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Python: Machine Learning and Data Science

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Curriculum For This Course
  • 1. Types of Data 6m 58s
  • 2. Mean, Median, Mode 5m 26s
  • 3. [Activity] Using mean, median, and mode in Python 8m 30s
  • 4. [Activity] Variation and Standard Deviation 11m 12s
  • 5. Probability Density Function; Probability Mass Function 3m 27s
  • 6. Common Data Distributions 7m 45s
  • 7. [Activity] Percentiles and Moments 12m 33s
  • 8. [Activity] A Crash Course in matplotlib 13m 46s
  • 9. [Activity] Covariance and Correlation 11m 31s
  • 10. [Exercise] Conditional Probability 10m 16s
  • 11. Exercise Solution: Conditional Probability of Purchase by Age 2m 18s
  • 12. Bayes' Theorem 5m 23s
  • 1. [Activity] Linear Regression 11m 1s
  • 2. [Activity] Polynomial Regression 8m 4s
  • 3. [Activity] Multivariate Regression, and Predicting Car Prices 9m 53s
  • 4. Multi-Level Models 4m 36s
  • 1. Supervised vs 8m 57s
  • 2. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 5m 47s
  • 3. Bayesian Methods: Concepts 3m 59s
  • 4. [Activity] Implementing a Spam Classifier with Naive Bayes 8m 5s
  • 5. K-Means Clustering 7m 23s
  • 6. [Activity] Clustering people based on income and age 5m 14s
  • 7. Measuring Entropy 3m 9s
  • 8. Decision Trees: Concepts 8m 43s
  • 9. [Activity] Decision Trees: Predicting Hiring Decisions 9m 47s
  • 10. Ensemble Learning 5m 59s
  • 11. Support Vector Machines (SVM) Overview 4m 27s
  • 12. [Activity] Using SVM to cluster people using scikit-learn 5m 36s
  • 1. User-Based Collaborative Filtering 7m 57s
  • 2. Item-Based Collaborative Filtering 8m 15s
  • 3. [Activity] Finding Movie Similarities 9m 8s
  • 4. [Activity] Improving the Results of Movie Similarities 7m 59s
  • 5. [Activity] Making Movie Recommendations to People 10m 22s
  • 6. [Exercise] Improve the recommender's results 5m 29s
  • 1. K-Nearest-Neighbors: Concepts 3m 44s
  • 2. [Activity] Using KNN to predict a rating for a movie 12m 29s
  • 3. Dimensionality Reduction; Principal Component Analysis 5m 44s
  • 4. [Activity] PCA Example with the Iris data set 9m 5s
  • 5. Data Warehousing Overview: ETL and ELT 9m 5s
  • 6. Reinforcement Learning 12m 44s
  • 1. Bias/Variance Tradeoff 6m 15s
  • 2. [Activity] K-Fold Cross-Validation to avoid overfitting 10m 55s
  • 3. Data Cleaning and Normalization 7m 10s
  • 4. [Activity] Cleaning web log data 10m 56s
  • 5. Normalizing numerical data 3m 22s
  • 6. [Activity] Detecting outliers 7m
  • 1. [Activity] Installing Spark - Part 1 7m 2s
  • 2. [Activity] Installing Spark - Part 2 13m 29s
  • 3. Spark Introduction 9m 10s
  • 4. Spark and the Resilient Distributed Dataset (RDD) 11m 42s
  • 5. Introducing MLLib 5m 9s
  • 6. [Activity] Decision Trees in Spark 16m
  • 7. [Activity] K-Means Clustering in Spark 11m 7s
  • 8. TF / IDF 6m 44s
  • 9. [Activity] Searching Wikipedia with Spark 8m 11s
  • 10. [Activity] Using the Spark 2.0 DataFrame API for MLLib 7m 57s
  • 1. A/B Testing Concepts 8m 23s
  • 2. T-Tests and P-Values 5m 59s
  • 3. [Activity] Hands-on With T-Tests 6m 4s
  • 4. Determining How Long to Run an Experiment 3m 24s
  • 5. A/B Test Gotchas 9m 26s
  • 1. Deep Learning Pre-Requisites 10m 51s
  • 2. The History of Artificial Neural Networks 11m 15s
  • 3. [Activity] Deep Learning in the Tensorflow Playground 12m
  • 4. Deep Learning Details 9m 29s
  • 5. Introducing Tensorflow 12m 39s
  • 6. [Activity] Using Tensorflow, Part 1 9m 37s
  • 7. [Activity] Using Tensorflow, Part 2 13m 27s
  • 8. [Activity] Introducing Keras 14m 22s
  • 9. [Activity] Using Keras to Predict Political Affiliations 12m 30s
  • 10. Convolutional Neural Networks (CNN's) 11m 28s
  • 11. [Activity] Using CNN's for handwriting recognition 8m 15s
  • 12. Recurrent Neural Networks (RNN's) 11m 2s
  • 13. [Activity] Using a RNN for sentiment analysis 10m 15s
  • 14. The Ethics of Deep Learning 11m 2s
  • 15. Learning More about Deep Learning 1m 45s
  • 1. Your final project assignment 6m 26s
  • 2. Final project review 8m 59s

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