Course curriculum

    1. Course Introduction

    2. What is Machine Learning

    3. Data Importance

    4. Supervised Machine Learning

    5. Over-Fitting

    6. Bias Variance Tradeoff

    7. Cross Validation

    8. Lab: Verify and Update Scikit-Learn

    1. Simple Linear Regression

    2. Defining the Hyperplane

    3. Estimators

    4. Cost Functions

    5. Scoring the Model

    6. Lab: A Basic Linear Regression Model

    7. Regurlarization

    8. Regression

    9. Multi-Variate Linear Regression

    10. Visualize the Wine Attributes

    11. Fitting and Evaluating the Model

    12. Gradient Descent

    13. Download Wine Dataset

    14. Lab: Applied Linear Regression

    1. Statistical Variables

    2. Numerical and Categorical Data

    3. One Hot Encoding

    4. Bag of Words Basics

    5. The Curse of Dimensionality

    6. Stop Words

    7. Lemmatizing

    8. Stemming

    9. Extracting Features from Images

    10. Extract Features from Text

    11. Data Standardization

    1. Loading the Dataset

    2. Extract Features from Text

    3. Execute the Model

    4. Pipelines

    5. SGDClassifier

    6. Lab: Natural Language Processing

    7. Confusion Matrix

    8. Precision and Recall

    9. ROC

    10. Grid Search

    1. Decision Trees

    2. Lab: Decision Tree Classifier

    3. Decision Tress: Classification

    4. Decision Tress: Regression

    5. Ensemble Models

    6. Lab: Random Forest Classifier

    1. K-Means Clustering

    2. How K Means Works

    3. K-Means Walk Through

    4. Lab: K-Means

About this course

  • Free
  • 53 lessons
  • 1 hour of video content