Machine Learning with Python Basics (For Beginners)

Machine Learning with Python Basics (For Beginners), available at $54.99, has an average rating of 4.65, with 52 lectures, based on 19 reviews, and has 188 subscribers.

You will learn about You Will learn basic concept of Machine Learning, Types of Machine Learning. You Will learn basic concept of Linear Regression With One Variable & Multiple You Will learn basic concept of Logistic Regression You Will learn basic concept of Regularization (Linear and Logistic Regression) You Will learn basic concept of Neural Network Machine Learning Projects This course is ideal for individuals who are Beginner in Machine Learning It is particularly useful for Beginner in Machine Learning.

Enroll now: Machine Learning with Python Basics (For Beginners)

Summary

Title: Machine Learning with Python Basics (For Beginners)

Price: $54.99

Average Rating: 4.65

Number of Lectures: 52

Number of Published Lectures: 52

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 52

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

You Will learn basic concept of Machine Learning, Types of Machine Learning.

You Will learn basic concept of Linear Regression With One Variable & Multiple

You Will learn basic concept of Logistic Regression

You Will learn basic concept of Regularization (Linear and Logistic Regression)

You Will learn basic concept of Neural Network

Machine Learning Projects

Who Should Attend

Beginner in Machine Learning

Target Audiences

Beginner in Machine Learning

Steps of Machine Learning that you Will learn:

1. Import the data.

2. Split data into Training & Test.

3. Create a Model.

4. Train The Model.

5. Make Predictions.

6. Evaluate and improve.

Machine Learning Course Contents:

1. What is Machine Learning – Types of Machine Learning (Supervised & Unsupervised).

2. Linear Regression with One Variable.

3. Linear Regression with One Variable (Cost Function – Gradient Descent).

4. Linear Regression with Multiple Variable.

5. Logistic Regression (Classification).

6. Logistic Regression (Cost Function – Gradient Descent).

7. Logistic Regression (Multiclass).

8. Regularization Overfitting.

9. Regularization (Linear and Logistic Regression).

10. Neural Network Overview.

11. Neural Network (Cost Function).

12. Advice for Applying Machine Leaning.

13. Machine Learning Project 1

14. Machine Learning Project 2

Python Basics Course Contents:

1. How to print

2. Variables

3. Receive Input from User

4. Type Conversion

5. String

6. Formatted String

7. String Methods

8. Arithmetic Operations

9. Math Functions

10. If Statement

11. Logical Operators

12. Comparison Operators

13. While

14. For Loops

15. Nested Loops

16. List

17. 2D List

18. List Methods

19. Tuples

20. Unpacking

21. Dictionaries

22. Functions

23. Parameters

24. Keyword Arguments

25. Return Statement

26. Try – Except

27. Comments

28. Classes

Notes:

1. You will Learn the basics of Machine Learning.

2. You will learn the basics of python.

3. You will need to setup Anaconda.

4. You will need to setup python & PyCharm

5. This course is considered as first step for the Machine Learning.

6. You can ask anytime.

7. No Programming Experience Needed for this course.

8. Python for Data Science and Machine Learning is a great course that you can take to learn the implementation of ML models in Python.

9. This course considered as step one in the Machine Leaning, You will learn the concept of the Machine Learning with python basics.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Machine Learning with Python Basics

Lecture 1: What is Machine Learning – Types of Machine Learning

Lecture 2: Linear Regression With One Variable

Lecture 3: Linear Regression With One Variable (Cost Function – Gradient Descent)

Lecture 4: Linear Regression With Multible Variable

Lecture 5: Logistic Regression (Classification)

Lecture 6: Logistic Regression (Cost Function – Gradient Descent)

Lecture 7: Logistic Regression (Multiclass)

Lecture 8: Regularization Overfitting

Lecture 9: Regularization (Linear and Logistic Regression)

Lecture 10: Neural Network Overview

Lecture 11: Neural Network (Cost Function)

Lecture 12: Advice for Applying Machine Leaning

Lecture 13: Unsupervised Machine Learning

Chapter 3: Machine Learning Projects

Lecture 1: Anaconda Setup

Lecture 2: Machine Learning Project 1

Lecture 3: Machine Learning Project 2

Chapter 4: Python Basics

Lecture 1: How to print

Lecture 2: Variables

Lecture 3: Receive Input from User

Lecture 4: Type Conversion

Lecture 5: String

Lecture 6: Formatted String

Lecture 7: String Methods

Lecture 8: Arithmetic Operations

Lecture 9: Math Functions

Lecture 10: If Statement

Lecture 11: If Statement Another Example

Lecture 12: Logical Operators

Lecture 13: Comparison Operators

Lecture 14: While

Lecture 15: For Loops

Lecture 16: Nested Loops

Lecture 17: List

Lecture 18: 2D List

Lecture 19: List Methods

Lecture 20: Tuples

Lecture 21: Unpacking

Lecture 22: Dictionaries

Lecture 23: Functions

Lecture 24: Parameters

Lecture 25: Keyword Arguments

Lecture 26: Return Statement

Lecture 27: Try – Except

Lecture 28: Comments

Lecture 29: Classes

Lecture 30: General Review 1

Lecture 31: General Review 2

Chapter 5: Python Projects

Lecture 1: Python & PyCharm Setup

Lecture 2: Quiz Game

Lecture 3: Guessing Game

Chapter 6: Bonus

Lecture 1: Bonus

Instructors

Mohamed Gamal
BIM Project Manager & CAD CAM Specialist

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 2 votes

4 stars: 0 votes

5 stars: 16 votes

Frequently Asked Questions

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