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Machine Learning and TensorFlow on the Google Cloud
Introduction To Google Cloud Platform
Introducing the Google Cloud Platform (13:20)
Lab Setting Up A GCP Account (6:59)
Lab Using The Cloud Shell (6:01)
TensorFlow and Machine Learning
Introducing Machine Learning (8:04)
Representation Learning (10:27)
NN Introduced (7:35)
Introducing TF (7:16)
Lab Simple Math Operations (8:46)
Computation Graph (10:17)
Tensors (9:02)
Lab Tensors (5:03)
Linear Regression Intro (9:57)
Placeholders and Variables (8:44)
Lab Placeholders (6:36)
Lab Variables (7:49)
Lab Linear Regression with Made-up Data (4:52)
Image Processing (8:05)
Images As Tensors (8:16)
Lab Reading and Working with Images (8:05)
Lab Image Transformations (6:37)
Introducing MNIST (4:13)
K-Nearest Neigbors as Unsupervised Learning (7:31)
One-hot Notation and L1 Distance (7:31)
Steps in the K-Nearest-Neighbors Implementation (9:32)
Lab K-Nearest-Neighbors (14:14)
Learning Algorithm (10:58)
Individual Neuron (9:52)
Learning Regression (7:51)
Learning XOR (10:27)
XOR Trained (11:11)
TensorFlowSourceCode
Regression in TensorFlow
Lab Access Data from Yahoo Finance (2:49)
Non TensorFlow Regression (8:05)
Lab Linear Regression - Setting Up a Baseline (11:18)
Gradient Descent (9:56)
Lab Linear Regression (14:42)
Lab Multiple Regression in TensorFlow (9:15)
Logistic Regression Introduced (10:16)
Linear Classification (5:25)
Lab Logistic Regression - Setting Up a Baseline (7:33)
Logit (8:33)
Softmax (11:55)
Argmax (12:13)
Lab Logistic Regression (16:56)
Estimators (4:10)
Lab Linear Regression using Estimators (7:49)
Lab Logistic Regression using Estimators
TensorFlowSourceCode
Vision, Translate, NLP and Speech Trained ML APIs
Lab Taxicab Prediction - Setting up the dataset (14:38)
Lab Taxicab Prediction - Training and Running the model (11:22)
Lab The Vision, Translate, NLP and Speech API (3:17)
Lab The Vision API for Label and Landmark Detection (7:00)
Machine Learning Algorithms
A Brief Introduction to Machine Learning Algorithms (1:10)
Solving Classification Problems
Solving Classification Problems (0:59)
Random Variables (11:27)
Bayes Theorem
Naive Bayes Classifier (5:26)
Naive Bayes Classifier An example (9:18)
Support Vector Machines Introduced (8:31)
Support Vector Machines Maximum Margin Hyperplane and Kernel Trick (16:40)
Association Detection
Association Rules Learning (9:32)
Machine Learning and TensorFlow on the Google Cloud\8 Dimensionality Reduction
Dimensionality Reduction (17:39)
Principal Component Analysis (19:18)
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning (2:36)
Sentiment Analysis - What's all the fuss about (17:17)
MLSOL~1 (19:57)
SENTI~1 (18:49)
Decision Trees
Using Tree Based Models for Classification (1:00)
Planting the seed - What are Decision Trees (17:00)
Growing the Tree - Decision Tree Learning (18:03)
Branching out - Information Gain (18:51)
Decision Tree Algorithms (7:50)
A Few Useful Things to Know About Overfitting
OVERF~1 (19:03)
Overfitting Continued (11:19)
Cross Validation (18:55)
SIMPL~1 (7:18)
THEWI~1 (16:39)
ENSEM~1 (18:02)
Random Forests
Overfitting
Random Forests - Much more than trees (12:28)
Recommendation Systems
Solving Recommendation Problems (0:56)
What do Amazon and Netflix have in common (16:43)
Recommendation Engines - A look inside (10:45)
What are you made of - Content-Based Filtering (13:35)
With a little help from friends - Collaborative Filtering (10:26)
A Neighbourhood Model for Collaborative Filtering (17:51)
Top Picks for You! - Recommendations with Neighbourhood Models (9:41)
Discover the Underlying Truth - Latent Factor Collaborative Filtering (8:41)
Latent Factor Collaborative Filtering contd. (12:09)
Gray Sheep and Shillings - Challenges with Collaborative Filtering (8:12)
The Apriori Algorithm for Association Rules (18:31)
What are you made of - Content-Based Filtering
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