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

A Beginner's Guide to Machine Learning (in Python), available at $59.99, has an average rating of 3.45, with 49 lectures, 6 quizzes, based on 78 reviews, and has 1193 subscribers.

You will learn about Understand Machine Learning, Data Mining, Big Data, Data Science, and Data Analytics Learn a little bit of coding in Python Learn Support Vector Machine, Linear Regression, Logistic Regression, K-Nearest Neighbor, Decision Trees, and Neural Networks Learn how to preprocess a dataset Learn how to handle categorical features Learn how to handle unbalanced datasets Understand the different validation methods Understand feature selection and dimensionality reduction Understand hyperparameter optimization This course is ideal for individuals who are Anyone who wants to learn the basics of Machine Learning It is particularly useful for Anyone who wants to learn the basics of Machine Learning.

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

Summary

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

Price: $59.99

Average Rating: 3.45

Number of Lectures: 49

Number of Quizzes: 6

Number of Published Lectures: 49

Number of Published Quizzes: 6

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Understand Machine Learning, Data Mining, Big Data, Data Science, and Data Analytics
Learn a little bit of coding in Python
Learn Support Vector Machine, Linear Regression, Logistic Regression, K-Nearest Neighbor, Decision Trees, and Neural Networks
Learn how to preprocess a dataset
Learn how to handle categorical features
Learn how to handle unbalanced datasets
Understand the different validation methods
Understand feature selection and dimensionality reduction
Understand hyperparameter optimization

Who Should Attend

Anyone who wants to learn the basics of Machine Learning

Target Audiences

Anyone who wants to learn the basics of Machine Learning

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 Science and 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 Machine Learning 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!!

Please feel free to ask me any question! Don’t like the course? Ask for a 30-day refund!!

Real Testaments –>

1) “Excellent course!! Dana is very knowledgeable about Machine Learning, and is able to present the concepts and practices in a way that is easy to understand, along with actionable exercises to implement and practice. The presentation is very detailed and direct. A topic is introduced, explained, displayed with example and then we began implementing it.” — Joseph, 5 star rating

2) “The instructor gives a very basic explanation for complicated material. that makes it very easy for me to understand given that I already studied that in a master class but I understand it better here. Thank you” — Fatimah, 5 star rating

3) “I think it was a very useful begginner’s guide to Machine Learning using Python. I learned a lot !. Thanks” — Hernan, 4 star rating

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Outline

Lecture 2: Practical Exercises

Lecture 3: Machine Learning

Lecture 4: Exploratory Data Analysis

Lecture 5: Introduction to Python

Lecture 6: Descriptive Statistics and Histograms in Python

Lecture 7: Spambase Dataset

Lecture 8: Dataset Resources

Chapter 2: Algorithms

Lecture 1: Model Evaluation

Lecture 2: Linear Regression

Lecture 3: Support Vector Machine

Lecture 4: Support Vector Machine in Python

Lecture 5: K-Nearest Neighbor

Lecture 6: K-Nearest Neighbor in Python

Lecture 7: Decision Trees

Lecture 8: Decision Trees in Python

Lecture 9: Logistic Regression

Lecture 10: Neural Networks

Lecture 11: Neural Networks in Python

Lecture 12: Ensemble Learning

Lecture 13: Ensemble Learning in Python

Lecture 14: Energy Efficiency Dataset

Lecture 15: Regression Problem in Python

Lecture 16: Hyperparameters

Lecture 17: Hyperparameters vs. Parameters Examples

Lecture 18: Kernels and Learning Rate vs. Momentum

Chapter 3: Model Performance

Lecture 1: Performance Metrics

Lecture 2: Overfitting vs. Underfitting

Chapter 4: Data Preprocessing

Lecture 1: Data Cleaning

Lecture 2: Data Transformation

Lecture 3: Data Transformation in Python

Lecture 4: Categorical Features

Lecture 5: Unbalanced Data

Chapter 5: Other

Lecture 1: Validation Methods

Lecture 2: The Holdout Method and Confusion Matrix in Python

Lecture 3: The K-Fold Method and Cleaning the Data

Lecture 4: Classifying New Observations

Lecture 5: Feature Selection

Lecture 6: Feature Selection in Python

Lecture 7: Dimensionality Reduction

Lecture 8: Principle Component Analysis in Python

Lecture 9: Hyperparameter Optimization

Lecture 10: Grid Search Optimization in Python, Part #1

Lecture 11: Grid Search Optimization in Python, Part #2

Lecture 12: Grid Search Optimization in Python, Part #3

Chapter 6: BONUS OFFER!!

Lecture 1: Bonus Lecture: Discounted Coupons

Chapter 7: Appendix

Lecture 1: Big Data

Lecture 2: Data Science

Lecture 3: Data Analytics

Instructors

Curiosity for Data Science
Architect and Industrial Engineer

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 17 votes
4 stars: 21 votes
5 stars: 35 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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