2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp

2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp, available at $69.99, has an average rating of 4.18, with 394 lectures, 35 quizzes, based on 185 reviews, and has 1415 subscribers.

You will learn about Define what Machine Learning does and its importance Learn the different types of Descriptive Statistics Apply and use Various Operations in Python Explore the usage of Two Categories of Supervised Learning Learn the difference of the Three Categories of Machine Learning Understand the Role of Machine Learning Explain the meaning of Probability and its importance Define how Probability Process happen Discuss the definition of Objectives and Data Gathering Step Know the different concepts of Data Preparation and Data Exploratory Analysis Step Define what is Supervised Learning Differentiate Key Differences Between Supervised,Unsupervised,and Reinforced Learning Explain the importance of Linear Regression Learn the different types of Logistic Regression Learn what is an Integrated Development Environment and its importance Understand the factors why Developers use Integrated Development Environment Learn the most important factors on How to Perform Addition operation and close Jupyter Notebook Discuss Arithmetic Operation in Python Identify the different Types of Built-in-Data Types in Python Learn the most important considerations of Dictionaries-Built-in Data types Explain the usage of Operations in Python and its importance Understand the importance of Logical Operators Define the different types of Controlled Statements Be able to create and write a program to find maximum number Differentiate the different types of range functions in Python Explain what is Statistics, Probability and key concepts Introduction to Python Date and Time in Python Sets and Trigonometry Logarithmic in Python Arrays in Python Round off, and Complex Numbers Strings in Python Strings, ord, and chr Lists in Python Tuples in Python Multiple Sequences Loops and List in Python Appending Sequences Comprehension in Python List, Item and Iterators Zip and Attributes in Python Mapping in Python dir Attributes Zip and Map Operator Printing Dictionaries Items Arguments and Functions in Python Sequences in Python Defining Functions Changer Function def in Python Knownly Type of a Function def Statementdef Statement String Code, and Sum Tree Sum Tree Echo and Lambda Function Schedule Function def and Reducing Function in Python for and if in Range def Saver and ASCII, and Exception Get Attributes and Decorator in Python Turtle and Compilation Logging and HTTP Make Calculator Binary Numbers in Python Countdown Time in Python Size and Path of a File Data Visualization Pandas Library Encoding and Decoding in Python Shelve in Python This course is ideal for individuals who are Anyone interested in the field of Machine Learning and key concepts or People who want to understand ML and build models in Python or For those who have interest in Python or For those who want to build their career in programming languages like python It is particularly useful for Anyone interested in the field of Machine Learning and key concepts or People who want to understand ML and build models in Python or For those who have interest in Python or For those who want to build their career in programming languages like python.

Enroll now: 2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp

Summary

Title: 2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp

Price: $69.99

Average Rating: 4.18

Number of Lectures: 394

Number of Quizzes: 35

Number of Published Lectures: 394

Number of Published Quizzes: 35

Number of Curriculum Items: 429

Number of Published Curriculum Objects: 429

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Define what Machine Learning does and its importance
Learn the different types of Descriptive Statistics
Apply and use Various Operations in Python
Explore the usage of Two Categories of Supervised Learning
Learn the difference of the Three Categories of Machine Learning
Understand the Role of Machine Learning
Explain the meaning of Probability and its importance
Define how Probability Process happen
Discuss the definition of Objectives and Data Gathering Step
Know the different concepts of Data Preparation and Data Exploratory Analysis Step
Define what is Supervised Learning
Differentiate Key Differences Between Supervised,Unsupervised,and Reinforced Learning
Explain the importance of Linear Regression
Learn the different types of Logistic Regression
Learn what is an Integrated Development Environment and its importance
Understand the factors why Developers use Integrated Development Environment
Learn the most important factors on How to Perform Addition operation and close Jupyter Notebook
Discuss Arithmetic Operation in Python
Identify the different Types of Built-in-Data Types in Python
Learn the most important considerations of Dictionaries-Built-in Data types
Explain the usage of Operations in Python and its importance
Understand the importance of Logical Operators
Define the different types of Controlled Statements
Be able to create and write a program to find maximum number
Differentiate the different types of range functions in Python
Explain what is Statistics, Probability and key concepts
Introduction to Python
Date and Time in Python
Sets and Trigonometry
Logarithmic in Python
Arrays in Python
Round off, and Complex Numbers
Strings in Python
Strings, ord, and chr
Lists in Python
Tuples in Python
Multiple Sequences
Loops and List in Python
Appending Sequences
Comprehension in Python
List, Item and Iterators
Zip and Attributes in Python
Mapping in Python
dir Attributes
Zip and Map Operator
Printing Dictionaries Items
Arguments and Functions in Python
Sequences in Python
Defining Functions
Changer Function
def in Python
Knownly Type of a Function
def Statementdef Statement
String Code, and Sum Tree
Sum Tree
Echo and Lambda Function
Schedule Function
def and Reducing Function in Python
for and if in Range
def Saver and ASCII, and Exception
Get Attributes and Decorator in Python
Turtle and Compilation
Logging and HTTP
Make Calculator
Binary Numbers in Python
Countdown Time in Python
Size and Path of a File
Data Visualization
Pandas Library
Encoding and Decoding in Python
Shelve in Python

Who Should Attend

Anyone interested in the field of Machine Learning and key concepts
People who want to understand ML and build models in Python
For those who have interest in Python
For those who want to build their career in programming languages like python

Target Audiences

Anyone interested in the field of Machine Learning and key concepts
People who want to understand ML and build models in Python
For those who have interest in Python
For those who want to build their career in programming languages like python

Course 1: Python Machine Learning > Section 1 – Section 68

Course 2: Python Bootcamp 30 Hours Of Step By Step > Section 69 – 94

Everything you get with this 2 in 1 course:

234-page Machine Learning workbook containing all the reference material
44 hours of clear and concise step by step instructions, practical lessons and engagement
25 Python coding files so you can download and follow along in the bootcamp to enhance your learning
35 quizzes and knowledge checks at various stages to test your learning and confirm your growth
Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificate

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn this skill.

What will you learn:

Define what Machine Learning does and its importance
Understand the Role of Machine Learning
Explain what is Statistics
Learn the different types of Descriptive Statistics
Explain the meaning of Probability and its importance
Define how Probability Process happens
Discuss the definition of Objectives and Data Gathering Step
Know the different concepts of Data Preparation and Data Exploratory Analysis Step
Define what is Supervised Learning
Differentiate Key Differences Between Supervised, Unsupervised, and Reinforced Learning
Learn the difference between the Three Categories of Machine Learning
Explore the usage of Two Categories of Supervised Learning
Explain the importance of Linear Regression
Learn the different types of Logistic Regression
Learn what is an Integrated Development Environment and its importance
Understand the factors why Developers use Integrated Development Environment
Learn the most important factors on How to Perform Addition operations and close the Jupyter Notebook
Apply and use Various Operations in Python
Discuss Arithmetic Operation in Python
Identify the different types of Built-in-Data Types in Python
Learn the most important considerations of Dictionaries-Built-in Data types
Explain the usage of Operations in Python and its importance
Understand the importance of Logical Operators
Define the different types of Controlled Statements
Be able to create and write a program to find the maximum number
…and more!

Contents and Overview

You’ll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.

Then you will learn about Defining Objectives and Data Gathering Step; Data Preparation and Data Exploratory Analysis Step; Building a Machine Learning Model and Model Evaluation; Prediction Step in the Machine Learning Process; How can a machine solve a problem-Lecture overview; What is Supervised Learning; What is Unsupervised Learning; What is Reinforced Learning; Key Differences Between Supervised,Unsupervised and Reinforced Learning; Three Categories of Machine Learning; What is Regression, Classification and Clustering; Two Categories of Supervised Learning; Category of Unsupervised Learning; Comparison of Regression , Classification and Clustering; What is Linear Regression; Advantages and Disadvantages of Linear Regression; Limitations of Linear Regression; What is Logistic Regression; Comparison of Linear Regression and Logistic Regression; Types of Logistic Regression; Advantages and Disadvantages of Logistic Regression; Limitations of Logistic Regression; What is Decision tree and its importance in Machine learning; Advantages and Disadvantages of Decision Tree.

We will also cover What is Integrated Development Environment; Parts of Integrated Development Environment; Why Developers Use Integrated Development Environment; Which IDE is used for Machine Learning; What are Open Source IDE; What is Python; Best IDE for Machine Learning along with Python; Anaconda Distribution Platform and Jupyter IDE; Three Important Tabs in Jupyter; Creating new Folder and Notebook in Jupyter; Creating Three Variables in Notebook; How to Check Available Variables in Notebook; How to Perform Addition operation and Close Jupyter Notebook; How to Avoid Errors in Jupyter Notebook; History of Python; Applications of Python; What is Variable-Fundamentals of Python; Rules for Naming Variables in Python; DataTypes in Python; Arithmetic Operation in Python; Various Operations in Python; Comparison Operation in Python; Logical Operations in Python; Identity Operation in Python; Membership Operation in Python; Bitwise Operation in Python; Data Types in Python; Operators in Python; Control Statements in Python; Libraries in Python; Libraries in Python; What is Scipy library; What is Pandas Library; What is Statsmodel and its features;

This course will also tackle Data Visualisation & Scikit Learn; What is Data Visualization; Matplotib Library; Seaborn Library; Scikit-learn Library; What is Dataset; Components of Dataset; Data Collection & Preparation; What is Meant by Data Collection; Understanding Data; Exploratory Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing; Categorical Variables; Data Pre-processing Techniques.

This course will also discuss What is Linear Regression and its Use Case; Dataset For Linear Regression; Import library and Load Data set- steps of linear regression; Remove the Index Column-Steps of Linear Regression; Exploring Relationship between Predictors and Response; Pairplot method explanation; Corr and Heatmap method explanation; Creating Simple Linear Regression Model; Interpreting Model Coefficients; Making Predictions with our Model; Model Evaluation Metric; Implementation of Linear Regression-lecture overview; Uploading the Dataset in Jupyter Notebook; Importing Libraries and Load Dataset into Dataframe; Remove the Index Column; Exploratory Analysis -relation of predictor and response; Creation of Linear Regression Model; Model Coefficients; Making Predictions; Evaluation of Model Performance.

Next, you will learn about Model Evaluation Metrics and Logistic Regression – Diabetes Model.

Who are the Instructors?

Samidha Kurle from Digital Regenesys is your lead instructor – a professional making a living from her teaching skills with expertise in Machine Learning. She has joined with content creator Peter Alkema to bring you this amazing new course.

You’ll get premium support and feedback to help you become more confident with finance!

Our happiness guarantee…

We have a 30-day 100% money-back guarantee,so if you aren’t happy with your purchase, we will refund your course – no questions asked!

We can’t wait to see you on the course!

Enrol now, and master Machine Learning!

Peter and Samidha

Course Curriculum

Chapter 1: Introduction

Lecture 1: Python Machine Learning – Introduction

Lecture 2: Course Overview On A Wipeboard: Mindmap Of Machine Learning In Python

Lecture 3: Introduce Yourself to Your Fellow Students And Tell Everyone What are Your Goals

Lecture 4: Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!

Lecture 5: Preview & Download The 234 Page Machine Learning Workbook You Get In This Course

Chapter 2: Introduction to Machine Learning

Lecture 1: Introduction of Instructor

Lecture 2: Machine Learning Lecture Outline

Lecture 3: Understanding of Thinking and Learning Process in Humans

Lecture 4: How Humans Think and Why we Need Machine Learning

Lecture 5: History of Machine Learning

Lecture 6: Difference Between Traditional Programming and Machine Learning

Lecture 7: Machine Learning Example

Chapter 3: Knowledge Check 1

Chapter 4: What Is Machine Learning

Lecture 1: What does Machine Learning do

Lecture 2: Definition of Machine Learning

Lecture 3: Apply Apple Sorting Example Experiences

Lecture 4: Role of Machine Learning

Lecture 5: Machine Learning Key Terms

Chapter 5: Knowledge Check 2

Chapter 6: Statistics

Lecture 1: What is Statistics

Lecture 2: Basic Terminologies of Statistics

Lecture 3: Descriptive Statistics-Types of Statistics

Lecture 4: Types of Descriptive Statistics

Lecture 5: What is Inferential Statistics

Lecture 6: What is Analysis and its types

Chapter 7: Knowledge Check 3

Chapter 8: Probability

Lecture 1: Introduction to Probability

Lecture 2: Probability and Real life Examples

Lecture 3: What is Probability

Lecture 4: How Probability is a Process

Lecture 5: Calculate Probability of an Event-Example

Lecture 6: Probability of One Fair Six-Sided Die-Example

Lecture 7: Views of Probability

Lecture 8: Base Theory of Probability

Lecture 9: Rain chances on a picnic day-Probability Example

Chapter 9: Knowledge Check 4

Chapter 10: Machine Learning Quiz 1

Chapter 11: Machine Learning Process

Lecture 1: Defining Objectives and Data Gathering Step

Lecture 2: Data Preparation and Data Exploratory Analysis Step

Lecture 3: Building a Machine Learning Model and Model Evaluation

Lecture 4: Prediction Step in the Machine Learning Process

Chapter 12: Knowledge Check 5

Chapter 13: Types of Machine Learning

Lecture 1: How can a machine solve a problem-Lecture overview

Lecture 2: What is Supervised Learning

Lecture 3: What is Unsupervised Learning

Lecture 4: What is Reinforced Learning

Lecture 5: Key Differences Between Supervised,Unsupervised and Reinforced Learning

Chapter 14: Knowledge Check 6

Chapter 15: Machine Learning Algorithms Part 1

Lecture 1: Three Categories of Machine Learning

Lecture 2: What is Regression, Classification and Clustering

Lecture 3: Two Categories of Supervised Learning

Lecture 4: Category of Unsupervised Learning

Lecture 5: Comparison of Regression , Classification and Clustering

Chapter 16: Knowledge Check 7

Chapter 17: Machine Learning Algorithms Part 2

Lecture 1: What is Linear Regression

Lecture 2: Advantages and Disadvantages of Linear Regression

Lecture 3: Limitations of Linear Regression

Lecture 4: You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>

Lecture 5: What is Logistic Regression

Lecture 6: Comparison of Linear Regression and Logistic Regression

Lecture 7: Types of Logistic Regression

Lecture 8: Advantages and Disadvantages of Logistic Regression

Lecture 9: Limitations of Logistic Regression

Lecture 10: What is Decision tree and its importance in Machine learning

Lecture 11: Advantages and Disadvantages of Decision Tree

Chapter 18: Knowledge Check 8

Chapter 19: Machine Learning Algorithms Part 3

Lecture 1: Machine Learning Algorithms Part 3

Chapter 20: Knowledge Check 9

Chapter 21: Machine Learning Quiz 2

Chapter 22: Model Building Platform

Lecture 1: What is Integrated Development Environment

Lecture 2: Parts of Integrated Development Environment

Lecture 3: Why Developers Use Integrated Development Environment

Lecture 4: Which IDE is used for Machine Learning

Lecture 5: What are Open Source IDE

Lecture 6: What is Python

Lecture 7: Best IDE for Machine Learning along with Python

Lecture 8: Anaconda Distribution Platform and Jupyter IDE

Chapter 23: Knowledge Check 10

Instructors

Peter Alkema
Business | Technology | Self Development
Regenesys Business School
Regenesys Business School

Rating Distribution

1 stars: 9 votes
2 stars: 10 votes
3 stars: 24 votes
4 stars: 49 votes
5 stars: 93 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.

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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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