Free Certification Course Title: Mathematics & Statistics of Machine Learning & Data Science

Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning

Mathematics & Statistics of Machine Learning & Data Science


  • Just some high-school math and statistics (optional)

What you’ll learn:

  • Mathematics and Statistics behind Machine Learning
  • Mathematics and Statistics behind Neural Networks
  • Mathematics and Statistics behind Deep Learning
  • Probably Approximately Correct (PAC) Learning
  • Vapnik-Chervonenkis (VC) Dimension
  • Gaussian (Normal) Density
  • Multivariate Normal Distribution
  • Linear Discriminant Analysis
  • Expectation-Maximization Algorithm
  • Supervised Learning after Clustering
  • Nonparametric Density Estimation
  • k-Nearest Neighbor Estimator
  • Condensed Nearest Neighbor
  • Learning Vector Quantization
  • Multiclass Kernel Machines

Who this course is for:

  • People who want to start their career in Machine Learning
  • People who want to learn Machine Learning
  • People who want to learn Deep Learning

Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

  • Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
  • Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
    The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
  • Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
  • Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
    Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.
  • Certificate of completion

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