Machine Learning Data Science A-Z- Hands-on Python 2024

Machine Learning & Data Science A-Z: Hands-on Python 2024, available at $94.99, has an average rating of 4.21, with 76 lectures, 6 quizzes, based on 1196 reviews, and has 69061 subscribers.

You will learn about Understanding the basic concepts Complete tutorial about basic packages like Numpy and Pandas Data Visualization Data Preprocessing Understanding the concept behind the algorithms Developing different kinds of Machine Learning models Knowing how to optimize your models hyperparameters Learn how to develop models based on the requirement of your future business This course is ideal for individuals who are Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic or Beginners, intermediate and even advanced students in the field of artificial intelligence, Data Science and Machine Learning or Students in college that looking for securing their future jobs or Employees that look forward to excel their job level by learning machine learning or Anyone who afraid of coding in Python but interested in Machine Learning Concepts or Any one who wants to create a new business using machine learning or Graduate students and researchers that want to apply machine learning models in their thesis and projects It is particularly useful for Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic or Beginners, intermediate and even advanced students in the field of artificial intelligence, Data Science and Machine Learning or Students in college that looking for securing their future jobs or Employees that look forward to excel their job level by learning machine learning or Anyone who afraid of coding in Python but interested in Machine Learning Concepts or Any one who wants to create a new business using machine learning or Graduate students and researchers that want to apply machine learning models in their thesis and projects.

Enroll now: Machine Learning & Data Science A-Z: Hands-on Python 2024

Summary

Title: Machine Learning & Data Science A-Z: Hands-on Python 2024

Price: $94.99

Average Rating: 4.21

Number of Lectures: 76

Number of Quizzes: 6

Number of Published Lectures: 76

Number of Published Quizzes: 6

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $124.99

Quality Status: approved

Status: Live

What You Will Learn

Understanding the basic concepts

Complete tutorial about basic packages like Numpy and Pandas

Data Visualization

Data Preprocessing

Understanding the concept behind the algorithms

Developing different kinds of Machine Learning models

Knowing how to optimize your models hyperparameters

Learn how to develop models based on the requirement of your future business

Who Should Attend

Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic

Beginners, intermediate and even advanced students in the field of artificial intelligence, Data Science and Machine Learning

Students in college that looking for securing their future jobs

Employees that look forward to excel their job level by learning machine learning

Anyone who afraid of coding in Python but interested in Machine Learning Concepts

Any one who wants to create a new business using machine learning

Graduate students and researchers that want to apply machine learning models in their thesis and projects

Target Audiences

Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic

Beginners, intermediate and even advanced students in the field of artificial intelligence, Data Science and Machine Learning

Students in college that looking for securing their future jobs

Employees that look forward to excel their job level by learning machine learning

Anyone who afraid of coding in Python but interested in Machine Learning Concepts

Any one who wants to create a new business using machine learning

Graduate students and researchers that want to apply machine learning models in their thesis and projects

Are you interested in data science and machine learning, but you don’t have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

This course is completely categorized, and we don’t start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

Chapter1: Introduction and all required installations

Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)

Chapter3: Preprocessing

Chapter4: Machine Learning Types

Chapter5: Supervised Learning: Classification

Chapter6: Supervised Learning: Regression

Chapter7: Unsupervised Learning: Clustering

Chapter8: Model Tuning

Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Content

Lecture 2: What is Machine Learning? Some Basic Terms

Lecture 3: Python Installation

Lecture 4: Python IDE

Lecture 5: IDE Installation

Lecture 6: Installation of Required Libraries

Lecture 7: Spyder Interface

Chapter 2: Machine Learning Useful Packages (Libraries)

Lecture 1: Python Source Codes

Lecture 2: NumPy1

Lecture 3: NumPy2

Lecture 4: NumPy3

Lecture 5: NumPy4

Lecture 6: NumPy5

Lecture 7: NumPy6

Lecture 8: Pandas1

Lecture 9: Pandas2

Lecture 10: Pandas3

Lecture 11: Pandas4

Lecture 12: Visualization with Matplotlib1

Lecture 13: Visualization with Matplotlib2

Lecture 14: Visualization with Matplotlib3

Lecture 15: Visualization with Matplotlib4

Lecture 16: Visualization with Matplotlib5

Chapter 3: Data Preprocessing

Lecture 1: Reading and Modifying a Dataset

Lecture 2: Statistics1

Lecture 3: Statistics2

Lecture 4: Statistics3 – Covariance

Lecture 5: Missing Values1

Lecture 6: Missing Values2

Lecture 7: Outlier Detection1

Lecture 8: Outlier Detection2

Lecture 9: Outlier Detection3

Lecture 10: Concatenation

Lecture 11: Dummy Variable

Lecture 12: Normalization

Chapter 4: Machine Learning Introduction

Lecture 1: Learning Types

Chapter 5: Supervised Learning – Classification

Lecture 1: Supervised Learning Models – Introduction and Understanding the Data

Lecture 2: k-NN Concepts

Lecture 3: k-NN Model Development

Lecture 4: k-NN Training-Set and Test-Set Creation

Lecture 5: Decision Tree Concepts

Lecture 6: Decision Tree Model Development

Lecture 7: Decision Tree – Cross Validation

Lecture 8: Naive Bayes Concepts

Lecture 9: Naive Bayes Model Development

Lecture 10: Logistic Regression Concepts

Lecture 11: Logistic Regression Model Development

Lecture 12: Model Evaluation Concepts

Lecture 13: Model Evaluation – Calculating with Python

Chapter 6: Supervised Learning – Regression

Lecture 1: Note!

Lecture 2: Simple and Multiple Linear Regression Concepts

Lecture 3: Multiple Linear Regression – Model Development

Lecture 4: Evaluation Metrics – Concepts

Lecture 5: Evaluation Metrics – Implementation

Lecture 6: Polynomial Linear Regression Concepts

Lecture 7: Polynomial Linear Regression Model Development

Lecture 8: Random Forest Concepts

Lecture 9: Random Forest Model Development

Lecture 10: Support Vector Regression Concepts

Lecture 11: Support Vector Regression Model Development

Chapter 7: Unsupervised Learning – Clustering Techniques

Lecture 1: Introduction

Lecture 2: K-means Concepts1

Lecture 3: K-means Concepts2

Lecture 4: K-means Model Development1

Lecture 5: K-means Model Development2

Lecture 6: K-means – Model Evaluation

Lecture 7: DBSCAN Concepts

Lecture 8: DBSCAN Model Development

Lecture 9: Hierarchical Clustering Concepts

Lecture 10: Hierarchical Clustering Model Development

Chapter 8: Hyper Parameter Optimization (Model Tuning)

Lecture 1: Introduction

Lecture 2: Support Vector Regression – Model Tuning

Lecture 3: K-Means – Model Tuning

Lecture 4: k-NN – Model Tuning

Lecture 5: Overfitting and Underfitting

Chapter 9: Bonus

Lecture 1: Bonus Lecture

Instructors

Navid Shirzadi, Ph.D.
Data Science & Optimization Expert

Rating Distribution

1 stars: 11 votes

2 stars: 24 votes

3 stars: 115 votes

4 stars: 416 votes

5 stars: 630 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!