This hands-on, foundational course explores the fast-changing field of artificial intelligence (AI) programming, logic, search, machine learning, and natural language understanding. You will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. You will leave this course with a practical understanding of core skills, methods and algorithms, and be prepared for continued learning in next-level, more advanced follow on courses that dive deeper into specific skillsets or tools.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This "skills-centric" course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations; practical application of these methods in a machine learning environment; and practical use cases and limitations of algorithms.
Working in a hands-on lab environment led by our expert instructor, attendees will explore:
Getting Started with Python & Jupyter
Statistics and Probability Refresher, and Python Practice
Matplotlib and Advanced Probability Concepts
Algorithm Overview
Predictive Models
Applied Machine Learning
Recommender Systems
Dealing with Data in the Real World
Machine Learning on Big Data (with Apache Spark)
Testing and Experimental Design
GUIs and REST: Build a UI & REST API for your Models

1

  • Getting Started

  • Installing a Python Data Science Environment
    Using and understanding IPython (Jupyter) Notebooks
    Python basics - Part 1
    Understanding Python code
    Importing modules
    Python basics - Part 2
    Running Python scripts
2

  • Statistics and Probability Refresher, and Python Practice

  • Types of data
    Mean, median, and mode
    Using mean, median, and mode in Python
    Standard deviation and variance
    Probability density function and probability mass function
    Types of data distributions
    Percentiles and moments
3

  • Matplotlib and Advanced Probability Concepts

  • A crash course in Matplotlib
    Covariance and correlation
    Conditional probability
    Bayes' theore
4

  • Algorithm Overview

  • Data Prep
    Linear Algorithms
    Non-Linear Algorithms
    Ensembles
5

  • Predictive Models

  • Linear regression
    Polynomial regression
    Multivariate regression and predicting car prices
    Multi-level models
6

  • Applied Machine Learning with Python

  • Machine learning and train/test
    Using train/test to prevent overfitting of a polynomial regression
    Bayesian methods - Concepts
    Implementing a spam classifier with Naïve Bayes
    K-Means clustering
7

  • Recommender Systems

  • What are recommender systems?
    Item-based collaborative filtering
    How item-based collaborative filtering works?
    Finding movie similarities
    Improving the results of movie similarities
    Making movie recommendations to people
    Improving the recommendation results
8

  • More Applied Machine Learning Techniques

  • K-nearest neighbors - concepts
    Using KNN to predict a rating for a movie
    Dimensionality reduction and principal component analysis
    A PCA example with the Iris dataset
    Data warehousing overview
    Reinforcement learning
9

  • Dealing with Data in the Real World

  • Bias/variance trade-off
    K-fold cross-validation to avoid overfitting
    Data cleaning and normalization
    Cleaning web log data
    Normalizing numerical data
    Detecting outliers
10

  • Apache Spark - Machine Learning on Big Data

  • Installing Spark
    Spark introduction
    Spark and Resilient Distributed Datasets (RDD)
    Introducing MLlib
    Decision Trees in Spark with MLlib
    K-Means Clustering in Spark
    TF-IDF
    Searching Wikipedia with Spark MLlib
    Using the Spark 2.0 DataFrame API for MLlib
11

  • Testing and Experimental Design

  • A/B testing concepts
    T-test and p-value
    Measuring t-statistics and p-values using Python
    Determining how long to run an experiment for
    A/B test gotchas
12

  • GUIs and REST

  • Build a UI for your Models
    Build a REST API for your Models
13

  • What the Future Holds

Audience

Students attending this class should have a grounding in Enterprise computing. Students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.

Language

English

Prerequisites

There are no stated prerequisites for this course. Please check with your representative for details.

$1,995

Length: 3.0 days (24 hours)

Level:

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