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Machine Learning and TensorFlow on the Google Cloud
Introduction To Google Cloud Platform
Introducing the Google Cloud Platform (13:22)
Lab: Setting Up A GCP Account (6:59)
Lab: Using The Cloud Shell (6:01)
TensorFlow and Machine Learning
Introducing Machine Learning (8:06)
Representation Learning (10:29)
NN Introduced (7:37)
Introducing TF (7:18)
Lab: Simple Math Operations (8:46)
Computation Graph (10:19)
Tensors (9:04)
Lab: Tensors (5:03)
Linear Regression Intro (9:59)
Placeholders and Variables (8:46)
Lab: Placeholders (6:36)
Lab: Variables (7:49)
Lab: Linear Regression with Made-up Data (4:52)
Image Processing (8:07)
Images As Tensors (8:18)
Lab: Reading and Working with Images (8:05)
Lab: Image Transformations (6:37)
Introducing MNIST (4:15)
K-Nearest Neigbors as Unsupervised Learning (7:44)
One-hot Notation and L1 Distance (7:33)
Steps in the K-Nearest-Neighbors Implementation (9:34)
Lab: K-Nearest-Neighbors (14:14)
Learning Algorithm (11:00)
Individual Neuron (9:54)
Learning Regression (7:53)
Learning XOR (10:29)
XOR Trained (11:13)
Regression in TensorFlow
Lab: Access Data from Yahoo Finance (2:49)
Non TensorFlow Regression (8:07)
Lab: Linear Regression - Setting Up a Baseline (11:18)
Gradient Descent (9:58)
Lab: Linear Regression (14:42)
Lab: Multiple Regression in TensorFlow (9:15)
Logistic Regression Introduced (10:18)
Linear Classification (5:27)
Lab: Logistic Regression - Setting Up a Baseline (7:33)
Logit (8:35)
Softmax (11:57)
Argmax (12:15)
Lab: Logistic Regression (16:56)
Estimators (4:12)
Lab: Linear Regression using Estimators (7:49)
Lab: Logistic Regression using Estimators (4:54)
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 (10:53)
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 (11:55)
Naive Bayes Classifier (5:26)
Naive Bayes Classifier : An example (9:19)
Support Vector Machines Introduced (8:33)
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick (16:42)
Association Detection
Association Rules Learning (9:34)
Dimensionality Reduction
Dimensionality Reduction (17:41)
Principal Component Analysis (19:20)
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning (2:36)
Sentiment Analysis - What's all the fuss about? (17:19)
ML Solutions for Sentiment Analysis - the devil is in the details (19:59)
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet) (18:51)
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
Overfitting - the bane of Machine Learning (19:03)
Overfitting Continued (11:19)
Cross Validation (18:55)
Simplicity is a virtue - Regularization (7:18)
The Wisdom of Crowds - Ensemble Learning (16:39)
Ensemble Learning continued - Bagging, Boosting and Stacking (18:02)
Random Forests
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 (9:42)
Discover the Underlying Truth - Latent Factor Collaborative Filtering (20:13)
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)
Top Picks for You! - Recommendations with Neighbourhood Models
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