Introduction to Artificial Intelligence (AI) and Machine Learning (ML)

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HQ7J3S Introduction to Artificial Intelligence (AI) and Machine Learning (ML)

This introduction-level hands-on course explores the field of artificial intelligence (AI), programming, logic, search, machine learning (ML), and natural language understanding. You learn current AI and ML methods, tools, techniques, and their application to computational problems. In this course, we cut through the math so you learn exactly how machine learning algorithms work. We focus on the algorithms used to create machine learning models. Using clear explanations, simple Python code (no libraries), and step-by-step labs, you discover how to load and prepare data, evaluate your models, and implement a suite of linear and nonlinear algorithms along with assembling algorithms from scratch. You also learn about algorithm applicability along with their limitations and practical use cases.

This course presents a wide variety of related technologies, concepts, and skills in a fast-paced, hands-on format. This provides a solid foundation for understanding and a jumpstart into working with artificial intelligence and machine learning.

This course is 50% hands-on labs and 50% lecture and includes engaging instruction, demos, group discussions, labs, and project work.

Business analysts, data analysts, developers, administrators, architects,
managers, and others new to AI and ML who want to understand core skills and put them into practice

Before attending this course, you should have:
• Basic Python skills
• A grounding in enterprise computing
• Familiarity with enterprise IT
• A general (high level) understanding of systems architecture
• Knowledge of business drivers that might be able to take advantage of AI
• Good foundational mathematics in linear algebra and probability
• Basic Linux skills
• Familiarity with command line options such as ls, cd, cp, and su

During this course, you will explore:
• Getting started with Python and 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

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
• Non-linear algorithms
• Ensembles

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

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
• A/B test gotchas

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

    Contact Us for more details