Intuitive Machine Learning With Python
 

Students will understand the intuition behind some of the most widely used machine learning techniques.
Students will understand how to interpret output from machine learning algorithms.
Students will understand the appropriateness of machine learning tools.
Students will understand the intuition behind Python codes for machine learning.
Requirements
Students do not need any basic knowledge for this course. I will provide you with Python codes that you can run and tweak. The objective of this course is to gather a firm intuition, not to delve deep into codes.
Description
Are you new to machine learning? Does the math seem overwhelming? If yes, this course is for you!In this course, you'll start from the basics. You'll download Python to your local machine and connect it with VS Code. You'll learn foundational Python skills - how to open .csv files, explore and select data, and apply basic data functions.From there, you'll dive into powerful machine learning techniques without diving into the math. You'll begin with supervised learning for classification, covering:Decision TreesK-Nearest NeighborsRandom ForestsRegression TreesThen, you'll move to more advanced models like:Ridge, Lasso, and ElasticNet RegressionSupport Vector MachinesAlong the way, you'll intuitively understand concepts like gradient descent and cost functions - no tough math, just insight.You'll also get:Practice quizzesDownloadable videosJupyter notebooks to code alongIn the final section, you'll explore unsupervised learning, including:ClusteringMarket Basket AnalysisPrincipal Component Analysis (PCA)These topics involve heavy math, but here, you'll just focus on the core intuition and practical use. You'll learn which technique to use and when, without memorizing formulas.With just 2.5 hours of content, this course is designed to be concise, giving you maximum intuition, hands-on practice, and real insights in the least amount of time.
Overview
Section 1: Configuring Python and Visual Studio Code
Lecture 1 Downloading and Installing Python Without Path Errors
Lecture 2 Configuring Visual Studio Code: Connecting VS Code with Python
Lecture 3 Installing Python Packages: VS Code and Command Prompt
Lecture 4 Formatting Jupyter Notebook
Section 2: Learning the Basics of Python
Lecture 5 Opening CSV Files: CSV Module vs Pandas
Lecture 6 Exploring Data
Lecture 7 Selecting Data
Lecture 8 Methods vs Attributes
Section 3: Supervised Learning: Classification
Lecture 9 K-Nearest Neighbors (Classification Through Voting!)
Lecture 10 Decision Trees (Classification By Splitting Data)
Lecture 11 Random Forests: Combining Multiple Decision Trees In Parallel
Lecture 12 Regression Trees: Combining Multiple Decision Trees Sequentially
Section 4: Supervised Learning: Prediction
Lecture 13 Linear Regression: Intuition
Lecture 14 Linear Regression: Minimum Cost Point
Lecture 15 Linear Regression with Gradient Descent
Lecture 16 Ridge Regression: Solving Linear Regression's Overfit Problem
Lecture 17 Lasso Regression: Automatic Feature Selection
Lecture 18 ElasticNet Regression: Combining Ridge and Lasso Regressions
Lecture 19 Support Vector Machines: Transforming Data Across Dimensions
Section 5: Unsupervised Learning
Lecture 20 Market Basket Analysis: Finding Patterns and Relationships in Sales Data
Lecture 21 Principal Component Analysis: Retaining Most Important Information
Lecture 22 Clustering: K-Means Vs Agglomerative Clustering
This course is suited for people who do not have a computer science background - ideally business students who want to gather an intuition and just enough knowledge to use machine learning.
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