A Journey to ML (II)

The moment I took Andrew Ng’s course

If you are starting in Machine Learning, it won’t take long before someone points you to Andrew Ng’s course in Coursera.

But… Who is Andrew Ng? He is the co-founder of Coursera. Only because of this, you should know him. But he is also one of those ten names in Machine Learning that you have to know; and his works lead to ROS; and he is a prolific scientist writer; and he started Google Brain; and lead Baidu’s efforts in AI

If all of that is not enough, his course from Stanford about Machine Learning is one of the most successful MOOCs in Coursera.

Do you know what I learned in that course? Prepare yourself for an avalanche of terms because that is what is coming!

  • Linear Regression
  • Multivariate Linear Regression
  • Normal Equation
  • Logistic Regression
  • Regularization
  • Neural Networks
  • SVM
  • Kernels and Gaussian Kernels
  • PCA
  • K-mean Algorithm
  • Collaborative Filtering
  • Feature Learning
  • Online Learning

Do you want more?

  • Batch Gradient Descent
  • Feature Scaling
  • Learning Rate
  • Unrolling parameters
  • Gradient Checking
  • Random Initialisation
  • Model Selection
  • Bias vs Variance
  • Learning Curves
  • Precision/Recall/F1
  • Kernels and Similarity
  • Dimensionality Reduction
  • Anomaly Detection
  • Mean Normalisation
  • Stochastic Gradient Descent
  • Mini-batch Gradient Descent

And more yet? Here you are a few more to chew!

  • Sigmoid function
  • Overfitting
  • Underfitting
  • Backpropagation
  • Large Margin Classifier
  • One-vs-All Classification
  • Eigenvectors
  • Low Rank Matrix Factorization
  • Symmetry breaking

With all this knowledge, you can start to feel you know a bit of what Machine Learning is about

Add to all these terms, definitions and maths a good bunch of exercises and you will know why Ng’s course is worth taking

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