Course curriculum

  • 1

    Introduction

    • Course Introduction

    • What is Machine Learning

    • Data Importance

    • Supervised Machine Learning

    • Over-Fitting

    • Bias Variance Tradeoff

    • Cross Validation

    • Lab: Verify and Update Scikit-Learn
  • 2

    Linear Regression

    • Simple Linear Regression

    • Defining the Hyperplane

    • Estimators

    • Cost Functions

    • Scoring the Model

    • Lab: A Basic Linear Regression Model

    • Regurlarization

    • Regression

    • Multi-Variate Linear Regression

    • Visualize the Wine Attributes

    • Fitting and Evaluating the Model

    • Gradient Descent

    • Download Wine Dataset

    • Lab: Applied Linear Regression

  • 3

    Feature Extraction and Preprocessing

    • Statistical Variables

    • Numerical and Categorical Data

    • One Hot Encoding

    • Bag of Words Basics

    • The Curse of Dimensionality

    • Stop Words

    • Lemmatizing

    • Stemming

    • Extracting Features from Images

    • Extract Features from Text

    • Data Standardization
  • 4

    Natural Language Processing

    • Loading the Dataset

    • Extract Features from Text

    • Execute the Model

    • Pipelines

    • SGDClassifier

    • Lab: Natural Language Processing

    • Confusion Matrix

    • Precision and Recall

    • ROC

    • Grid Search
  • 5

    Nonlinear Classification

    • Decision Trees

    • Lab: Decision Tree Classifier

    • Decision Tress: Classification

    • Decision Tress: Regression

    • Ensemble Models

    • Lab: Random Forest Classifier

  • 6

    K-Means Clustering

    • K-Means Clustering

    • How K Means Works

    • K-Means Walk Through

    • Lab: K-Means