Applied Machine Learning
Learn Python programming, write programs to implement machine learning in business. *The course requires an undergraduate knowledge of statistics, calculus, linear algebra, and probability.


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    Who is this course for?

    The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.

    Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Analytics taught by Emeritus. This will provide you with the programming knowledge required to do the assignments and application projects that are part of the Applied Machine Learning course.

    If you are looking to implement or lead a machine learning project or looking to incorporate machine learning capability in your software application, this course is appropriate for you. This is a programming course: you will be required to write code, but no prior programming knowledge is required.


    The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).

    *Assessment: Students will be given an assessment to test their math skills prior to commencement of the course. You can view sample questions by clicking here. To familiarize yourself with the topics of the assessment, refer to learning resources by clicking here.


    anticipated spending growth on AI and ML by 2021.



    expected wage growth for data scientists (vs. <2% average wage increase across all occupations).



    decrease in ‘click-to-ship’ time by Amazon using ML algorithm.



    Course Highlights

    240+ Faculty Video Lectures

    45 Quizzes / Assignments

    18 Moderated Discussion Boards

    20+ Q&A Sessions with Course Leaders

    12 Application Projects

    Includes Live Online Teaching

    Learning Journey

    Going beyond the theory, our approach invites participants into a conversation, where learning is facilitated by live subject matter experts and enriched by practitioners in the field of machine learning:

    • Define a model for your data and make the model learn.
    • Build regression models to predict an unknown output from a given set of inputs.
    • Create classification models to categorize datasets such as email messages as spam or non-spam.
    • Develop unsupervised models like topic models or recommender systems to extract hidden patterns from large amounts of data
    • Determine hidden parameters in data to improve the accuracy of your model’s predictions.
    • Create probabilistic data models to predict a range of possible outcomes that account for real-world risks and uncertainties.

    Program Topics

    Designed to teach you models and methods used in machine learning for real-world applications such as recommender systems and classification models, this 5-month program begins by building your foundational skills in Python, followed by the supervised and unsupervised learning techniques of applied machine learning.

    Part 1: Python for Data Analytics (Video content and delivery by Emeritus)

    • Module 1: Introduction to Data Science
      Learn the tools, skills, and common workflows of data scientists, identify bias in data science, and understand reproducibility, collaboration, and communication.
    • Module 2: Working with Data Types and Operators in Python
      Learn to code in Jupyter Notebooks and use various data types in Python including strings, integers, floats, Booleans, lists, and tuples.
    • Module 3: Writing Functions in Python
      Use functions and methods to perform analyses and identify correct syntax of functions along with uses of if statements and loops.
    • Module 4: Popular Data Science Packages in Python
      Perform basic analyses and define proper syntax using NumPy and Pandas, import and use packages in Jupyter Notebooks, and filter and slice data frames with Pandas.
    • Module 5: Advanced Functions
      Analyze data frames with lambda functions and construct complex functions with multiple parameters, nested functions, as well as functions with default arguments;
    • Module 6: Data Manipulation and Analysis with Pandas
      Use Pandas to build, extract, filter, and transform data frames as well as to manipulate and transform a dataset from Kaggle.
    • Module 7: Data Visualization with Matplotlib
      Draw insights from your data and communicate recommendations to others by creating data visualizations such as basic plots, source codes, and time series using Matplotlib.
    • Module 8: Random Variables and Statistical Inferences
      Examine probability as well as statistics in Python, differentiate between standard deviation and variance in a dataset, and collaborate on GitHub.
    • Module 9: Statistical Distributions and Hypothesis Testing
      Use normal distribution, t-distribution, and Bernoulli distribution in Python, test a model using P values as well as confidence intervals, and conduct basic hypothesis testing.
    • Module 10: Data Cleaning
      Examine and clean different types of data across data types to prepare it for analysis and place data into a Tidy Data format.
    • Module 11: Exploratory Data Analysis
      Learn new methods to explore and visualize your data with Matplotlib, define key characteristics of exploratory data analysis methods, and use them to describe data.
    • Module 12: Getting Started with Linear Algebra for Machine Learning
      Apply foundational linear algebra concepts in Python to build basic algorithms, solve equations with matrix operations, and perform advanced linear algebra procedures.

    Part 2: Applied Machine Learning (Video content from Columbia Engineering and delivery by Emeritus)

    Supervised Learning

    • Module 1: Regression
      Perform supervised learning with probabilistic models, linear regression, and maximum likelihood, use matrix operations to code least squares, and implement Ordinary Least Squares.
    • Module 2: Ridge Regression
      Analyze the bias-variance tradeoff through least squares and ridge regression, apply the Bayes rule to quantify uncertainty, and use regression analysis to solve a real-world problem.
    • Module 3: Bayesian Methods
      Understand “active learning,” the Lagrange multipliers tool, and the sparse regression model, identify MAP solutions, estimate covariants, and apply Bayesian linear regression.
    • Module 4: Classification Algorithms
      Deal with classification using nearest neighbors, K-nearest neighbors, and the Bayes classifier, predict outcomes with supervised learning classification, and use the perceptron algorithm.
    • Module 5: Logistic Regression, Kernel Methods, and Gaussian Processes
      Analyze logistic regression and its algorithm, learn about a kernel, kernelized perceptron, the Nadaraya-Watson regression model, and Gaussian processes.
    • Module 6: Support Vector Machines and Decision Trees
      Plot hyperplanes with the maximum margin method, modify SVM, code decision tree-based classifiers to make predictions about variables, and organize grid search hyperparameters.
    • Module 7: Boosting and K-Means Clustering
      Understand a bootstrap dataset and decision stump, learn about bagging and boosting techniques, identify characteristics of K-means tools, and use a label encoder.

    Unsupervised Learning

    • Module 8: Clustering Methods
      Code functions in Python implementing K-means, soft K-means, and Gaussian mixture model clustering to assign points and recalculate clusters.
    • Module 9: Recommendation Systems
      Employ recommendation systems to predict a user score, compare LDA vs. PMF vs. NMF, translate a mathematical algorithm into code, and use SVD to make item recommendations.
    • Module 10: Principal Component Analysis and Markov Models
      Use PCA to code a recommender system in Python, implement PCA using sklearn, prepare and scale data in preparation for PCA, and implement Markov chains using quantecon.
    • Module 11: Hidden Markov Models and Kalman Filtering
      Make predictions about variables using the Hidden Markov Model, differentiate between PCAs, Markov models, and Gaussian models, and use forward/backward algorithm to solve HMM.
    • Module 12: Association Analysis and Model Selection
      Apply association analysis to a dataset, model future stock prices using sequential methods, apply one-hot encoding to a data frame, and use the library Mlxtend for association analysis.

    Application Projects

    Movie Recommendation Engine

    You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.

    House Price Prediction

    You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.

    Human Activity Prediction

    You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.


    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.