implementation/10. Decision Trees for Regression.mp489.5 MB
16. Decision Trees Implementation/11. Decision Tree Code - Sklearn.mp436.7 MB
16. Decision Trees Implementation/2. CODE - Train Decision Tree.mp4119.7 MB
16. Decision Trees Implementation/3. CODE - Assign Target Variable to Each Node.mp459.9 MB
16. Decision Trees Implementation/4. CODE - Stopping Conditions.mp472.4 MB
16. Decision Trees Implementation/5. CODE - Train Child Nodes.mp483.4 MB
16. Decision Trees Implementation/6. CODE - Explore Decision Tree Model.mp4102.3 MB
16. Decision Trees Implementation/7. CODE - Prediction.mp4116.4 MB
16. Decision Trees Implementation/8. Handling Numeric Features.mp4110 MB
16. Decision Trees Implementation/9. Bias Variance Tradeoff.mp458.9 MB
17. PROJECT Titanic Survival Prediction/1. Project Overview.mp4100.8 MB
17. PROJECT Titanic Survival Prediction/1.1 titanic_train.csv58.9 KB
17. PROJECT Titanic Survival Prediction/2. Exploratory Data Analysis.mp483.8 MB
17. PROJECT Titanic Survival Prediction/3. Exploratory Data Analysis - II.mp479 MB
17. PROJECT Titanic Survival Prediction/4. Data Preparation for ML Model.mp483.4 MB
17. PROJECT Titanic Survival Prediction/5. Handling Missing Values.mp494.8 MB
17. PROJECT Titanic Survival Prediction/6. Decision Tree Model Building.mp477.8 MB
17. PROJECT Titanic Survival Prediction/7. Visualize Decision Tree.mp492.6 MB
18. Ensemble Learning Bagging/1. Ensemble Learning.mp469.3 MB
18. Ensemble Learning Bagging/2. Bagging Model.mp4128.8 MB
18. Ensemble Learning Bagging/3. Why Bagging Helps.mp4142.6 MB
18. Ensemble Learning Bagging/4. Random Forest Algorithm.mp4118.1 MB
18. Ensemble Learning Bagging/5. Bias Variance Tradeoff.mp4127.4 MB
18. Ensemble Learning Bagging/6. CODE Random Forest.mp4115.6 MB
19. Ensemble Learning Boosting/1. Boosting Introduction.mp4120.4 MB
19. Ensemble Learning Boosting/2. Boosting Intuition.mp4133.5 MB
19. Ensemble Learning Boosting/3. Boosting Mathematical Formulation.mp4211.5 MB
19. Ensemble Learning Boosting/4. Concept of Pseudo Residuals.mp4152.8 MB
19. Ensemble Learning Boosting/5. GBDT Algorithm.mp4245.2 MB
19. Ensemble Learning Boosting/6. Bias Variance Tradeoff.mp483.4 MB
19. Ensemble Learning Boosting/7. CODE - Gradient Boosting Decision Trees.mp4131.6 MB
19. Ensemble Learning Boosting/8. XGBoost.mp4119.3 MB

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