Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Peter Alkema, Regenesys Business School
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
3 views
Course Overview
Learn model building, algorithms, data science PLUS 30 hours of step by step coding, libraries, arguments, projects +++
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 Content
- 95 section(s)
- 394 lecture(s)
- Section 1 Introduction
- Section 2 Introduction to Machine Learning
- Section 3 Knowledge Check 1
- Section 4 What Is Machine Learning
- Section 5 Knowledge Check 2
- Section 6 Statistics
- Section 7 Knowledge Check 3
- Section 8 Probability
- Section 9 Knowledge Check 4
- Section 10 Machine Learning Quiz 1
- Section 11 Machine Learning Process
- Section 12 Knowledge Check 5
- Section 13 Types of Machine Learning
- Section 14 Knowledge Check 6
- Section 15 Machine Learning Algorithms Part 1
- Section 16 Knowledge Check 7
- Section 17 Machine Learning Algorithms Part 2
- Section 18 Knowledge Check 8
- Section 19 Machine Learning Algorithms Part 3
- Section 20 Knowledge Check 9
- Section 21 Machine Learning Quiz 2
- Section 22 Model Building Platform
- Section 23 Knowledge Check 10
- Section 24 Jupyter Notebook
- Section 25 Knowledge Check 11
- Section 26 Python Insights
- Section 27 Knowledge Check 12
- Section 28 Data Types in Python
- Section 29 Knowledge Check 13
- Section 30 Operators in Python
- Section 31 Knowledge Check 14
- Section 32 Control Statements in Python
- Section 33 Knowledge Check 15
- Section 34 Libraries in Python
- Section 35 Knowledge Check 16
- Section 36 NumPy Part 1
- Section 37 Knowledge Check 17
- Section 38 NumPy Part 2
- Section 39 Knowledge Check 18
- Section 40 Pandas Part 1
- Section 41 Knowledge Check 19
- Section 42 Pandas Part 2
- Section 43 Knowledge Check 20
- Section 44 Pandas Part 3
- Section 45 Knowledge Check 21
- Section 46 Data Visualisation & Scikit Learn
- Section 47 Knowledge Check 22
- Section 48 Test your knowledge now to achieve your goals!
- Section 49 Matplotlib Part 1
- Section 50 Knowledge Check 23
- Section 51 Matplotlib Part 2
- Section 52 Knowledge Check 24
- Section 53 Python Coding - Seaborn Part 1
- Section 54 Knowledge Check 25
- Section 55 Python Coding - Seaborn Part 2
- Section 56 Knowledge Check 26
- Section 57 Machine Learning Quiz 3
- Section 58 Data Collection & Preparation
- Section 59 Knowledge Check 27
- Section 60 Linear Regression - Use Case
- Section 61 Knowledge Check 28
- Section 62 Linear Regression with Python
- Section 63 Knowledge Check 29
- Section 64 Model Evaluation Metrics
- Section 65 Knowledge Check 30
- Section 66 Logistic Regression - DIabetes Model
- Section 67 Knowledge Check 31
- Section 68 Machine Learning Quiz 4
- Section 69 Additional Data Science Insights: Lessons From A Live Webinar Interview
- Section 70 Python Bootcamp - Introduction
- Section 71 Introduction to Python
- Section 72 Date and Time in Python
- Section 73 Sets, Trigonometry, Logarithmic in Python
- Section 74 Arrays in Python
- Section 75 Round off, Trigonometry, and Complex Numbers in Python
- Section 76 Strings in Python
- Section 77 Strings, ord, chr, and Binary Numbers in Python
- Section 78 Lists and Dictionaries in Python
- Section 79 Tuples in Python
- Section 80 Tuples and Sequences
- Section 81 Loops, Sequences and List in Python
- Section 82 Dictionaries and Comprehension in Python
- Section 83 Mapping, Zip and Attributes in Python
- Section 84 Arguments and Functions in Python
- Section 85 Argument, Defining Functions, and def in Python
- Section 86 Argument, String Code, and Sum Tree
- Section 87 Echo and Lambda Function
- Section 88 Lambda and Generating Function
- Section 89 def and Reducing Function in Python
- Section 90 def Saver, ASCII, Exception, Encoding and Decoding in Python
- Section 91 Get Attributes and Decorator in Python
- Section 92 Turtle, Pandas, Compilation, and Data Visualization
- Section 93 Logging, Data Visualization, and HTTP
- Section 94 Make Calculator, Countdown Time, Size and Path of a File
- Section 95 PyAudio, DataFrame, More Pandas Library & Create a Leap Year
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
Skills covered in this course
Reviews
-
PPoojashree Shivanna
sedf
-
PPiyush Chopra
yes thus course is very good and help alot in learning new skills
-
AAnonymized User
It's okay for beginners only
-
AAman Gupta
Very poor editing some videos are missing sound and in some videos, content is repeated which breaks the learning link. Also, the tutor is making frequent code errors in the second part of the course.
