Introduction to Machine Learning

Machine learning is a great field to get into; not only is it highly sought after by employers, it also helps you understand the world in a new way.

Most machine learning algorithms are based heavily in math, and are made possible by programming. Here are the basic things I would suggest picking up as you tackle machine learning

Quick overview of Machine learning

Basic terminology:

  • Most common settings: Supervised setting, Unsupervised setting, Semi-supervised setting, Reinforcement learning.
  • Most common problems: Classification (binary & multiclass), Regression, Clustering.
  • Preprocessing of data: Data normalization.

Optimization basics:

  • Terminology & Basic concepts: Convex optimization, Lagrangian, Primal-dual problems, Gradients & subgradients, ℓ1ℓ1 and ℓ2ℓ2 regularized objective functions.
  • Algorithms: Batch gradient descent & stochastic gradient descent, Coordinate gradient descent.
  • Implementation: Write code for stochastic gradient descent for a simple objective function, tune the step size, and get an intuition of the algorithm.

Classification:

  • Logistic Regression
  • Support vector machines: Geometric intuition, primal-dual formulations, notion of support vectors, kernel trick, understanding of hyperparameters, grid search.
  • Regression:
    1. Ridge regression
  • Clustering:
    1. k-means & Expectation-Maximization algorithm.
    2. Top-down and bottom-up hierarchical clustering.

Bayesian methods:

  • Basic terminology: Priors, posteriors, likelihood, maximum likelihood estimation and maximum-a-posteriori inference.
  • Gaussian Mixture Models
  • Latent Dirichlet Allocation: The generative model and basic idea of parameter estimation.

Neural Networks:

  • Basic terminology: Neuron, Activation function, Hidden layer.
  • Convolutional neural networks: Convolutional layer, pooling layer, Backpropagation.

Most popular Machine learning packages : numpy, scikit-learn, openCV, pandas

Most Important topics in Machine learning

  1. Linear regression
  2. Neural networks
  3. Back propogation algorithm
  4. Principal component analysis
  5. Support Vector Machines (SVM)
  6. Recommender Systems

Here are some examples:

  • Supervised Learning – Your email provider kindly places that sketchy email from the “Nigerian prince with $50,000 to deposit into an overseas bank account” into the spam folder.
  • Unsupervised Learning – Marketing firms “kindly” use hundreds of behavior and demographic indicators to segment customers into targeted offer groups.
  • Reinforcement Learning – A computer and camera within a self-driving car interact with the road and other cars to learn how to navigate a city.

Nilkanth Shet Shirodkar is the founder & CEO of Redicals. A Software Engineer and a passionate Web developer by heart. He just love – working with computers

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