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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Imperial College Business School, London

Meet the faculty and get an overview of the programme, including certificate requirements, the learning platform and support resources. Receive an introduction to Python and learn about the capstone competition

Become familiar with the fundamental components of and approaches to machine learning problems and ways to classify problems along the major dividing lines of the ML landscape
Understand how to differentiate ML from statistics
Examine real-world applications of machine learning across a variety of industries

Learn how to calculate absolute, conditional and total probabilities and understand how to classify independent versus dependent events
Discover how to run simulations using random number generating libraries in Python and Numpy
Explore the difference between discrete and continuous random variables and learn how to compute probabilities and values related to binomial distribution
Understand how to conduct maximum likelihood estimations using existing data

Understand how to detect outliers and calculate regression or correlation coefficients fora data set
Examine the potential consequences of eliminating outliers from a data set
Explore the validity of statements about data sets and relationships among data
Learn how to compute confidence interval

Recognise when it is feasible to draw meaningful conclusions from data
Learn how to calculate the probability of selecting the correct model from a set of sample data
Examine key components of generalisation bounds
Learn how to estimate the fit of a selected model on a new data set

Learn how to use performance measures to evaluate regression problems with a numerical output variable
Understand how to apply a confusion matrix to evaluate classification problems with a categorical output variable
Examine challenges that can be addressed by machine learning competitions across a variety of industries

Explore effective applications of oversampling to a machine learning problem
Understand how to apply oversampling to classification problems
Learn how to estimate the performance of a given predictor using the k-fold cross-validation algorithm

Learn how to calculate distance functions for k-nearest neighbour methods and understand how to apply normalisation methods to scale data sets
Learn how to use validation and test sets to predict and select the value for regression and classification problems
Explore real-life applications of k-nearest neighbour methods that take into consideration the advantages and shortcomings of such methods

Discover how a decision tree makes predictions and understand the difference between howto measure purity in categorical models with entropy and the Gini Index
Explore how a computer constructs a decision tree and examine strategies for pruning a classification tree
Recognize the differences between constructing regression and classification trees

Learn how to select the most appropriate tree depth for making a prediction
Become familiar with the concepts of interpretability, fairness and non-discrimination
Explore the functionality of the k-nearest neighbour and decision tree methods

Understand key components of Bayes’ theorem and learn how to use Bayes' theorem to calculate conditional probabilities
Recognize when it is better to predict a probability instead of an actual value
Explore real-life applications of the Naïve Bayes theorem

Understand the use of surrogate models as well as their applications and pitfalls
Explore machine learning algorithm parameters and the most common surrogate methodsused for tuning
Examine trade-offs between exploration and exploitation in Bayesian optimisation
Discover when continued parameter tuning is no longer worthwhile

Compare logistic and linear regression to understand the categorical output and best use cases of logistic regression for binary classification
Using a real-life data set, apply the maximum likelihood method to fit a logistic regression
Select an approach for the final code base of your capstone project that aligns with your professional goals

Understand the concepts of linear separation through hyperplanes
Learn about hard-margin and soft-margin support vector machines
Gain knowledge of the kernel trick and discover how support-vector machines can be applied to classification problems with more than one outcome

Delve deeper into calculating the direction and location of principal components
Learn how to determine the number of principal components to use

Become familiar with the history of deep learning and discover the five building blocks of deep learning

Understand the function approximation technique
Learn how the backpropagation algorithm is used to train a neural network using thechain rule technique
Understand the backward pass component of backpropagation
Discover how to optimise parameters using stochastic gradient descent

Understand how to fine-tune machine learning models using a hyperparameter search
Discover how to use regularisation techniques to make adjustments that will improve model performance
Receive an introduction to using PyTorch