Data Science and Machine Learning with Python - Hands On! - Skillshare

Data Science and Machine Learning with Python - Hands On!

Frank Kane, Founder of Sundog Education, ex-Amazon

Play Speed
  • 0.5x
  • 1x (Normal)
  • 1.25x
  • 1.5x
  • 2x
69 Videos (9h 2m)
    • Introduction

      2:44
    • Getting What you Need

      2:36
    • Installing Enthought Canopy

      6:51
    • Python Basics, Part 1

      15:58
    • Python Basics, Part 2

      9:41
    • Running Python Scripts

      3:55
    • Introducing the Pandas Library

      10:14
    • Types of Data

      6:58
    • Mean, Median, Mode

      5:26
    • Using mean, median, and mode in Python

      8:30
    • Variation and Standard Deviation

      11:12
    • Probability Density Function; Probability Mass Function

      3:27
    • Common Data Distributions

      7:45
    • Percentiles and Moments

      12:32
    • A Crash Course in matplotlib

      13:46
    • Covariance and Correlation

      11:31
    • Exercise: Conditional Probability

      10:16
    • Exercise Solution: Conditional Probability

      2:18
    • Bayes' Theorem

      5:23
    • Linear Regression

      11:01
    • Polynomial Regression

      8:04
    • Multivariate Regression

      9:52
    • Multi-Level Models

      4:36
    • Supervised vs. Unsupervised Learning, Train / Test

      8:57
    • Using Train/Test to Prevent Overfitting

      5:47
    • Bayesian Methods: Concepts

      3:59
    • Implementing a Spam Classifier with Naive Bayes

      8:05
    • K-Means Clustering

      7:23
    • Clustering People by Income and Age

      5:14
    • Measuring Entropy

      3:09
    • Decision Trees: Concepts

      8:43
    • Decision Trees: Predicting Hiring Decisions

      9:47
    • Ensemble Learning

      5:59
    • Support Vector Machines (SVM) Overview

      4:27
    • Using SVM to Cluster People

      5:36
    • User-Based Collaborative Filtering

      7:57
    • Item-Based Collaborative Filtering

      8:15
    • Finding Movie Similarities

      9:08
    • Improving the Results of Movie Similarities

      7:59
    • Making Movie Recommendations to People

      10:22
    • Improving the Recommender's Results

      5:29
    • K-Nearest-Neighbors: Concepts

      3:44
    • Using KNN to Predict a Rating for a Movie

      12:29
    • Dimensionality Reduction; Principal Component Analysis

      5:44
    • PCA Example with the Iris Data Set

      9:05
    • Data Warehousing; ETL and ELT

      9:05
    • Reinforcement Learning

      12:44
    • Bias / Variance Tradeoff

      6:15
    • K-Fold Cross Validation

      10:54
    • Data Cleaning and Normalization

      7:10
    • Cleaning Web Log Data

      10:56
    • Normalizing Numerical Data

      3:22
    • Detecting Outliers

      7:00
    • Installing Spark - Part 1

      7:02
    • Installing Spark - Part 2

      13:29
    • Spark Introduction

      9:10
    • Spark and the Resilient Distributed Dataset (RDD)

      11:42
    • Introducing MLLib

      5:09
    • Decision Trees in Spark

      16:00
    • K-Means Clustering in Spark

      11:07
    • TF / IDF

      6:43
    • Searching Wikipedia with Spark

      8:11
    • Using the Spark 2.0 DataFrame API for MLLib

      7:57
    • A/B Testing Concepts

      8:23
    • T-Tests and P-Values

      5:59
    • Hands-On with T-Tests

      6:03
    • Determining How Long to Run an Experiment

      3:24
    • A/B Test Gotchas

      9:26
    • Where to Go From Here

      2:59
118 students are watching this class