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

Name: 2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp
Rating: 4.2 (256 reviews)

Learn model building, algorithms, data science PLUS 30 hours of step by step coding, libraries, arguments, projects +++

Created byPeter Alkema, Regenesys Business School

Last updated 11/2024

English

What you'll 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

Course content

96 sections • 394 lectures • 44h 46m total length

Python Machine Learning - Introduction4:37
Welcome to this course where you will gain practical skills and understand important concepts in machine learning. Within the broader field of artificial intelligence is Machine learning, which allows computers to act like humans, because they are able to improve their behaviour as they encounter more data. This courses starts with a foundational understanding of statistics and probability as well as an introduction to machine learning, then you will learn the machine learning process, different types of machine learning and then we get into the algorithms and a machine learning model building platform.
Course Overview On A Wipeboard: Mindmap Of Machine Learning In Python19:43
In this wipeboard lesson, I draw a mindmap of this Machine Learning in Python course. We go through an overview of all the key topics in the course so that you know exactly what to anticipate and how best to plan your learning journey through the content.
Introduce Yourself to Your Fellow Students And Tell Everyone What are Your Goals0:17
This is your opportunity to share something about yourself with the rest of the students in this course. Tell us all about your goals and what you want to achieve. You can come back to this board and add more thoughts as you go through the course and achieve your goals. Seeing all the other students in the course will also motivate you and keep you going as you participate in this community of learning.
Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!1:20
Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>
Preview & Download The 234 Page Machine Learning Workbook You Get In This Course5:42
In this lesson we step through the master workbook that you get with this course. It contains all the reference material which you can easily access and download for your journey with machine learning. Each of the pages are also attached as worksheets to the individual lessons where they are taught, which means you can also download them individually depending on your requirements.

Introduction of Instructor1:12
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.
Machine Learning Lecture Outline0:16
In the upcoming lectures, you will be able to learn 1. Learning and thinking 2. History of machine learning 3. Machine learning examples.
Understanding of Thinking and Learning Process in Humans2:10
In this lecture, we will understand what is learning? We will also understand what is thinking? We will give you a brief overview of learning and thinking in machine learning.
How Humans Think and Why we Need Machine Learning2:26
In this lecture, WE will understand how do we think and what are the needs for machine learning? Moreover, we will learn how does a machine works and what are the salient features under which a machine regulates its functions under human command.
History of Machine Learning1:59
In this lecture, we will learn about the history of machine learning. Where it was started and who have a big contribution to its evolution and formation.
Difference Between Traditional Programming and Machine Learning1:23
In this lecture, we will learn about the difference between traditional programming and machine learning. We will learn how traditional programming works? and how machine learning has its relative impact?
Machine Learning Example2:04
In this lecture, we will give you some examples of machine learning. Like, as Facebook recommendations of different people which you may know already.

What does Machine Learning do1:38
In this lecture, we will learn what is machine learning? And what are the key terms of machine learning? We will learn what does machine learning do in detail. We will describe the important feature of machine learning about its working and functionality.
Definition of Machine Learning0:59
In this lecture, we will define machine learning by data scientists "Arthur Samuel" and "Tom Mitchell". We will learn what they said about what is machine learning?
Apply Apple Sorting Example Experiences4:27
In this lecture, we will give some examples to understand machine learning. The examples of machine learning will clarify your concepts and strengthen your thinking about machine learning.
Role of Machine Learning0:57
You will learn the role of machine learning. You will learn where it is applicable and how does it affect facilitate the human virtual experience.
Machine Learning Key Terms4:33
In this lecture, we shall understand the key features of machine learning. We shall describe the algorithm, model, variables, and data in a detailed way.

What is Statistics1:49
In this lecture, you will learn what is statistics. You will understand the different terms that are being used in the definition of statistics.
Basic Terminologies of Statistics2:02
In this lecture, you will understand the basics terminology of statistics like population, and sample. We will describe what is population and sample?
Descriptive Statistics-Types of Statistics3:10
In this lecture, we will understand the type of statistics. Also, we will explain what is descriptive and inferential statistics with examples?
Types of Descriptive Statistics1:31
In this lecture, we will learn about the types of descriptive statistics. We shall explain central tendency and dispersion along with their related sub-branches.
What is Inferential Statistics0:47
In this lecture, we will explain inferential statistics. We will also explain the important steps of inferential statistics.
What is Analysis and its types2:56
In this lecture, we will understand the analysis and types of analysis. We will also learn about quantitative and qualitative analysis. Moreover, we shall explain the summary of the last lectures.

Introduction to Probability1:03
In this lecture, we shall explain the outline and introduction to probability. We shall describe what content will be taught in the upcoming lectures. However, this lecture includes an introduction to probability as well.
Probability and Real life Examples1:23
We will define what is the probability. We explain how probability is connected with machine learning. We will also give you examples of probability.
What is Probability0:52
In this lecture, we will describe the mathematical definition of probability. You will also learn the formula to find the probability in a detailed way.
How Probability is a Process1:52
In this lecture, we will learn how probability is a process? We shall also learn about the random experiments and sample space.
Calculate Probability of an Event-Example1:47
Here, in this lecture, we are taking an example to understand probability. We will calculate the probability of an event when all outcomes in the sample space are equally likely.
Probability of One Fair Six-Sided Die-Example1:10
In this lecture, we are taking another example to understand probability. We will calculate the probability of a fair six-sided die with the number 1,2,3,4,5,6 on its face.
Views of Probability2:51
In this lecture, we shall understand the subjective and objective views of probability. We shall explain what is a subjective view of probability and an objective view of probability are in a detailed way.
Base Theory of Probability1:17
In this lecture, we shall understand the base formula of probability. We shall explain the base formula with events A and B with restricted conditional probability.
Rain chances on a picnic day-Probability Example2:41
In this lecture, we shall find the chance of rain happening contemporary to different weather conditions. We shall explain how the base formula is applicable in daily life with an example of rain.

Requirements

No technical knowledge or experience is required to get going in this course
A basic understanding of the importance of data science will be useful
Laptop, or Computer, or Mobile
Internet Connection

Description

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

Who this course is for:

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

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

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 32min

Introduction to Machine Learning7 lectures • 12min

Knowledge Check 10

What Is Machine Learning5 lectures • 13min

Knowledge Check 20

Statistics6 lectures • 12min

Knowledge Check 30

Probability9 lectures • 15min

Knowledge Check 40

Machine Learning Quiz 10

Requirements

Description

Who this course is for: