Introduction to Artificial Intelligence (AI) & Machine Learning | AI / ML JumpStart

Trivera Technologies LLC
Training overview
Professional Course
3 days
From 2,195 USD
Start dates
Online
2,195 USD
8/30/2021

Online
2,195 USD
10/4/2021

Online
2,195 USD
11/15/2021

Online
2,195 USD
12/13/2021

Course description

Introduction to Artificial Intelligence (AI) & Machine Learning | AI / ML JumpStart

Introduction to Artificial Intelligence (AI) & Machine Learning (AI & ML JumpStart) is a three-day, foundation-level, hands-on course that explores the fast-changing field of artificial intelligence (AI). programming, logic, search, machine learning, and natural language understanding. Students will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.

In this course, we will cut through the math and learn exactly how machine learning algorithms work. Although there is clearly a requirement for the students to have an aptitude for math, this course is about focusing on the algorithms that will be used to create machine learning models. Using clear explanations, simple pure Python code (no libraries!) and step-by-step labs, you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

This course presents a wide variety of related technologies, concepts and skills in a fast-paced, hands-on format, providing students with a solid foundation for understanding and getting a jumpstart into working with AI and machine learning. Each topic area presents a specific challenge area, current progress, and approaches to the presented problem. Attendees will exit the course with practical understanding of related 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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Who should attend?

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.

This course is ideally suited for a wide variety of technical learners who need an introduction to the core skills, concepts and technologies related to AI programming and machine learning. Attendees might include:

  • Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand data science techniques
  • Information Architects who want to gain expertise in Machine Learning algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in Data Science and machine learning
  • Experienced professionals who would like to harness machine learning in their fields to get more insight about customers

Training content

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

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

Matplotlib and Advanced Probability Concepts

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

Algorithm Overview

  • Data Prep
  • Linear Algorithms
    • Simple Linear Algorithms
    • Multivariate Linear Regression
    • Logistic Regression
    • Perceptrons
  • Non-Linear Algorithms
    • Classification Trees (CARTs)
    • Naive Bayes
    • k-Nearest Neighbors
  • Ensembles
    • Bootstrap Aggregation
    • Random Forest

Predictive Models

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

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
    • Clustering people based on income and age
    • Measuring entropy
    • Decision trees - Concepts
    • Decision trees - Predicting hiring decisions using Python
    • Ensemble learning
    • Support vector machine overview
    • Using SVM to cluster people by using scikit-learn

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

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

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

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

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

GUIs and REST

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

What the Future Holds

Costs

  • Price: $2,195.00
  • Discounted Price: $1,426.75

Why choose Trivera Technologies LLC?

Over 25 years of technology training expertise.

Robust portfolio of over 1,000 leading edge technology courses.

Guaranteed to run courses and flexible learning options.

About Trivera Technologies LLC

Trivera Technologies

Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...


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Trivera Technologies LLC

7862 West Irlo Bronson Highway
STE 626
Kissimmee FL 34747

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www.triveratech.com

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