Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

CreativeLive

Python Machine Learning Bootcamp

via CreativeLive

Go to class Write review

Overview

This course begins with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course then progresses to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forests. This bring...

Syllabus

  • Python for Machine Learning Introduction
  • Colab Setup
  • Drive Setup
  • Jupyter Setup
  • Loading The Cars Data
  • Panda Dataframe Slices
  • Challenge 1 Slices
  • Challenge 1 Slices Solution
  • Initial Data Analysis Tools
  • Median
  • Mode
  • Tuples
  • Averages In Real Data
  • Standard Deviation And The Bell Curve
  • Calculating Standard Deviation
  • Variance
  • Percentile
  • Challenge 2 Percentile From User Input
  • Challenge 2 Percentile From User Input Solution
  • Uniform Distribution Historgrams
  • Normal Distribution Histograms
  • Linear Regression
  • Plotting Attendance Against Concessions
  • Using Pandas For Vector Operations
  • A Prediction Concessions Function
  • Predicting Concessions From User Input
  • Adding Predictions To Our Data
  • Polynomial Regressions
  • Supervised Learning Intro
  • Car Data Overview
  • Picking Features From Domain Knowledge
  • Correlation Matrixes
  • Pair Plots Intro
  • Pair Plot Analysis
  • Cleaning Data
  • Splitting Training Data From Test Data
  • Scaling the Data
  • Linear Regression Model Intro
  • Training the Linear Regression Model
  • Comparing Predictions to Test Data
  • Accuracy Score
  • Removing Outliers
  • Filtering Outliers Out
  • Training and Testing a Second Model
  • Logistic Regression Intro
  • Hr Data Overview
  • Data Analysis Crosstabs
  • Making Our Crosstab More Readable
  • One Hot Encoding
  • Concatenating Ohe Data
  • Training a Logistic Regression Model
  • Measuring Accuracy
  • Confusion Matrix
  • Precision and Recall
  • Knn Intro
  • Visualizing Knn
  • Plotting Training Data
  • Creating and Training Our Knn Model
  • Visualizing Our New Point
  • Visualizing the Prediction
  • Iris Knn Intro
  • Visualizing Multi Dimensional Datasets
  • Loading and Reviewing Iris Data
  • Creating Iris Dataframe
  • Prepping Our Data for Knn
  • Training and Testing Our Knn Model
  • Creating a Classification Report
  • Analyzing and Summing Up Knn Results
  • Titanic Dataset Intro
  • Titanic Dataset Closer Look
  • Filling in Na Values
  • Plotting Survived vs Perished
  • Plotting by 2 Columns
  • Plotting More Data
  • Graphing a Combined Column
  • Label Encoding
  • Splitting and Scaling Our Data
  • Random Forest Classifiers
  • Creating and Training Our Model
  • Cleaning Up the Test Data
  • Label Encoding and Scaling Our Test Data
  • Fixing Column Order Error
  • Submitting to Kaggle
  • Neural Networks Intro
  • Notebook Setup
  • Task Intro
  • Analyzing the Shape of Our Data
  • Unpacking All Our Data
  • Looking at a Digit Array Part 1
  • Looking at a Digit Array Part 2
  • Training and Testing Review
  • Normalizing Our Data
  • Building a Neural Network
  • Training Our Model
  • Analyzing Predictions

Reviews

4.7 rating at CreativeLive based on 105 ratings

Start your review of Python Machine Learning Bootcamp

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.