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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

Visit Deep Learning Prerequisites: Logistic Regression in Python (Udemy) to read more...

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