Deep Learning Jan 2025 udemy

/07 - Building a CNN/

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

001 Get the code and dataset ready.html

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

002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.srt

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

003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.srt

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

004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.srt

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

005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.srt

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

006 Step 5 - Deploying a CNN for Real-World Image Recognition.srt

007 Develop an Image Recognition System Using Convolutional Neural Networks.srt

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Course updated jan 2025.txt

/01 - Welcome to the course!/

001 Welcome Challenge!.html

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

002 Introduction to Deep Learning From Historical Context to Modern Applications.srt

003 Get the codes, datasets and slides here.html

004 EXTRA Use ChatGPT to Boost your Deep Learning Skills.html

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

001 Welcome to Part 1 - Artificial Neural Networks.html

/03 - ANN Intuition/

001 What You'll Need for ANN.html

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

002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.srt

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

003 Understanding Neurons The Building Blocks of Artificial Neural Networks.srt

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

004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.srt

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

005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.srt

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

006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.srt

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

007 Mastering Gradient Descent Key to Efficient Neural Network Training.srt

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

008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.srt

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

009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.srt

/04 - Building an ANN/

001 Get the code and dataset ready.html

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

002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.srt

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

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

004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.srt

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

005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.srt

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

006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.srt

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

007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.srt

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

001 Welcome to Part 2 - Convolutional Neural Networks.html

/06 - CNN Intuition/

001 What You'll Need for CNN.html

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

002 Understanding CNN Architecture From Convolution to Fully Connected Layers.srt

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

003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.srt

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

004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.srt

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

005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.srt

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

006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.srt

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

007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).srt

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

008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).srt

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

009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.srt

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

010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.srt

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

001 Welcome to Part 3 - Recurrent Neural Networks.html

/09 - RNN Intuition/

001 What You'll Need for RNN.html

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

002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.srt

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

003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.srt

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

004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).srt

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

005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.srt

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

006 How LSTMs Work in Practice Visualizing Neural Network Predictions.srt

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

007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.srt

/10 - Building a RNN/

001 Get the code and dataset ready.html

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

002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.srt

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

003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.srt

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

004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.srt

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

005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.srt

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

006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.srt

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

007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.srt

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

008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.srt

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

009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.srt

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

010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.srt

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

011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.srt

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

012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.srt

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

013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.srt

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

014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.srt

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

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

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

016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.srt

/11 - Evaluating and Improving the RNN/

001 Evaluating the RNN.html

002 Improving the RNN.html

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

001 Welcome to Part 4 - Self Organizing Maps.html

/13 - SOMs Intuition/

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

001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.srt

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

002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.srt

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

003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.srt

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

004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.srt

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

005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.srt

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

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

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

007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.srt

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

008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.srt

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

009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.srt

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

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

/14 - Building a SOM/

001 Get the code and dataset ready.html

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

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

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

003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.srt

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

004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.srt

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

005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.srt

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/15 - Mega Case Study/

001 Get the code and dataset ready.html

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

002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.srt

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

003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.srt

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

004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.srt

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

005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.srt

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

001 Welcome to Part 5 - Boltzmann Machines.html

/17 - Boltzmann Machine Intuition/

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

001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.srt

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

002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.srt

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

003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.srt

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

004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.srt

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

005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.srt

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

006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.srt

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

007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.srt

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

008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.srt

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/18 - Building a Boltzmann Machine/

001 Get the code and dataset ready.html

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

002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.srt

003 Same Data Preprocessing in Parts 5 and 6.html

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

004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.srt

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

005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.srt

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

006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.srt

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

007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.srt

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

008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.srt

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

009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.srt

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

010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.srt

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

011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.srt

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

012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.srt

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

013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.srt

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

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

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

015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.srt

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

016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.srt

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

017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.srt

018 Evaluating the Boltzmann Machine.html

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

001 Welcome to Part 6 - AutoEncoders.html

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/20 - AutoEncoders Intuition/

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

001 Deep Learning Autoencoders Types, Architecture, and Training Explained.srt

002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4

002 Autoencoders in Machine Learning Applications and Architecture Overview.srt

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

003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.srt

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

004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.srt

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

005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.srt

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

006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.srt

007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4

007 Denoising Autoencoders Deep Learning Regularization Technique Explained.srt

008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4

008 What are Contractive Autoencoders Deep Learning Regularization Techniques.srt

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

009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.srt

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

010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.srt

/21 - Building an AutoEncoder/

001 Get the code and dataset ready.html

002 Same Data Preprocessing in Parts 5 and 6.html

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

003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.srt

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

004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.srt

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

005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.srt

006 Homework Challenge - Coding Exercise.html

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

007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.srt

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

008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.srt

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

009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.srt

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

010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.srt

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

011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.srt

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

012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.srt

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

013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.srt

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

014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.srt

015 THANK YOU Video.mp4

015 THANK YOU Video.srt

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

001 Annex - Get the Machine Learning Basics.html

/23 - Regression & Classification Intuition/

001 What You Need for Regression & Classification.html

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

002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.srt

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

003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.srt

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

004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.srt

005 Understanding Logistic Regression Intuition and Probability in Classification.mp4

005 Understanding Logistic Regression Intuition and Probability in Classification.srt

/24 - Data Preprocessing/

001 Data Preprocessing.html

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

002 How to Scale Features in Machine Learning Normalization vs Standardization.srt

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

003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.srt

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

004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.srt

/25 - Data Preprocessing in Python/

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

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

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

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

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

003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.srt

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

004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.srt

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

005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.srt

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

006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.srt

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

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

008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.srt

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

009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.srt

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

010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.srt

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

011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.srt

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

012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt

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

013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.srt

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

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

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

015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.srt

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

016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.srt

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

017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.srt

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

018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.srt

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

019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.srt

/26 - Logistic Regression/

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

001 Understanding the Logistic Regression Equation A Step-by-Step Guide.srt

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

002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.srt

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

003 Step 1a - Machine Learning Classification Logistic Regression in Python.srt

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

004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.srt

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

005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.srt

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

006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.srt

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

007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.srt

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

008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.srt

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

009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.srt

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

010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.srt

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

011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.srt

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

012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.srt

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

013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.srt

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

014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.srt

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

015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.srt

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

016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.srt

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

018 Machine Learning Regression and Classification EXTRA.html

019 EXTRA CONTENT Logistic Regression Practical Case Study.html

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

001 Huge Congrats for completing the challenge!.html

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

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