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Machine Learning: Understanding Solutions and Developing Applications

List Tuition: $250.00

Course Overview

Duration: 20+ Hours
Access: 1 Year
Slide Deck Included: 549 pages
Format: Self-Paced Learning

Machine Learning is a discipline of Artificial Intelligence focused on teaching machines to gather and apply knowledge. We're already beginning to see the profound effects that educated intelligence systems are having on our world. The decade ahead promises to be one in which we will see an explosive growth in Machine Learning applications, techniques, solutions, and platforms.

In this training class we will focus on learning the core concepts of Machine Learning and getting hands-on with the latest technologies to learn how to create your own solutions!

This class will cover:

Chapter 1: Getting Started with Machine Learning
Chapter 2: Data Sets and Pandas
Chapter 3: Python Visualization
Chapter 4: Intro Scikit-Learn
Chapter 5: SVM Classification
Chapter 6: Classification NB (ROC and Naive Bayes)
Chapter 7: Decision Trees
Chapter 8: PCA
Chapter 9: Deep Learning Playground
Chapter 10: Multi-Layer Perceptrons
Chapter 11: DNN Low Level
Chapter 12: CNN
Chapter 13: Tensorboard
Chapter 14: Transfer Learning
Chapter 15: TensorFlow
Chapter 16: Long Short Term Memory (LSTM) Neural Networks
Chapter 17: Scaling Machine Learning
Chapter 18: Feature Engineering

Audience

This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasoned software developer who is comfortable and fluent in one or more modern development languages (preferably R, Python, or Spark).

Students are also expected to have a strong knowledge of the mathematical and statistical concepts which underlie Machine Learning as this course will NOT cover these concepts in-depth!

Course Outline


Chapter 1: Getting Started with Machine Learning
Git Repo (Getting Started with Machine Learning)
Lab - Brief Intro to Pandas

Chapter 2: Data Sets and Pandas
Introducing Datasets
Exploring - Pandas (Pandas and Files)
Lab - Exploring Pandas

Chapter 3: Python Visualization
Python Visualization
Lab - Intro to Visualization
Lab 2 - Visualization
Visualization Stats (Data Exploration)

Chapter 4: Intro Scikit-Learn
Lab Intro to Intro Scikit-Learn
Scikit-Learn Intro with Linear Regression
Lab - Linear Regression
Linear Regression
Multiple Linear Regression
Logistic Regression

Chapter 5: SVM Classification
SVM Classification
Lab - SVM College Admission

Chapter 6: Classification NB (ROC and Naive Bayes)
ROC and Naive Bayes
Lab - ROC and Naive Bayes

Chapter 7: Decision Trees
Decision Trees
Lab - Decision Tree Prosper Loan
Random Forests
K - Means Clustering
Lab - K - Means MTCars

Chapter 8: PCA
PCA
Lab - PCA

Chapter 9: Deep Learning Playground
Lab 1 - Deep Learning Playground
Lab 2 - Understanding TensorFlow Sessions
Lab 3 - Tips and 2b: MNIST Linear
Lab 4 - Estimator Cars and 2d: Keras Linear MNIST

Chapter 10: Multi-Layer Perceptrons
Multi Layer Perceptrons
Lab 1 -DL Playground with Hidden Layer

Chapter 11: DNN Low Level
Lab 2 - DNN Low Level Intro and Lab
DNN Iris Estimator and Lab

Chapter 12: CNN
CNN
CNN in Tensorflow

Chapter 13: Tensorboard
Tensorboard

Chapter 14: Transfer Learning
Transfer Learning

Chapter 15: TensorFlow
TensorFlow RNN

Chapter 16: Long Short Term Memory (LSTM) Neural Networks
LSTM Neural Networks

Chapter 17: Scaling Machine Learning
Scaling Machine Learning

Chapter 18: Feature Engineering
Feature Engineering

 


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Machine Learning: Understanding Solutions and Developing Applications

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