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Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python

## (1) Start Here

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (2) Basics: What is linear classification? What's the relation to neural networks?

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (3) Solving for the optimal weights

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (4) Practical concerns

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (5) Checkpoint and applications: How to make sure you know your stuff

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (6) Project: Facial Expression Recognition

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

## (7) Appendix

- Introduction and Outline
- Review of the classification problem
- Introduction to the E-Commerce Course Project
- Linear Classification
- Biological inspiration – the neuron
- How do we calculate the output of a neuron / logistic classifier? – Theory
- How do we calculate the output of a neuron / logistic classifier? – Code
- E-Commerce Course Project: Pre-Processing the Data
- E-Commerce Course Project: Making Predictions
- A closed-form solution to the Bayes classifier
- What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.
- The cross-entropy error function – Theory
- The cross-entropy error function – Code
- Visualizing the linear discriminant / Bayes classifier / Gaussian clouds
- Maximizing the likelihood
- Updating the weights using gradient descent – Theory
- Updating the weights using gradient descent – Code
- E-Commerce Course Project: Training the Logistic Model
- Regularization – Theory
- Regularization – Code
- The donut problem
- The XOR problem
- BONUS: Sentiment Analysis
- BONUS: Where to get Udemy coupons and FREE deep learning material
- BONUS: Exercises + how to get good at this
- Facial Expression Recognition Problem Description
- The class imbalance problem
- Utilities walkthrough
- Facial Expression Recognition in Code
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Gradient Descent Tutorial

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