Curriculum For This Course

Getting Started

6Lectures

Time 00:48:12

Statistics and Probability Refresher, and Python Practise

12Lectures

Time 01:39:05

Predictive Models

4Lectures

Time 00:33:33

Machine Learning with Python

12Lectures

Time 01:17:06

Recommender Systems

6Lectures

Time 00:49:10

More Data Mining and Machine Learning Techniques

6Lectures

Time 00:52:51

Dealing with Real-World Data

6Lectures

Time 00:45:38

Apache Spark: Machine Learning on Big Data

10Lectures

Time 01:36:31

Experimental Design

5Lectures

Time 00:33:16

Deep Learning and Neural Networks

15Lectures

Time 02:39:12

Final Project

2Lectures

Time 00:16:52

Getting Started
	1. [Activity] Getting What You Need	05:10
	2. [Activity] Installing Enthought Canopy	15:58
	3. Python Basics, Part 1 [Optional]	09:41
	4. [Activity] Python Basics, Part 2 [Optional]	03:55
	5. Running Python Scripts [Optional]	10:15
	6. Introducing the Pandas Library [Optional]	10:14

Statistics and Probability Refresher, and Python Practise
	1. Types of Data	06:58
	2. Mean, Median, Mode	05:26
	3. [Activity] Using mean, median, and mode in Python	08:30
	4. [Activity] Variation and Standard Deviation	11:12
	5. Probability Density Function; Probability Mass Function	03:27
	6. Common Data Distributions	07:45
	7. [Activity] Percentiles and Moments	12:33
	8. [Activity] A Crash Course in matplotlib	13:46
	9. [Activity] Covariance and Correlation	11:31
	10. [Exercise] Conditional Probability	10:16
	11. Exercise Solution: Conditional Probability of Purchase by Age	02:18
	12. Bayes' Theorem	05:23

Predictive Models
	1. [Activity] Linear Regression	11:01
	2. [Activity] Polynomial Regression	08:04
	3. [Activity] Multivariate Regression, and Predicting Car Prices	09:53
	4. Multi-Level Models	04:36

Machine Learning with Python
	1. Supervised vs. Unsupervised Learning, and Train/Test	08:57
	2. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression	05:47
	3. Bayesian Methods: Concepts	03:59
	4. [Activity] Implementing a Spam Classifier with Naive Bayes	08:05
	5. K-Means Clustering	07:23
	6. [Activity] Clustering people based on income and age	05:14
	7. Measuring Entropy	03:09
	8. Decision Trees: Concepts	08:43
	9. [Activity] Decision Trees: Predicting Hiring Decisions	09:47
	10. Ensemble Learning	05:59
	11. Support Vector Machines (SVM) Overview	04:27
	12. [Activity] Using SVM to cluster people using scikit-learn	05:36

Recommender Systems
	1. User-Based Collaborative Filtering	07:57
	2. Item-Based Collaborative Filtering	08:15
	3. [Activity] Finding Movie Similarities	09:08
	4. [Activity] Improving the Results of Movie Similarities	07:59
	5. [Activity] Making Movie Recommendations to People	10:22
	6. [Exercise] Improve the recommender's results	05:29

More Data Mining and Machine Learning Techniques
	1. K-Nearest-Neighbors: Concepts	03:44
	2. [Activity] Using KNN to predict a rating for a movie	12:29
	3. Dimensionality Reduction; Principal Component Analysis	05:44
	4. [Activity] PCA Example with the Iris data set	09:05
	5. Data Warehousing Overview: ETL and ELT	09:05
	6. Reinforcement Learning	12:44

Dealing with Real-World Data
	1. Bias/Variance Tradeoff	06:15
	2. [Activity] K-Fold Cross-Validation to avoid overfitting	10:55
	3. Data Cleaning and Normalization	07:10
	4. [Activity] Cleaning web log data	10:56
	5. Normalizing numerical data	03:22
	6. [Activity] Detecting outliers	07:00

Apache Spark: Machine Learning on Big Data
	1. [Activity] Installing Spark - Part 1	07:02
	2. [Activity] Installing Spark - Part 2	13:29
	3. Spark Introduction	09:10
	4. Spark and the Resilient Distributed Dataset (RDD)	11:42
	5. Introducing MLLib	05:09
	6. [Activity] Decision Trees in Spark	16:00
	7. [Activity] K-Means Clustering in Spark	11:07
	8. TF / IDF	06:44
	9. [Activity] Searching Wikipedia with Spark	08:11
	10. [Activity] Using the Spark 2.0 DataFrame API for MLLib	07:57

Experimental Design
	1. A/B Testing Concepts	08:23
	2. T-Tests and P-Values	05:59
	3. [Activity] Hands-on With T-Tests	06:04
	4. Determining How Long to Run an Experiment	03:24
	5. A/B Test Gotchas	09:26

Deep Learning and Neural Networks
	1. Deep Learning Pre-Requisites	10:51
	2. The History of Artificial Neural Networks	11:15
	3. [Activity] Deep Learning in the Tensorflow Playground	12:00
	4. Deep Learning Details	09:29
	5. Introducing Tensorflow	12:39
	6. [Activity] Using Tensorflow, Part 1	09:37
	7. [Activity] Using Tensorflow, Part 2	13:27
	8. [Activity] Introducing Keras	14:22
	9. [Activity] Using Keras to Predict Political Affiliations	12:30
	10. Convolutional Neural Networks (CNN's)	11:28
	11. [Activity] Using CNN's for handwriting recognition	08:15
	12. Recurrent Neural Networks (RNN's)	11:02
	13. [Activity] Using a RNN for sentiment analysis	10:15
	14. The Ethics of Deep Learning	11:02
	15. Learning More about Deep Learning	01:45

Final Project
	1. Your final project assignment	06:26
	2. Final project review	08:59

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Python: Machine Learning and Data Science

Curriculum For This Course

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