A Beginner's Guide to Machine Learning (in Python)

Dana Bani-Hani

Play Speed
  • 0.5x
  • 1x (Normal)
  • 1.25x
  • 1.5x
  • 2x
44 Videos (3h 27m)
    • Introduction and Course Scope

      3:21
    • Big Data

      2:38
    • Data Science

      6:10
    • Data Analytics

      3:16
    • Machine Learning and Data Mining

      7:27
    • Machine Learning in This Course

      1:15
    • Exploratory Data Analysis

      7:55
    • Introduction to Python

      4:34
    • Descriptive Statistics in Python

      6:48
    • Dataset Resources

      2:51
    • Model Evaluation

      3:39
    • Linear Regression

      4:30
    • Support Vector Machine

      1:38
    • Support Vector Machine in Python

      5:42
    • K-Nearest Neighbor

      1:38
    • K-Nearest Neighbor in Python

      1:32
    • Decision Trees

      1:44
    • Decision Trees in Python

      2:10
    • Logistic Regression

      1:34
    • Neural Networks

      8:08
    • Neural Networks in Python

      1:30
    • Ensemble Learning

      1:48
    • Ensemble Learning in Python

      8:03
    • Regression Problem in Python

      8:50
    • Hyperparameters

      3:31
    • Performance Metrics

      6:28
    • Overfitting vs Underfitting

      1:44
    • Data Cleaning

      1:59
    • Data Transformation

      4:07
    • Data Transformation in Python

      1:54
    • Categorical Features

      2:46
    • Unbalanced Data

      3:07
    • Validation Methods

      4:42
    • The Holdout Method and Confusion Matrix in Python

      6:23
    • The K Fold Method and Cleaning the Data in Python

      15:51
    • Classifying New Observations

      4:39
    • Feature Selection

      3:34
    • Feature Selection in Python

      8:49
    • Dimensionality Reduction

      2:25
    • Principle Component Analysis in Python

      6:08
    • Hyperparameter Optimization

      5:21
    • Grid Search #1 in Python

      5:51
    • Grid Search #2 in Python

      6:36
    • Grid Search #3 in Python

      11:59

About This Class

In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, and Neural Networks. You'll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data. By the end of this course, you will understand the ABCs of Data Mining and be able to implement what you've learnt on your own, more specifically, be able to implement what you've learnt on Python. There is no ideal student as there are no prior requirements needed - everybody is welcome!!

13

Students

--

Projects

0

Reviews (0)

Hi! I'm Dana. I'm currently a PhD student in Industrial Engineering at SUNY Binghamton. I finished my B.S. in Architectural Engineering at Jordan University of Science and Technology and my M.S. in Industrial Engineering at my current school. I enjoy learning new things. My research interests are Machine Learning and Artificial Intelligence in Healthcare.

See full profile