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Премиум
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    Introduction
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    Udemy 101: Getting the Most From This Course
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    [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
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    [Activity] MAC: Installing and Using Anaconda & Course Materials
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    [Activity] LINUX: Installing and Using Anaconda & Course Materials
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    Python Basics, Part 1 [Optional]
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    [Activity] Python Basics, Part 2 [Optional]
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    [Activity] Python Basics, Part 3 [Optional]
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    [Activity] Python Basics, Part 4 [Optional]
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    Introducing the Pandas Library [Optional]
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    Types of Data
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    Mean, Median, Mode
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    [Activity] Using mean, median, and mode in Python
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    [Activity] Variation and Standard Deviation
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    Probability Density Function; Probability Mass Function
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    Common Data Distributions
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    [Activity] Percentiles and Moments
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    [Activity] A Crash Course in matplotlib
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    [Activity] Advanced Visualization with Seaborn
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    [Activity] Covariance and Correlation
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    [Exercise] Conditional Probability
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    Exercise Solution: Conditional Probability of Purchase by Age
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    Bayes' Theorem
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    [Activity] Linear Regression
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    [Activity] Polynomial Regression
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    [Activity] Multiple Regression, and Predicting Car Prices
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    Multi-Level Models
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    Supervised vs. Unsupervised Learning, and Train/Test
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    [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
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    Bayesian Methods: Concepts
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    [Activity] Implementing a Spam Classifier with Naive Bayes
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    K-Means Clustering
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    [Activity] Clustering people based on income and age
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    Measuring Entropy
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    [Activity] WINDOWS: Installing Graphviz
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    [Activity] MAC: Installing Graphviz
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    [Activity] LINUX: Installing Graphviz
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    Decision Trees: Concepts
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    [Activity] Decision Trees: Predicting Hiring Decisions
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    Ensemble Learning
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    [Activity] XGBoost
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    Support Vector Machines (SVM) Overview
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    [Activity] Using SVM to cluster people using scikit-learn
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    User-Based Collaborative Filtering
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    Item-Based Collaborative Filtering
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    [Activity] Finding Movie Similarities
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    [Activity] Improving the Results of Movie Similarities
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    [Activity] Making Movie Recommendations to People
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    [Exercise] Improve the recommender's results
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    K-Nearest-Neighbors: Concepts
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    [Activity] Using KNN to predict a rating for a movie
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    Dimensionality Reduction; Principal Component Analysis
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    [Activity] PCA Example with the Iris data set
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    Data Warehousing Overview: ETL and ELT
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    Reinforcement Learning
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    [Activity] Reinforcement Learning & Q-Learning with Gym
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    Understanding a Confusion Matrix
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    Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
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    Bias/Variance Tradeoff
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    [Activity] K-Fold Cross-Validation to avoid overfitting
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    Data Cleaning and Normalization
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    [Activity] Cleaning web log data
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    Normalizing numerical data
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    [Activity] Detecting outliers
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    Feature Engineering and the Curse of Dimensionality
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    Imputation Techniques for Missing Data
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    Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
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    Binning, Transforming, Encoding, Scaling, and Shuffling
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    [Activity] Installing Spark - Part 1
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    [Activity] Installing Spark - Part 2
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    Spark Introduction
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    Spark and the Resilient Distributed Dataset (RDD)
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    Introducing MLLib
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    Introduction to Decision Trees in Spark
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    [Activity] K-Means Clustering in Spark
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    TF / IDF
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    [Activity] Searching Wikipedia with Spark
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    [Activity] Using the Spark 2.0 DataFrame API for MLLib
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    Deploying Models to Real-Time Systems
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    A/B Testing Concepts
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    T-Tests and P-Values
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    [Activity] Hands-on With T-Tests
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    Determining How Long to Run an Experiment
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    A/B Test Gotchas
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    Deep Learning Pre-Requisites
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    The History of Artificial Neural Networks
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    [Activity] Deep Learning in the Tensorflow Playground
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    Deep Learning Details
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    Introducing Tensorflow
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    [Activity] Using Tensorflow, Part 1
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    [Activity] Using Tensorflow, Part 2
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    [Activity] Introducing Keras
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    [Activity] Using Keras to Predict Political Affiliations
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    Convolutional Neural Networks (CNN's)
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    [Activity] Using CNN's for handwriting recognition
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    Recurrent Neural Networks (RNN's)
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    [Activity] Using a RNN for sentiment analysis
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    [Activity] Transfer Learning
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    Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
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    Deep Learning Regularization with Dropout and Early Stopping
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    The Ethics of Deep Learning
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    Learning More about Deep Learning
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    Your final project assignment
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    Final project review
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    More to Explore