Enterprise Machine Learning

Solve real-world problems in public open data and private industry specific domains.

  • 08
    Jun
    2 days, Sat 9:00 AM - Sun 6:00 PM
    • $3,500.00 excl. Tax
  • 27
    Jul
    3 days, Sat 9:00 AM - Mon 6:00 PM
    • $3,500.00 excl. Tax
  • 12
    Oct
    6 weeks, Sat 9:30 AM - Sun 1:30 PM
    • $3,500.00 excl. Tax
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    Description

    The 6-week intermediate level hands on course brings together the interdisciplinary fields of data science and machine learning, which is at the intersection of computer science, statistics, and business. You will begin with using various techniques and tools to help you acquire, clean, parse, and filter your data.

     

    A significant portion of the course will be a hands-on approach to the fundamental modeling techniques based on Statistics, Machine Learning and Neural Networks/Deep Learning algorithms that enable you to build robust predictive models about real-world data and test their validity. You will also gain practice communicating your results and insights about how to build systems that are more intelligent using the data that you have gathered.

     

    Hands on exercises on building diverse models including pricing, risk, recommenders, image and text classification gives clear comprehension of selecting the right strategy given the business need..

    As Machine Learning practices in enterprises increasingly migrate to the Cloud, this course brings the latest of Cloud innovations from companies including Microsoft(Azure), AWS, GCP. Get hands as well as additional training material to prepare for Machine Learning certifications provided by Cloud providers.

    Capstone project helps crystallize concepts by building a Data Product that you can show case on Github to prospective employers or senior leadership within your own organization.

    Key skills & technologies

    • Acquire, clean, and parse enterprise scale data sets using Python.
    • Gain knowledge on choosing the appropriate modeling technique to apply to your data
    • Apply Statistics and Deep Learning concepts to create and validate predictions about your data at Enterprise Scale.
    • Communicate your results to an appropriate audience with compelling and interactive visualizations
    • Enable enterprises migrate machine learning models into Cloud

    Course Plan

    WEEK 0 (Prerequisites: Self Paced): DATA SCIENCE FOUNDATIONS – STATISTICS, PYTHON AND SQL; EXPLORATORY DATA ANALYSIS

    Build on Descriptive Statistics, Probability Theory, and explore distributions using python and enterprise visualization tools (E.g.: Tableau/Power BI).

     

    WEEK 1: MACHINE LEARNING, BIAS-VARIANCE AND MODEL EVALUATION

    Model Selection, Evaluation and Diagnostics

     

    WEEK 2: REGRESSION AND CLASSIFICATION

    Building Regression and Classification models using Statistical and Neural Network techniques. You will learn Model Evaluation and Model Interpretation techniques to help decide whether to use Statistical approach Vs Neural Networks.

     

    WEEK 3: NATURAL LANGUAGE PROCESSING

    Extract features from text (convert text into numbers & vectors) and build Sentiment Analysis using Naïve Bayes Classifiers and more advanced Neural Network techniques including Long Short-term Memory.

     

    WEEK 4: DECISION TREES AND ENSEMBLES, CLUSTERING

    Supervised Learning beyond classical models and Unsupervised learning with K-means

     

    WEEK 5,6: BIG DATA & CLOUD CERTIFICATION PREPARATION

    Scaling data analysis with large datasets using Spark ML, Hadoop ecosystem in the Cloud (E.g.: Azure/AWS)

    Is it for me?

    Do I need any programming experience to attend?

    Yes – you will need previous experience in Python language.

    What will I be doing?

    In 6-weeks you will learn the foundations of data science, Supervised and Unsupervised learning techniques applied to Structured, Semi Structured and Unstructured data (Text, Images) and Time-series data both locally and on big data clusters.

    What age is this for?

    This course is suitable for working professionals.

    Do I need a computer?

    It is best if you bring your own laptop with 8GB RAM and 100-150GB free space on your hard drive. We moved all our learning to the Cloud (Digitalocean, Azure, AWS and Google)

    What’s the experience like?

    The course is 70% hands-on in-class modeling exercises combined with 30% lectures to explain the concepts. There are four homework assignments to reinforce the learning in the class and a final project presentation to be presented in front of a Divergence Datascience meetup audience. Our instructors are always there to help you if you get stuck.