Machine Learning With Python - 2022

K-NN, Linear Regression, SVM, K Means Clustering and Decision Tree

What you'll learn
You will learn data science, pattern recognition and machine learning all using Python.
Have a great intuition of many Machine Learning models
Implement popular Machine Learning Algorithms such as KNN, SVM, Linear Regression, K Means Clustering and Decision Tree
Know which Machine Learning model to choose for each type of problem
Requirements
Basic knowledge of Python programming
You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
Description
Are you interested in the field of machine learning? Then you have come to the right place and this course is exactly what you need! In this course, you will learn the basics of various popular machine learning approaches through several practical examples. Various machine learning algorithms such as K-NN, Linear Regression, SVM, K-Means Clustering, and Decision Tree will be explained and implemented in Python. In this course, I try to share my knowledge and teach you the basics of the theories, algorithms, and programming libraries in a simple way. I will guide you step by step on your journey into the world of machine learning.Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. This course will teach you the basic techniques used by real-world industry data scientists. I'll cover the fundamentals of machine learning techniques  that are essential for real-world problems, including:Linear RegressionK-Nearest NeighborSupport Vector MachinesK-Means ClusteringDecision TreeThese are the basic topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

Overview

Section 1: Introduction

Lecture 1 Course Intro

Section 2: Introduction and Setup

Lecture 2 Environment Setup

Section 3: Linear Regression

Lecture 3 Data Preparation

Lecture 4 How Linear Regression Works?

Lecture 5 Linear Regression Example: Student Performance Estimation

Lecture 6 Saving Model & Plotting Data

Section 4: K Nearest Neighbors Classification

Lecture 7 Irregular Data

Lecture 8 How K Nearest Neighbors works?

Lecture 9 K-NN Implementation

Section 5: Support Vector Machine

Lecture 10 Using Sklearn Datasets

Lecture 11 How SVM Works?

Lecture 12 SVM Implementation

Section 6: K Means Clustering

Lecture 13 How K Means Clustering Works?

Lecture 14 K Means Clustering Implementation

Section 7: Decision Tree

Lecture 15 How Decision Tree Works?

Lecture 16 Decision Tree Implementation

Beginner Python developers curious about data science