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Tensorflow Masterclass For Machine Learning and Artificial Intelligence in Python
Introduction to the Course
Welcome to the World of TensorFlow (4:03)
Data and Code
Anaconda:Python Data Science Environment
Anaconda Installation For Mac Users
Install Tensorflow (15:12)
Written Instructions for Tensorflow Install
Introduction to Python Data Science Packages
Commonly Used Python Data Science Packages (3:16)
What is Numpy? (3:46)
Create Numpy (10:51)
Numpy for Statistical Operations (7:23)
Introduction to Pandas (12:06)
Read in CSV (7:13)
Read in Excel (5:31)
Basic Data Preprocessing (4:30)
Introduction to Tensorflow
Start With Tensorflow (2:36)
Start With Tensorflow Computational Graphs (2:56)
Common Mathematical Operations
A Brief Tensorflow Session (4:37)
Interactive Tensorflow Session (1:38)
Constants and Variables in Tensorflow (3:42)
Placeholders in Tensorflow
TensorBoard: Visualize Graphs in TensorFlow (2:44)
Access TensorBoard Graphs (2:55)
Some Preliminary Tensorflow and Keras Applications
Ordinary Least Squares Linear Regression (OLS): Theory (10:44)
OLS From First Principles (9:22)
Visualize the Results of OLS (3:28)
OLS With Multiple Predictors With Tensorflow-Part 1 (5:08)
Estimate With Tensorflow Estimators (3:05)
Multiple Regression With Tensorflow Estimators (5:24)
More on Linear Regressor Estimator (8:24)
GLM: Generalized Linear Model (5:25)
Linear Classifier For Binary Classification (9:33)
Accuracy Assessment For Binary Classification (4:19)
Linear Classification with Binary Classification With Mixed Predictors (8:15)
Some Basic Concepts
Machine Learning: Theory
What Are ANN (Artificial Neural Network) and DNN (Deep Neural Networks)? (9:17)
Unsupervised and Supervised Learning With Tensorflow
What is Unsupervised Learning? (5:32)
K-means Clustering: Theory (5:44)
Implement K-Means on Real Data (5:37)
Softmax Classification (7:35)
Random Forests (RF) Theory (7:14)
Random Forest (RF) for Binary Classification (7:09)
Random Forest (RF) for Multiclass Classification (5:07)
kNN Theory
Implement kNN (3:22)
Neural Network for Tensorflow & Keras
Multi Layer Perceptron (MLP) with Tensorflow (6:24)
Multi Layer Perceptron (MLP) With Keras (3:31)
Keras MLP For Binary Classification (4:01)
Keras MLP for Multiclass Classification (6:01)
Keras MLP for Regression (3:27)
Deep Learning For Tensorflow & Keras
Deep Neural Network (DNN) Classifier With Tensorflow (6:47)
Deep Neural Network (DNN) Classifier With Mixed Predictors (8:11)
Deep Neural Network (DNN) Regression With Tensorflow (5:24)
New Lecture
Wide & Deep Learning (Tensorflow) (11:34)
DNN Classifier With Keras (3:30)
DNN Classifier With Keras-Example 2 (4:23)
Autoencoders with Convolution Neural Networks (CNN)
Autoencoders With CNN-Tensorflow (7:15)
Autoencoders With CNN- Keras (4:46)
Recurrent Neural Network (RNN)
Introduction to RNN (5:40)
LSTM for Time Series (6:24)
LSTM for Stock Prices (7:21)
Implement K-Means on Real Data
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