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Machine Learning,Data Science and Deep Learning With Python
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Getting Started
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Introduction
[Activity] WINDOWS: Installing and Using Anaconda & Course Materials
[Activity] MAC: Installing and Using Anaconda & Course Materials
[Activity] LINUX: Installing and Using Anaconda & Course Materials
Python Basics, Part 1 [Optional]
Python Basics, Part 2 [Optional]
Python Basics, Part 3 [Optional]
Python Basics, Part 4 [Optional]
Introducing the Pandas Library [Optional]
Statistics and Probability Refresher, Python Practice
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Types of Data (Numerical, Categorical, Ordinal)
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 (Normal, Binomial, Poisson, etc)
[Activity] Percentiles and Moments
[Activity] A Crash Course in matplotlib
[Activity] Advanced Visualization with Seaborn
[Activity] Covariance and Correlation
[Exercise] Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes’ Theorem
Predictive Model
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[Activity] Linear Regression
[Activity] Polynomial Regression
[Activity] Multiple Regression, and Predicting Car Prices
Multi-Level Models
Machine Learning with Python
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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- Mean Clustering
[Activity] Clustering people based on income and age
Measuring Entropy
[Activity] WINDOWS: Installing Graphviz
[Activity] MAC: Installing Graphviz
[Activity] LINUX: Installing Graphviz
Decision Trees: Concepts
[Activity] Decision Trees: Predicting Hiring Decisions
Ensemble Learning
[Activity] XG Boost
Support Vector Machines (SVM) Overview
[Activity] Using SVM to cluster people using scikit-learn
Recommender System
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User-Based Collaborative Filtering
Item-Based Collaborative Filtering
[Activity] Finding Movie Similarities using Cosine Similarity
[Activity] Improving the Results of Movie Similarities
[Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
[Exercise] Improve the recommender’s results
More Data Mining and Machine Learning Techniques
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K-Nearest-Neighbors: Concepts
[Activity] Using KNN to predict a rating for a movie
Dimensionality Reduction; Principal Component Analysis (PCA)
[Activity] PCA Example with the Iris data set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
Cat and Mouse Example
Pac-Man Example
Python Markov Decision Process Toolbox
[Activity] Reinforcement Learning & Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
Dealing with Real-World Data
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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
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
Binning, Transforming, Encoding, Scaling, and Shuffling
Apache Spark: Machine Learning on Big Data
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Warning about Java 11 and Spark 3!
Spark installation notes for MacOS and Linux users
[Activity] Installing Spark – Part 1
[Activity] Installing Spark – Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
Introduction to Decision Tress in Spark
[Activity] K-Means Clustering in Spark
TF/IDF
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark 2.0 DataFrame API for MLLib
Experimental Design / ML in the Real World
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Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
[Activity] Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Gotchas
Deep Learning and Neural Networks
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Deep Learning Pre-Requisites
The History of Artificial Neural Networks
[Activity] Deep Learning in the Tensorflow Playground
Deep Learning Details
Introducing Tensorflow
Important note about Tensorflow 2
[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
[Activity] Transfer Learning
Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
Deep Learning Regularization with Dropout and Early Stopping
The Ethics of Deep Learning
Learning More about Deep Learning
Final Project
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Your final project assignment: Mammogram Classification
Final project review
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