Introduction to Machine Learning with Python

Introduction to Machine Learning with Python, available at $19.99, has an average rating of 4.94, with 30 lectures, based on 8 reviews, and has 180 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner Python developers curious about data science It is particularly useful for Beginner Python developers curious about data science.

Enroll now: Introduction to Machine Learning with Python

Summary

Title: Introduction to Machine Learning with Python

Price: $19.99

Average Rating: 4.94

Number of Lectures: 30

Number of Published Lectures: 29

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Beginner Python developers curious about data science

Target Audiences

Beginner Python developers curious about data science

Update(11/11/2023): Convolutional Neural Network Lecture in added.

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 Regression
K-Nearest Neighbor
Support Vector Machines
K-Means Clustering
Decision Tree

These are the basic topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Intro

Chapter 2: Introduction and Setup

Lecture 1: Environment Setup

Chapter 3: Introduction to Statistics for Machine Learning

Lecture 1: Getting Started

Lecture 2: Mean, Median, and Mode

Lecture 3: What is Standard Deviation?

Lecture 4: What are Percentiles?

Lecture 5: Scatter plot

Chapter 4: Linear Regression

Lecture 1: Data Preparation

Lecture 2: How Linear Regression Works?

Lecture 3: Linear Regression Example: Student Performance Estimation

Lecture 4: Saving Model & Plotting Data

Lecture 5: Resources Related to the Assignment 1

Lecture 6: Logistic Regression

Chapter 5: K Nearest Neighbors Classification

Lecture 1: Irregular Data

Lecture 2: How K Nearest Neighbors works?

Lecture 3: K-NN Implementation

Lecture 4: K-NN Regression Example

Lecture 5: K-NN Classification Example

Chapter 6: Support Vector Machine

Lecture 1: Using Sklearn Datasets

Lecture 2: How SVM Works?

Lecture 3: SVM Implementation

Chapter 7: K Means Clustering

Lecture 1: How K Means Clustering Works?

Lecture 2: K Means Clustering Implementation

Lecture 3: Resources related to the K-Means Assignment

Chapter 8: Decision Tree

Lecture 1: How Decision Tree Works?

Lecture 2: Decision Tree Implementation

Lecture 3: Decision Tree Example

Lecture 4: Downloadable resource for DT Assignment + Solution

Chapter 9: Convolutional Neural Networks

Lecture 1: CNNs and Deep Learning

Instructors

Dr. Zahra Golrizkhatami
Assistant Professor at Antalya Bilim University

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4 stars: 1 votes
5 stars: 7 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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