FreeCourseSite com Udemy Deep Learning 2025 Neural Networks AI ChatGPT Prize

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/01. Welcome to the course!/

1. Introduction to Deep Learning From Historical Context to Modern Applications.mp4

1. Introduction to Deep Learning From Historical Context to Modern Applications.vtt

2. Get the Codes, Datasets and Slides Here.html

3. Prizes $$ for Learning.html

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/02. --------------------- Part 1 - Artificial Neural Networks ---------------------/

1. Welcome to Part 1 - Artificial Neural Networks.html

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/03. ANN Intuition/

1. What You'll Need for ANN.html

2. How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4

2. How Neural Networks Learn Gradient Descent and Backpropagation Explained.vtt

3. Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4

3. Understanding Neurons The Building Blocks of Artificial Neural Networks.vtt

4. Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4

4. Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.vtt

5. How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4

5. How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.vtt

6. How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4

6. How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.vtt

7. Mastering Gradient Descent Key to Efficient Neural Network Training.mp4

7. Mastering Gradient Descent Key to Efficient Neural Network Training.vtt

8. How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4

8. How to Use Stochastic Gradient Descent for Deep Learning Optimization.vtt

9. Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4

9. Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.vtt

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/04. Building an ANN/

1. Get the code and dataset ready.html

2. Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4

2. Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.vtt

3. Check out our free course on ANN for Regression.html

4. Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4

4. Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.vtt

5. Step 3 - Constructing an Artificial Neural Network Adding Input & Hidden Layers.mp4

5. Step 3 - Constructing an Artificial Neural Network Adding Input & Hidden Layers.vtt

6. Step 4 - Compile and Train Neural Network Optimizers, Loss Functions & Metrics.mp4

6. Step 4 - Compile and Train Neural Network Optimizers, Loss Functions & Metrics.vtt

7. Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4

7. Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.vtt

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/05. -------------------- Part 2 - Convolutional Neural Networks --------------------/

1. Welcome to Part 2 - Convolutional Neural Networks.html

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/06. CNN Intuition/

1. What You'll Need for CNN.html

10. Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4

10. Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.vtt

2. Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4

2. Understanding CNN Architecture From Convolution to Fully Connected Layers.vtt

3. How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4

3. How Do Convolutional Neural Networks Work Understanding CNN Architecture.vtt

4. How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4

4. How to Apply Convolution Filters in Neural Networks Feature Detection Explained.vtt

5. Rectified Linear Units (ReLU) in Deep Learning Optimizing CNN Performance.mp4

5. Rectified Linear Units (ReLU) in Deep Learning Optimizing CNN Performance.vtt

6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4

6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.vtt

7. How to Flatten Pooled Feature Maps in Convolutional Neural Networks (CNNs).mp4

7. How to Flatten Pooled Feature Maps in Convolutional Neural Networks (CNNs).vtt

8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).mp4

8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).vtt

9. CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4

9. CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.vtt

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/07. Building a CNN/

1. Get the code and dataset ready.html

2. Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4

2. Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.vtt

3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.mp4

3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.vtt

4. Step 3 - Building CNN Architecture Convolutional Layers & Max Pooling Explained.mp4

4. Step 3 - Building CNN Architecture Convolutional Layers & Max Pooling Explained.vtt

5. Step 4 - Train CNN for Image Classification Optimize with Keras & TensorFlow.mp4

5. Step 4 - Train CNN for Image Classification Optimize with Keras & TensorFlow.vtt

6. Step 5 - Deploying a CNN for Real-World Image Recognition.mp4

6. Step 5 - Deploying a CNN for Real-World Image Recognition.vtt

7. Develop an Image Recognition System Using Convolutional Neural Networks.mp4

7. Develop an Image Recognition System Using Convolutional Neural Networks.vtt

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/08. ---------------------- Part 3 - Recurrent Neural Networks ----------------------/

1. Welcome to Part 3 - Recurrent Neural Networks.html

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/09. RNN Intuition/

1. What You'll Need for RNN.html

2. How Do Recurrent Neural Networks (RNNs) Work Deep Learning Explained.mp4

2. How Do Recurrent Neural Networks (RNNs) Work Deep Learning Explained.vtt

3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.mp4

3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.vtt

4. Understanding the Vanishing Gradient Problem in Recurrent Neural Networks (RNNs).mp4

4. Understanding the Vanishing Gradient Problem in Recurrent Neural Networks (RNNs).vtt

5. Understanding Long Short-Term Memory (LSTM) Architecture for Deep Learning.mp4

5. Understanding Long Short-Term Memory (LSTM) Architecture for Deep Learning.vtt

6. How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4

6. How LSTMs Work in Practice Visualizing Neural Network Predictions.vtt

7. LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4

7. LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.vtt

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/10. Building a RNN/

1. Get the code and dataset ready.html

10. Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4

10. Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.vtt

11. Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4

11. Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.vtt

12. Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4

12. Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.vtt

13. Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4

13. Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.vtt

14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4

14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.vtt

15. Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4

15. Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.vtt

16. Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4

16. Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.vtt

2. Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4

2. Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.vtt

3. Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4

3. Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.vtt

4. Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4

4. Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.vtt

5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4

5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.vtt

6. Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4

6. Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.vtt

7. Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4

7. Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.vtt

8. Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4

8. Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.vtt

9. Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4

9. Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.vtt

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/11. Evaluating and Improving the RNN/

1. Evaluating the RNN.html

2. Improving the RNN.html

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/12. ------------------------ Part 4 - Self Organizing Maps ------------------------/

1. Welcome to Part 4 - Self Organizing Maps.html

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/13. SOMs Intuition/

1. How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4

1. How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.vtt

10. How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4

10. How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.vtt

2. Self-Organizing Maps (SOM) Unsupervised Deep Learning for Dimensionality Reduct.mp4

2. Self-Organizing Maps (SOM) Unsupervised Deep Learning for Dimensionality Reduct.vtt

3. Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4

3. Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.vtt

4. Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4

4. Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.vtt

5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.mp4

5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.vtt

6. How to Create a Self-Organizing Map (SOM) in DL Step-by-Step Tutorial.mp4

6. How to Create a Self-Organizing Map (SOM) in DL Step-by-Step Tutorial.vtt

7. Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4

7. Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.vtt

8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4

8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.vtt

9. K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4

9. K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.vtt

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/14. Building a SOM/

1. Get the code and dataset ready.html

2. Step 1 - Implementing Self-Organizing Maps (SOMs) for Fraud Detection in Python.mp4

2. Step 1 - Implementing Self-Organizing Maps (SOMs) for Fraud Detection in Python.vtt

3. Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4

3. Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.vtt

4. Step 3 - SOM Visualization Techniques Colorbar & Markers for Outlier Detection.mp4

4. Step 3 - SOM Visualization Techniques Colorbar & Markers for Outlier Detection.vtt

5. Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4

5. Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.vtt

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/15. Mega Case Study/

1. Get the code and dataset ready.html

2. Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4

2. Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.vtt

3. Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4

3. Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.vtt

4. Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4

4. Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.vtt

5. Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4

5. Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.vtt

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/16. ------------------------- Part 5 - Boltzmann Machines -------------------------/

1. Welcome to Part 5 - Boltzmann Machines.html

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/17. Boltzmann Machine Intuition/

1. Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4

1. Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.vtt

2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4

2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.vtt

3. Deep Learning Fundamentals Energy-Based Models & Their Role in Neural Networks.mp4

3. Deep Learning Fundamentals Energy-Based Models & Their Role in Neural Networks.vtt

4. How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4

4. How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.vtt

5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4

5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.vtt

6. How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4

6. How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.vtt

7. Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4

7. Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.vtt

8. Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4

8. Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.vtt

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/18. Building a Boltzmann Machine/

1. Get the code and dataset ready.html

10. Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4

10. Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.vtt

11. Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4

11. Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.vtt

12. Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4

12. Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.vtt

13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4

13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.vtt

14. Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4

14. Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.vtt

15. Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4

15. Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.vtt

16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4

16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.vtt

17. Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4

17. Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.vtt

18. Evaluating the Boltzmann Machine.html

2. Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4

2. Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.vtt

3. Same Data Preprocessing in Parts 5 and 6.html

4. Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4

4. Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.vtt

5. Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4

5. Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.vtt

6. Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4

6. Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.vtt

7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.mp4

7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.vtt

8. Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4

8. Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.vtt

9. Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4

9. Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.vtt

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/19. ---------------------------- Part 6 - AutoEncoders ----------------------------/

1. Welcome to Part 6 - AutoEncoders.html

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/20. AutoEncoders Intuition/

1. Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4

1. Deep Learning Autoencoders Types, Architecture, and Training Explained.vtt

10. Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4

10. Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.vtt

2. Autoencoders in Machine Learning Applications and Architecture Overview.mp4

2. Autoencoders in Machine Learning Applications and Architecture Overview.vtt

3. Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4

3. Autoencoder Bias in Deep Learning Improving Neural Network Performance.vtt

4. How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4

4. How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.vtt

5. How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4

5. How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.vtt

6. Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4

6. Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.vtt

7. Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4

7. Denoising Autoencoders Deep Learning Regularization Technique Explained.vtt

8. What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4

8. What are Contractive Autoencoders Deep Learning Regularization Techniques.vtt

9. What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4

9. What are Stacked Autoencoders in Deep Learning Architecture and Applications.vtt

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/21. Building an AutoEncoder/

1. Get the code and dataset ready.html

10. Step 7 - Python Autoencoder Tutorial Implementing Activation Functions & Layers.mp4

10. Step 7 - Python Autoencoder Tutorial Implementing Activation Functions & Layers.vtt

11. Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4

11. Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.vtt

12. Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4

12. Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.vtt

13. Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4

13. Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.vtt

14. Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4

14. Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.vtt

15. THANK YOU Video.mp4

15. THANK YOU Video.vtt

2. Same Data Preprocessing in Parts 5 and 6.html

3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4

3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.vtt

4. Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4

4. Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.vtt

5. Step 3 - Preparing Data for Recommendation Systems User & Movie Count in Python.mp4

5. Step 3 - Preparing Data for Recommendation Systems User & Movie Count in Python.vtt

6. Homework Challenge - Coding Exercise.html

7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4

7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.vtt

8. Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4

8. Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.vtt

9. Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4

9. Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.vtt

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/22. ------------------- Annex - Get the Machine Learning Basics -------------------/

1. Annex - Get the Machine Learning Basics.html

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/23. Regression & Classification Intuition/

1. What You Need for Regression & Classification.html

2. Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4

2. Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.vtt

3. Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4

3. Linear Regression Explained Finding the Best Fitting Line for Data Analysis.vtt

4. Multiple Linear Regression - Understanding Dependent & Independent Variables.mp4

4. Multiple Linear Regression - Understanding Dependent & Independent Variables.vtt

5. Understanding Logistic Regression Intuition and Probability in Classification.mp4

5. Understanding Logistic Regression Intuition and Probability in Classification.vtt

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/24. Data Preprocessing/

1. Data Preprocessing.html

2. How to Scale Features in Machine Learning Normalization vs Standardization.mp4

2. How to Scale Features in Machine Learning Normalization vs Standardization.vtt

3. Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4

3. Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.vtt

4. Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4

4. Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.vtt

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/25. Data Preprocessing in Python/

1. Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4

1. Step 1 - Data Preprocessing in Python Essential Tools for ML Models.vtt

10. Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4

10. Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.vtt

11. Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.mp4

11. Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.vtt

12. Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4

12. Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.vtt

13. Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4

13. Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.vtt

14. Step 2 - Split Data into Train & Test Sets with Scikit-learn's train_test_split.mp4

14. Step 2 - Split Data into Train & Test Sets with Scikit-learn's train_test_split.vtt

15. Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4

15. Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.vtt

16. Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4

16. Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.vtt

17. Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4

17. Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.vtt

18. Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4

18. Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.vtt

19. Step 4 - How to Apply Feature Scaling to Training & Test Sets in ML.mp4

19. Step 4 - How to Apply Feature Scaling to Training & Test Sets in ML.vtt

2. Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4

2. Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.vtt

3. Step 1 - Importing Essential Python Libraries for Data Preprocessing & Analysis.mp4

3. Step 1 - Importing Essential Python Libraries for Data Preprocessing & Analysis.vtt

4. Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4

4. Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.vtt

5. Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4

5. Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.vtt

6. Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4

6. Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.vtt

7. For Python learners, summary of Object-oriented programming classes & objects.html

8. Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4

8. Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.vtt

9. Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4

9. Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.vtt

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/26. Logistic Regression/

1. Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4

1. Understanding the Logistic Regression Equation A Step-by-Step Guide.vtt

10. Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4

10. Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.vtt

11. Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4

11. Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.vtt

12. Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4

12. Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.vtt

13. Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4

13. Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.vtt

14. Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4

14. Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.vtt

15. Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4

15. Step 7b - Visualizing Logistic Regression Interpreting Classification Results.vtt

16. Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4

16. Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.vtt

17. 2023-10-27_09-33-51-79d68c341b6d6d2ca73c27f9e2697b29.png

17. 2023-10-27_09-33-51-7c6b56531ac00d135d9ae1b931c61936.png

17. Logistic Regression in Python - Step 7 (Colour-blind friendly image).html

18. Machine Learning Regression and Classification EXTRA.html

19. EXTRA CONTENT Logistic Regression Practical Case Study.html

2. How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4

2. How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.vtt

3. Step 1a - Machine Learning Classification Logistic Regression in Python.mp4

3. Step 1a - Machine Learning Classification Logistic Regression in Python.vtt

4. Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4

4. Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.vtt

5. Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4

5. Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.vtt

6. Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4

6. Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.vtt

7. Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4

7. Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.vtt

8. Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4

8. Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.vtt

9. Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4

9. Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.vtt

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/27. Congratulations!! Don't forget your Prize )/

1. Find Your Career Path!.html

2. 2025-01-13_07-39-00-d4903ef226a918fe48f699f14bb6e25e.png

2. Bonus How To UNLOCK Top Salaries (Live Training).html

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