Udemy - Machine Learning Essentials (2023) - Master core ML concepts

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Udemy - Machine Learning Essentials (2023) - Master core ML concepts (Size: 15.9 GB)

	1. Bayes Theorem.mp4	87.3 MB
	1. Binary Classification Introduction.mp4	85.5 MB
	1. Biological Neural Network.mp4	28.4 MB
	1. Boosting Introduction.mp4	120.4 MB
	1. CODE - Decision Tree Node.mp4	61.2 MB
	1. Course Overview.mp4	49.6 MB
	1. Curse of Dimensionality.mp4	17 MB
	1. Decision Trees Introduction.mp4	78 MB
	1. Ensemble Learning.mp4	69.3 MB
	1. Introduction to Linear Regression.mp4	26.6 MB
	1. Introduction to PCA.mp4	63.4 MB
	1. Introduction.mp4	45 MB
	1. K-Means Algorithm.mp4	60.1 MB
	1. Multinomial Naive Bayes.mp4	141.1 MB
	1. OpenCV - Working with Images.mp4	34 MB
	1. Project Overview.mp4	122.4 MB
	1. Supervised Learning Introduction.mp4	78.3 MB
	1.1 Dataset Link.html	102.4 B
	1.1 titanic_train.csv	58.9 KB
	10. A Note about Shapes.mp4	30.1 MB
	10. CODE - Likelihood.mp4	166.5 MB
	10. CODE - Model Building.mp4	45.8 MB
	10. Code 01 - Data Generation.mp4	68.2 MB
	10. Code 05 - Training Loop.mp4	61.6 MB
	10. Code Repository.html	204.8 B
	10. Decision Trees for Regression.mp4	89.5 MB
	10. Predictions.mp4	30.2 MB
	11. CODE - Model Training and Testing.mp4	85 MB
	11. CODE - Prediction.mp4	71.4 MB
	11. Code 02 - Data Normalisation.mp4	170.9 MB
	11. Code 06 - Evaluation.mp4	50.9 MB
	11. Code 06 - Visualise Decision Boundary.mp4	43.1 MB
	11. Decision Tree Code - Sklearn.mp4	36.7 MB
	12. Code 03 - Train Test Split.mp4	89.3 MB
	12. Code 07 - Predictions & Accuracy.mp4	55.5 MB
	12. Implementing Naive Bayes - Sklearn.mp4	111.5 MB
	12. Linear Regression using Sk-Learn.mp4	35.4 MB
	13. Code 04 - Modelling.mp4	118.1 MB
	13. Logistic Regression using Sk-Learn.mp4	29.5 MB
	14. Code 05 - Predictions.mp4	54.1 MB
	14. Multiclass Classification One Vs Rest.mp4	72.4 MB
	15. Multiclass Classification One Vs One.mp4	33.5 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. A Neuron.mp4	34.1 MB
	2. Artificial Intelligence.mp4	48.6 MB
	2. Bagging Model.mp4	128.8 MB
	2. Boosting Intuition.mp4	133.5 MB
	2. CODE - Train Decision Tree.mp4	119.7 MB
	2. Code 01 - Data Prep.mp4	18.6 MB
	2. Conceptual Overview of PCA.mp4	140.9 MB
	2. Data Clearning.mp4	157.9 MB
	2. Decision Trees Example.mp4	137.4 MB
	2. Derivation of Bayes Theorem.mp4	74.8 MB
	2. Exploratory Data Analysis.mp4	103.3 MB
	2. Feature Selection Vs. Feature Extraction.mp4	15.1 MB
	2. Hypothesis.mp4	28.8 MB
	2. KNN Idea.mp4	34.5 MB
	2. Laplace Smoothing.mp4	91.5 MB
	2. Notation.mp4	105.3 MB
	2. OpenCV - Video Input from WebCam.mp4	34.2 MB
	2. Reading Images.mp4	24.2 MB
	2. Supervised Learning Example.mp4	198.1 MB
	2. The Data.mp4	48.6 MB
	3. Bayes Theorem Question.mp4	145 MB
	3. Boosting Mathematical Formulation.mp4	211.5 MB
	3. CODE - Assign Target Variable to Each Node.mp4	59.9 MB
	3. Code 02 - Init Centers.mp4	65.7 MB
	3. Data Visualisation.mp4	52.5 MB
	3. Entropy.mp4	118.4 MB
	3. Exploratory Data Analysis - II.mp4	79 MB
	3. Filter Method.mp4	23.5 MB
	3. Finding Clusters.mp4	53.9 MB
	3. How does a perceptron Learns.mp4	42.8 MB
	3. Hypothesis Function.mp4	272.3 MB
	3. Hypothesis.mp4	95.1 MB
	3. KNN Data Prep.mp4	29.2 MB
	3. Loss Function.mp4	33.2 MB
	3. Machine Learning.mp4	67 MB
	3. Maximising Variance.mp4	178 MB
	3. Multinomial Naive Bayes Example.mp4	179.2 MB
	3. Object Detection using Haarcascades.mp4	79.6 MB
	3. Structured Data.mp4	31.9 MB
	3. Unsupervised Learning.mp4	94 MB
	3. Why Bagging Helps.mp4	142.6 MB
	3. WordCloud.mp4	106.2 MB
	4. Bernoulli Naive Bayes.mp4	204.7 MB
	4. Binary Cross-Entropy Loss Function.mp4	90.8 MB
	4. CODE - Stopping Conditions.mp4	72.4 MB
	4. CODE Entropy.mp4	70.1 MB
	4. Code 03 - Assigning Points.mp4	75.6 MB
	4. Concept of Pseudo Residuals.mp4	152.8 MB
	4. Data Loading.mp4	42.8 MB
	4. Data Preparation for ML Model.mp4	83.4 MB
	4. Deep Learning.mp4	54.5 MB
	4. Dominant Color Swatches.mp4	39.7 MB
	4. Face Detection in Images.mp4	78.7 MB
	4. Finding relations.mp4	67.5 MB
	4. Gradient Descent Updates.mp4	52.8 MB
	4. KNN Algorithm Code.mp4	90.8 MB
	4. Loss Error Function.mp4	195.4 MB
	4. Minimising Distances.mp4	95.3 MB
	4. Naive Bayes Algorithm.mp4	80.7 MB
	4. Random Forest Algorithm.mp4	118.1 MB
	4. Text Featurization.mp4	44.2 MB
	4. Training & Gradient Updates.mp4	43.3 MB
	4. Wrapper Method.mp4	23 MB
	5. Bernoulli Naive Bayes Example.mp4	138.3 MB
	5. Bias Variance Tradeoff.mp4	127.4 MB
	5. CODE - Train Child Nodes.mp4	83.4 MB
	5. Code 01 - Data Prep.mp4	104.3 MB
	5. Code 04 - Updating Centroids.mp4	59.1 MB
	5. Computer Vision.mp4	43.1 MB
	5. Data Preparation.mp4	61.3 MB
	5. Data Preprocessing.mp4	50.3 MB
	5. Eigen Values & Eigen Vectors.mp4	48.5 MB
	5. Embedded Method.mp4	12.8 MB
	5. Euclidean and Manhattan Distance.mp4	14.9 MB
	5. Face Detection in Live Video.mp4	49.3 MB
	5. GBDT Algorithm.mp4	245.2 MB
	5. Gradient Update Rule.mp4	146.6 MB
	5. Handling Missing Values.mp4	94.8 MB
	5. Image in K-Colors.mp4	71 MB
	5. Information Gain.mp4	199.5 MB
	5. Model Building.mp4	52.1 MB
	5. Naive Bayes for Text Classification.mp4	160.7 MB
	5. Neural Networks.mp4	58 MB
	5. Training Idea.mp4	48.3 MB
	6. 3 Layer NN.mp4	28 MB
	6. Bias Variance Tradeoff.mp4	83.4 MB
	6. CODE - Explore Decision Tree Model.mp4	102.3 MB
	6. CODE Random Forest.mp4	115.6 MB
	6. CODE Split Data.mp4	135.8 MB
	6. Code 01 - Data Prep.mp4	79.9 MB
	6. Code 02 - Hypothesis.mp4	78.5 MB
	6. Code 05 - Visualizing K-Means & Results.mp4	81.8 MB
	6. Computing Likelihood.mp4	193.2 MB
	6. Deciding value of K.mp4	6.8 MB
	6. Decision Tree Model Building.mp4	77.8 MB
	6. Face Recognition Project Intro.mp4	15.2 MB
	6. Feature Selection - Code.mp4	63.6 MB
	6. Gradient Descent Optimisation.mp4	110.4 MB
	6. Model Architecture.mp4	33.2 MB
	6. Model Building.mp4	74.6 MB
	6. Model Evaluation.mp4	67.9 MB
	6. Natural Language Processing.mp4	64.4 MB
	6. PCA Summary.mp4	18.3 MB
	6.1 train.csv	119.5 KB
	7. Automatic Speech Recognition.mp4	100.7 MB
	7. CODE - Gradient Boosting Decision Trees.mp4	131.6 MB
	7. CODE - Prediction.mp4	116.4 MB
	7. CODE Information Gain.mp4	93.8 MB
	7. Code 02 - Hypothesis Logit Model.mp4	34.1 MB
	7. Code 03 - Loss Function.mp4	22.5 MB
	7. Face Recognition 01 - Data Collection.mp4	198 MB
	7. Gaussian Naive Bayes.mp4	109.3 MB
	7. Gradient Descent Code.mp4	271.3 MB
	7. Hyperparameter tuning.mp4	101.2 MB
	7. KNN and Data Standardisation.mp4	15.2 MB
	7. Softmax Function.mp4	18.4 MB
	7. Understanding Eigen Values.mp4	44.6 MB
	7. Understanding Golf Dataset.mp4	218.7 MB
	7. Visualize Decision Tree.mp4	92.6 MB
	7. Why Neural Nets.mp4	49.9 MB
	7.1 golf.csv	409.6 B
	8. CODE - Prior Probability.mp4	61.1 MB
	8. CODE - Variants of Naive Bayes.mp4	93.9 MB
	8. Code 03 - Binary Cross Entropy Loss.mp4	19.4 MB
	8. Code 04 - Gradient Computation.mp4	222.3 MB
	8. Construction of Decision Trees.mp4	66.4 MB
	8. Face Recognition 02 - Loading Data.mp4	71.7 MB
	8. Gradient Descent - for Linear Regression.mp4	51.8 MB
	8. Handling Numeric Features.mp4	110 MB
	8. KNN Pros and Cons.mp4	53.8 MB
	8. Model Training.mp4	17.3 MB
	8. PCA Code.mp4	50.6 MB
	8. Reinforcement Learning.mp4	43.9 MB
	8. Tensorflow Playground.mp4	88.7 MB
	8. XGBoost.mp4	119.3 MB
	9. Adaptive Boosting (AdaBoost).mp4	118.9 MB
	9. Bias Variance Tradeoff.mp4	58.9 MB
	9. CODE - Conditional Probability.mp4	108.1 MB
	9. CODE -Data Preparation.mp4	43.8 MB
	9. Choosing the right dimensions.mp4	45.4 MB
	9. Code 04 - Gradient Computation.mp4	45.2 MB
	9. Code 05 - Training Loop.mp4	86.7 MB
	9. Face Recognition 03 - Predictions using KNN.mp4	99.6 MB
	9. KNN using Sk-Learn.html	409.6 B
	9. Model evaluation.mp4	50.2 MB
	9. Pre-requisites.html	921.6 B
	9. Stopping Conditions.mp4	98.3 MB
	9. The Math of Training.mp4	105.3 MB
	[CourseClub.Me].url	102.4 B
	[FreeCourseSite.com].url	102.4 B
	[GigaCourse.Com].url	0 B
▲ 214 total files

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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
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Udemy - Machine Learning Essentials (2023) - Master core ML concepts

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