Machine Learning with Python Basics (For Beginners)

Name: Machine Learning with Python Basics (For Beginners)
Rating: 4.4 (20 reviews)

You will Learn the Basics of Machine Learning with Python step by step (First Step For Beginners)

Created byMohamed Gamal

Last updated 10/2023

English

What you'll 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

Course content

6 sections • 52 lectures • 5h 58m total length

Introduction1:54
Explain machine learning basics, differentiate supervised and unsupervised learning, compare linear and logistic regression with regularization, and outline Python basics, data import, and model training.

What is Machine Learning - Types of Machine Learning10:40
Linear Regression With One Variable4:55
Linear Regression With One Variable (Cost Function - Gradient Descent)16:59
Linear Regression With Multible Variable9:33
Logistic Regression (Classification)9:39
Logistic Regression (Cost Function - Gradient Descent)5:49
Logistic Regression (Multiclass)2:12
Explore multiclass classification and the one versus all approach, illustrated with examples like email filtering and weather categories, showing how each class is trained against the rest.
Regularization Overfitting8:19
Regularization (Linear and Logistic Regression)4:24
Neural Network Overview11:18
Neural Network (Cost Function)2:40
Advice for Applying Machine Leaning16:01
Unsupervised Machine Learning5:49

How to print4:08
Variables4:25
Explore variables in Python and how they store temporary data. Learn types like float for decimals, integer for whole numbers, string for text, and boolean for true or false.
Receive Input from User5:25
Type Conversion8:07
Demonstrate type conversion by turning user input from string to integer to compute age, and show how to handle errors when mixing integer and string types.
String7:28
Formatted String5:49
Learn how to create and use a formatted string in Python, combining first and last names into a full name using format strings and string formatting techniques.
String Methods4:59
Arithmetic Operations9:19
Math Functions3:23
Master how to use Python maths functions from the math module, including round, floor, ceiling, and abs, with practical examples.
If Statement4:51
If Statement Another Example2:59
Logical Operators6:17
Comparison Operators7:53
While4:49
For Loops7:47
Nested Loops2:39
Master nested loops in Python by building a loop inside a loop to generate coordinate pairs, using for loops with range and print to format x, y outputs.
List6:31
2D List5:13
Explore two dimensional lists in Python by treating a matrix as a list of lists, accessing and modifying elements, and printing all items using a nested loop.
List Methods7:43
Tuples2:30
Unpacking2:24
Explore Python unpacking by assigning x, y, z from a coordinates list and printing results to see values one, two, three.
Dictionaries7:10
Functions5:51
Parameters6:40
Explore how parameters pass information to functions, distinguish between the print function and user-defined functions, and use arguments to customize greetings with first and last names.
Keyword Arguments5:37
Explore the distinction between position and keyword arguments in Python, learning how keyword arguments improve readability by clearly labeling parameters like first name and last name.
Return Statement3:18
Try - Except6:13
Comments2:43
Learn how to use Python comments to remind yourself of assumptions and reasons for steps, while avoiding unnecessary or repetitive notes that clutter the code.
Classes6:03
General Review 17:06
Explore a quick Python variable review: define x, y, z with different types, print their types, and learn variable naming rules, case sensitivity, underscores, and multi-variable assignments.
General Review 29:10

Requirements

No Programming Experience Needed

Description

Steps of Machine Learning that you Will learn:

Import the data.
Split data into Training & Test.
Create a Model.
Train The Model.
Make Predictions.
Evaluate and improve.

Machine Learning Course Contents:

What is Machine Learning - Types of Machine Learning (Supervised & Unsupervised).
Linear Regression with One Variable.
Linear Regression with One Variable (Cost Function - Gradient Descent).
Linear Regression with Multiple Variable.
Logistic Regression (Classification).
Logistic Regression (Cost Function - Gradient Descent).
Logistic Regression (Multiclass).
Regularization Overfitting.
Regularization (Linear and Logistic Regression).
Neural Network Overview.
Neural Network (Cost Function).
Advice for Applying Machine Leaning.
Machine Learning Project 1
Machine Learning Project 2

Python Basics Course Contents:

How to print
Variables
Receive Input from User
Type Conversion
String
Formatted String
String Methods
Arithmetic Operations
Math Functions
If Statement
Logical Operators
Comparison Operators
While
For Loops
Nested Loops
List
2D List
List Methods
Tuples
Unpacking
Dictionaries
Functions
Parameters
Keyword Arguments
Return Statement
Try - Except
Comments
Classes

Notes:

You will Learn the basics of Machine Learning.
You will learn the basics of python.
You will need to setup Anaconda.
You will need to setup python & PyCharm
This course is considered as first step for the Machine Learning.
You can ask anytime.
No Programming Experience Needed for this course.
Python for Data Science and Machine Learning is a great course that you can take to learn the implementation of ML models in Python.
This course considered as step one in the Machine Leaning, You will learn the concept of the Machine Learning with python basics.

Who this course is for:

Beginner in Machine Learning

Machine Learning with Python Basics (For Beginners)

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 2min

Machine Learning with Python Basics13 lectures • 1hr 48min

Machine Learning Projects3 lectures • 49min

Python Basics31 lectures • 2hr 55min

Python Projects3 lectures • 24min

Bonus1 lecture • 1min

Requirements

Description

Who this course is for: