FreeCourseSite com Udemy Machine Learning Essentials 2023 Master core ML concepts

FreeCourseSite com Udemy Machine Learning Essentials 2023 Master core ML concepts

Name	[FreeCourseSite.com] Udemy - Machine Learning Essentials (2023) - Master core ML concepts	DOWNLOAD Copy Link
Total Size	17.0 GB
Total Files	214
Last Seen	2024-07-08 23:36
Hash	DFF3F9FA09449DC2C837C358F8DEBB0414345AFB

/0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/1. Introduction/
1. Course Overview.mp4	52.0 MB
10. Code Repository.html	0.2 KB
2. Artificial Intelligence.mp4	51.0 MB
3. Machine Learning.mp4	70.2 MB
4. Deep Learning.mp4	57.1 MB
5. Computer Vision.mp4	45.2 MB
6. Natural Language Processing.mp4	67.6 MB
7. Automatic Speech Recognition.mp4	105.6 MB
8. Reinforcement Learning.mp4	46.0 MB
9. Pre-requisites.html	0.9 KB
/.../0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/10. K-Means/
1. K-Means Algorithm.mp4	63.1 MB
2. Code 01 - Data Prep.mp4	19.5 MB
3. Code 02 - Init Centers.mp4	68.9 MB
4. Code 03 - Assigning Points.mp4	79.3 MB
5. Code 04 - Updating Centroids.mp4	61.9 MB
6. Code 05 - Visualizing K-Means & Results.mp4	85.7 MB
/11. Project - Dominant Color Extraction/
1. Introduction.mp4	26.4 MB
2. Reading Images.mp4	25.3 MB
3. Finding Clusters.mp4	56.5 MB
4. Dominant Color Swatches.mp4	41.7 MB
5. Image in K-Colors.mp4	74.5 MB
/12. Naive Bayes Algorithm/
1. Bayes Theorem.mp4	91.5 MB
10. CODE - Likelihood.mp4	174.6 MB
11. CODE - Prediction.mp4	74.9 MB
12. Implementing Naive Bayes - Sklearn.mp4	116.9 MB
2. Derivation of Bayes Theorem.mp4	78.5 MB
3. Bayes Theorem Question.mp4	152.0 MB
4. Naive Bayes Algorithm.mp4	84.7 MB
5. Naive Bayes for Text Classification.mp4	168.5 MB
6. Computing Likelihood.mp4	202.5 MB
7. Understanding Golf Dataset.mp4	229.4 MB
7.1 golf.csv	0.4 KB
8. CODE - Prior Probability.mp4	64.1 MB
9. CODE - Conditional Probability.mp4	113.3 MB
/13. Multinomial Naive Bayes/
1. Multinomial Naive Bayes.mp4	148.0 MB
2. Laplace Smoothing.mp4	96.0 MB
3. Multinomial Naive Bayes Example.mp4	187.9 MB
4. Bernoulli Naive Bayes.mp4	214.7 MB
5. Bernoulli Naive Bayes Example.mp4	145.0 MB
6. Bias Variance Tradeoff.mp4	99.0 MB
7. Gaussian Naive Bayes.mp4	114.7 MB
8. CODE - Variants of Naive Bayes.mp4	98.5 MB
/14. PROJECT Spam Classifier/
1. Project Overview.mp4	91.7 MB
2. Data Clearning.mp4	165.6 MB
3. WordCloud.mp4	111.4 MB
4. Text Featurization.mp4	46.3 MB
5. Model Building.mp4	54.6 MB
6. Model Evaluation.mp4	71.2 MB
/.../0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/15. Decision Trees/
1. Decision Trees Introduction.mp4	81.8 MB
2. Decision Trees Example.mp4	144.0 MB
3. Entropy.mp4	124.2 MB
4. CODE Entropy.mp4	73.5 MB
5. Information Gain.mp4	209.2 MB
6. CODE Split Data.mp4	142.3 MB
7. CODE Information Gain.mp4	98.3 MB
8. Construction of Decision Trees.mp4	69.6 MB
9. Stopping Conditions.mp4	103.0 MB
/16. Decision Trees Implementation/
1. CODE - Decision Tree Node.mp4	64.1 MB
10. Decision Trees for Regression.mp4	93.8 MB
11. Decision Tree Code - Sklearn.mp4	38.5 MB
2. CODE - Train Decision Tree.mp4	125.6 MB
3. CODE - Assign Target Variable to Each Node.mp4	62.8 MB
4. CODE - Stopping Conditions.mp4	75.9 MB
5. CODE - Train Child Nodes.mp4	87.4 MB
6. CODE - Explore Decision Tree Model.mp4	107.3 MB
7. CODE - Prediction.mp4	122.0 MB
8. Handling Numeric Features.mp4	115.3 MB
9. Bias Variance Tradeoff.mp4	61.8 MB
/17. PROJECT Titanic Survival Prediction/
1. Project Overview.mp4	105.7 MB
1.1 titanic_train.csv	60.3 KB
2. Exploratory Data Analysis.mp4	87.9 MB
3. Exploratory Data Analysis - II.mp4	82.9 MB
4. Data Preparation for ML Model.mp4	87.4 MB
5. Handling Missing Values.mp4	99.4 MB
6. Decision Tree Model Building.mp4	81.6 MB
7. Visualize Decision Tree.mp4	97.1 MB
/18. Ensemble Learning Bagging/
1. Ensemble Learning.mp4	72.7 MB
2. Bagging Model.mp4	135.1 MB
3. Why Bagging Helps.mp4	149.6 MB
4. Random Forest Algorithm.mp4	123.8 MB
5. Bias Variance Tradeoff.mp4	133.6 MB
6. CODE Random Forest.mp4	121.2 MB
/19. Ensemble Learning Boosting/
1. Boosting Introduction.mp4	126.2 MB
2. Boosting Intuition.mp4	140.0 MB
3. Boosting Mathematical Formulation.mp4	221.8 MB
4. Concept of Pseudo Residuals.mp4	160.2 MB
5. GBDT Algorithm.mp4	257.1 MB
6. Bias Variance Tradeoff.mp4	87.4 MB
7. CODE - Gradient Boosting Decision Trees.mp4	138.0 MB
8. XGBoost.mp4	125.1 MB
9. Adaptive Boosting (AdaBoost).mp4	124.6 MB
/2. Supervised vs Unsupervised Learning/
1. Supervised Learning Introduction.mp4	82.1 MB
2. Supervised Learning Example.mp4	207.7 MB
3. Unsupervised Learning.mp4	98.5 MB
/20. PROJECT Customer Churn Prediction/
1. Project Overview.mp4	128.3 MB
2. Exploratory Data Analysis.mp4	108.3 MB
3. Data Visualisation.mp4	55.1 MB
4. Finding relations.mp4	70.7 MB
5. Data Preparation.mp4	64.3 MB
6. Model Building.mp4	78.3 MB
7. Hyperparameter tuning.mp4	106.1 MB
/21. Deep Learning Introduction - Neural Network/
1. Biological Neural Network.mp4	29.8 MB
10. CODE - Model Building.mp4	48.0 MB
11. CODE - Model Training and Testing.mp4	89.1 MB
2. A Neuron.mp4	35.8 MB
3. How does a perceptron Learns.mp4	44.8 MB
4. Gradient Descent Updates.mp4	55.3 MB
5. Neural Networks.mp4	60.8 MB
6. 3 Layer NN.mp4	29.4 MB
7. Why Neural Nets.mp4	52.3 MB
8. Tensorflow Playground.mp4	93.0 MB
9. CODE -Data Preparation.mp4	45.9 MB
/22. PROJECT Pokemon Image Classification/
1. Introduction.mp4	37.5 MB
1.1 Dataset Link.html	0.1 KB
10. Predictions.mp4	31.7 MB
2. The Data.mp4	51.0 MB
3. Structured Data.mp4	33.4 MB
4. Data Loading.mp4	44.8 MB
5. Data Preprocessing.mp4	52.7 MB
6. Model Architecture.mp4	34.9 MB
7. Softmax Function.mp4	19.3 MB
8. Model Training.mp4	18.2 MB
9. Model evaluation.mp4	52.7 MB
/3. Linear Regression/
1. Introduction to Linear Regression.mp4	27.9 MB
10. Code 01 - Data Generation.mp4	71.5 MB
11. Code 02 - Data Normalisation.mp4	179.2 MB
12. Code 03 - Train Test Split.mp4	93.6 MB
13. Code 04 - Modelling.mp4	123.8 MB
14. Code 05 - Predictions.mp4	56.7 MB
15. R2 Score.mp4	146.1 MB
16. Code 06 - Evaluation.mp4	30.2 MB
17. Code 07 - Visualisation.mp4	108.5 MB
18. Code 08 - Trajectory [Optional].mp4	98.5 MB
2. Notation.mp4	179.7 MB
3. Hypothesis.mp4	99.7 MB
4. Loss Error Function.mp4	204.9 MB
5. Training Idea.mp4	50.7 MB
6. Gradient Descent Optimisation.mp4	115.7 MB
7. Gradient Descent Code.mp4	284.5 MB
8. Gradient Descent - for Linear Regression.mp4	54.3 MB
9. The Math of Training.mp4	110.4 MB
/.../0. Websites you may like/
[CourseClub.Me].url	0.1 KB
[FreeCourseSite.com].url	0.1 KB
[GigaCourse.Com].url	0.0 KB
/4. Linear Regression - Multiple Features/
1. Introduction.mp4	92.5 MB
10. A Note about Shapes.mp4	31.6 MB
11. Code 06 - Evaluation.mp4	53.4 MB
12. Linear Regression using Sk-Learn.mp4	37.2 MB
2. Hypothesis.mp4	30.2 MB
3. Loss Function.mp4	34.8 MB
4. Training & Gradient Updates.mp4	45.4 MB
5. Code 01 - Data Prep.mp4	109.3 MB
6. Code 02 - Hypothesis.mp4	82.3 MB
7. Code 03 - Loss Function.mp4	23.6 MB
8. Code 04 - Gradient Computation.mp4	233.1 MB
9. Code 05 - Training Loop.mp4	91.0 MB
/5. Logistic Regression/
1. Binary Classification Introduction.mp4	89.6 MB
10. Code 05 - Training Loop.mp4	64.6 MB
11. Code 06 - Visualise Decision Boundary.mp4	45.2 MB
12. Code 07 - Predictions & Accuracy.mp4	58.2 MB
13. Logistic Regression using Sk-Learn.mp4	30.9 MB
14. Multiclass Classification One Vs Rest.mp4	75.9 MB
15. Multiclass Classification One Vs One.mp4	35.1 MB
2. Notation.mp4	110.4 MB
3. Hypothesis Function.mp4	285.5 MB
4. Binary Cross-Entropy Loss Function.mp4	95.2 MB
5. Gradient Update Rule.mp4	153.7 MB
6. Code 01 - Data Prep.mp4	83.7 MB
7. Code 02 - Hypothesis Logit Model.mp4	35.8 MB
8. Code 03 - Binary Cross Entropy Loss.mp4	20.4 MB
9. Code 04 - Gradient Computation.mp4	47.4 MB
/6. Dimensionality Reduction Feature Selection/
1. Curse of Dimensionality.mp4	17.8 MB
2. Feature Selection Vs. Feature Extraction.mp4	15.8 MB
3. Filter Method.mp4	24.6 MB
4. Wrapper Method.mp4	24.1 MB
5. Embedded Method.mp4	13.4 MB
6. Feature Selection - Code.mp4	66.7 MB
6.1 train.csv	122.4 KB
/7. Principal Component Analysis (PCA)/
1. Introduction to PCA.mp4	66.4 MB
2. Conceptual Overview of PCA.mp4	147.7 MB
3. Maximising Variance.mp4	186.6 MB
4. Minimising Distances.mp4	99.9 MB
5. Eigen Values & Eigen Vectors.mp4	50.8 MB
6. PCA Summary.mp4	19.2 MB
7. Understanding Eigen Values.mp4	46.8 MB
8. PCA Code.mp4	53.0 MB
9. Choosing the right dimensions.mp4	47.6 MB
/8. K-Nearest Neigbours/
1. Introduction.mp4	47.2 MB
2. KNN Idea.mp4	36.2 MB
3. KNN Data Prep.mp4	30.6 MB
4. KNN Algorithm Code.mp4	95.2 MB
5. Euclidean and Manhattan Distance.mp4	15.6 MB
6. Deciding value of K.mp4	7.1 MB
7. KNN and Data Standardisation.mp4	16.0 MB
8. KNN Pros and Cons.mp4	56.4 MB
9. KNN using Sk-Learn.html	0.4 KB
/9. PROJECT - Face Recognition/
1. OpenCV - Working with Images.mp4	35.6 MB
2. OpenCV - Video Input from WebCam.mp4	35.9 MB
3. Object Detection using Haarcascades.mp4	83.5 MB
4. Face Detection in Images.mp4	82.5 MB
5. Face Detection in Live Video.mp4	51.7 MB
6. Face Recognition Project Intro.mp4	15.9 MB
7. Face Recognition 01 - Data Collection.mp4	207.6 MB
8. Face Recognition 02 - Loading Data.mp4	75.2 MB
9. Face Recognition 03 - Predictions using KNN.mp4	104.5 MB
Total files 214