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Machine Learning, Data Science and Deep Learning with Python

Getting Started :
Udemy 101: Getting the Most From This Course
[Activity] Getting What You Need
[Activity] Installing Enthought Canopy
Python Basics, Part 1 [Optional]
[Activity] Python Basics, Part 2 [Optional]
Running Python Scripts [Optional]
Introducing the Pandas Library [Optional]

Statistics and Probability Refresher, and Python Practise :
Types of Data
Mean, Median, Mode
[Activity] Using mean, median, and mode in Python
[Activity] Variation and Standard Deviation
Probability Density Function; Probability Mass Function
Common Data Distributions
[Activity] Percentiles and Moments
[Activity] A Crash Course in matplotlib
[Activity] Covariance and Correlation
[Exercise] Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes' Theorem

Predictive Models :
[Activity] Linear Regression
[Activity] Polynomial Regression
[Activity] Multivariate Regression, and Predicting Car Prices
Multi-Level Models

Machine Learning with Python :
Supervised vs. Unsupervised Learning, and Train/Test
[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
Bayesian Methods: Concepts
[Activity] Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
[Activity] Clustering people based on income and age
Measuring Entropy
[Activity] Install GraphViz
Decision Trees: Concepts
[Activity] Decision Trees: Predicting Hiring Decisions
Ensemble Learning
Support Vector Machines (SVM) Overview
[Activity] Using SVM to cluster people using scikit-learn

Recommender Systems :
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
[Activity] Finding Movie Similarities
[Activity] Improving the Results of Movie Similarities
[Activity] Making Movie Recommendations to People
[Exercise] Improve the recommender's results

More Data Mining and Machine Learning Techniques :
K-Nearest-Neighbors: Concepts
[Activity] Using KNN to predict a rating for a movie
Dimensionality Reduction; Principal Component Analysis
[Activity] PCA Example with the Iris data set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning

Dealing with Real-World Data :
Bias/Variance Tradeoff
[Activity] K-Fold Cross-Validation to avoid overfitting
Data Cleaning and Normalization
[Activity] Cleaning web log data
Normalizing numerical data
[Activity] Detecting outliers

Apache Spark: Machine Learning on Big Data :
Warning about Java 11 and Spark 2.4!
[Activity] Installing Spark - Part 1
[Activity] Installing Spark - Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
[Activity] Decision Trees in Spark
[Activity] K-Means Clustering in Spark
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark 2.0 DataFrame API for MLLib

Experimental Design :
A/B Testing Concepts
T-Tests and P-Values
[Activity] Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas

Deep Learning and Neural Networks :
Deep Learning Pre-Requisites
The History of Artificial Neural Networks
[Activity] Deep Learning in the Tensorflow Playground
Deep Learning Details
Introducing Tensorflow
[Activity] Using Tensorflow, Part 1
[Activity] Using Tensorflow, Part 2
[Activity] Introducing Keras
[Activity] Using Keras to Predict Political Affiliations
Convolutional Neural Networks (CNN's)
[Activity] Using CNN's for handwriting recognition
Recurrent Neural Networks (RNN's)
[Activity] Using a RNN for sentiment analysis
The Ethics of Deep Learning
Learning More about Deep Learning

Final Project:
Your final project assignment
Final project review

You made it!:
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Bonus Lecture: Discounts on my Spark and MapReduce courses!