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Machine Learning & Data Science in Python For Beginners

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Machine Learning & Data Science in Python For Beginners, available at $54.99, has an average rating of 3.85, with 74 lectures, 8 quizzes, based on 54 reviews, and has 416 subscribers.

You will learn about What is Machine Learning Supervised Machine Learning Unsupervised Machine Learning Semi-Supervised Machine Learning Types of Supervised Learning: Classification Regression Types of Unsupervised Learning: Clustering Association Data Collection Data Preparing Selection of a Model Data Training and Evaluation HPT in Machine Learning Prediction in ML DPP in ML Need of DPP Steps in DPP Python Libraries Missing, Encoding, and Splitting Data in ML Python, Java, R,and C ++ How to install python and anaconda? Interface of Jupyter Notebook Mathematics in Python Euler's Number and Variables Degree into Radians and Radians into Degrees in Python Printing Functions in Python Feature Scaling for ML How to Select Features for ML Filter Method LDA in ML Chi Square Method Forward Selection Training and Testing Data Set for ML Selection of Final Model ML Applications Practical Skills in ML: Mastery Process of ML What is Extension in ML ML Tradeoff ML Variance Error Logistic Regression Data Visualization Pandas and Seaborn-Library for ML This course is ideal for individuals who are For beginners and professional as well or Searching jobs in data science and machine learning or For those who want to practice python, data science, and machine learning at the same time It is particularly useful for For beginners and professional as well or Searching jobs in data science and machine learning or For those who want to practice python, data science, and machine learning at the same time.

Enroll now: Machine Learning & Data Science in Python For Beginners

Summary

Title: Machine Learning & Data Science in Python For Beginners

Price: $54.99

Average Rating: 3.85

Number of Lectures: 74

Number of Quizzes: 8

Number of Published Lectures: 74

Number of Published Quizzes: 8

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

  • What is Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Machine Learning
  • Types of Supervised Learning: Classification
  • Regression
  • Types of Unsupervised Learning: Clustering
  • Association
  • Data Collection
  • Data Preparing
  • Selection of a Model
  • Data Training and Evaluation
  • HPT in Machine Learning
  • Prediction in ML
  • DPP in ML
  • Need of DPP
  • Steps in DPP
  • Python Libraries
  • Missing, Encoding, and Splitting Data in ML
  • Python, Java, R,and C ++
  • How to install python and anaconda?
  • Interface of Jupyter Notebook
  • Mathematics in Python
  • Euler's Number and Variables
  • Degree into Radians and Radians into Degrees in Python
  • Printing Functions in Python
  • Feature Scaling for ML
  • How to Select Features for ML
  • Filter Method
  • LDA in ML
  • Chi Square Method
  • Forward Selection
  • Training and Testing Data Set for ML
  • Selection of Final Model
  • ML Applications
  • Practical Skills in ML: Mastery
  • Process of ML
  • What is Extension in ML
  • ML Tradeoff
  • ML Variance Error
  • Logistic Regression
  • Data Visualization
  • Pandas and Seaborn-Library for ML

Who Should Attend

  • For beginners and professional as well
  • Searching jobs in data science and machine learning
  • For those who want to practice python, data science, and machine learning at the same time

Target Audiences

  • For beginners and professional as well
  • Searching jobs in data science and machine learning
  • For those who want to practice python, data science, and machine learning at the same time

Get instant access to a 69-page Machine Learning workbook containing all the reference material

Over 9 hours of clear and concise step-by-step instructions, practical lessons, and engagement

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

What will you get from doing this course?

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 analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses.

You don’t need to have any technical knowledge to learn these skills.

What will you learn:

  • What is Machine Learning

  • Supervised Machine Learning

  • Unsupervised Machine Learning

  • Semi-Supervised Machine Learning

  • Types of Supervised Learning: Classification

  • Regression

  • Types of Unsupervised Learning: Clustering

  • Association

  • Data Collection

  • Data Preparing

  • Selection of a Model

  • Data Training and Evaluation

  • HPT in Machine Learning

  • Prediction in ML

  • DPP in ML

  • Need of DPP

  • Steps in DPP

  • Python Libraries

  • Missing, Encoding, and Splitting Data in ML

  • Python, Java, R,and C ++

  • How to install python and anaconda?

  • Interface of Jupyter Notebook

  • Mathematics in Python

  • Euler’s Number and Variables

  • Degree into Radians and Radians into Degrees in Python

  • Printing Functions in Python

  • Feature Scaling for ML

  • How to Select Features for ML

  • Filter Method

  • LDA in ML

  • Chi-Square Method

  • Forward Selection

  • Training and Testing Data Set for ML

  • Selection of Final Model

  • ML Applications

  • Practical Skills in ML: Mastery

  • Process of ML

  • What is Extension in ML

  • ML Tradeoff

  • ML Variance Error

  • Logistic Regression

  • Data Visualization

  • Pandas and Seaborn-Library for ML

  • …and more!

Contents and Overview

You’ll start with the What is Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Example of Supervised Machine Learning; Example of Un-Supervised Machine Learning; Example of Semi-Supervised Machine Learning; Types of Supervised Learning: Classification; Regression; Types of Unsupervised Learning: Clustering; Association.

Then you will learn about Data Collection; Data Preparation; Selection of a Model; Data Training and Evaluation; HPT in Machine Learning; Prediction in ML; DPP in ML; Need of DPP; Steps in DPP; Python Libraries; Missing, Encoding, and Splitting Data in ML.

We will also cover Feature Scaling for ML; How to Select Features for ML; Filter Method; LDA in ML; Chi Square Method; Forward Selection; Training and Testing Data Set for ML; Selection of Final Model; ML Applications; Practical Skills in ML: Mastery; Process of ML; What is Extension in ML; ML Tradeoff; ML Variance Error; What is Regression; Logistic Regression.

This course will also tackle Python, Java, R,and C ++; How to install python and anaconda?; Interface of Jupyter Notebook; Mathematics in Python; Euler’s Number and Variables; Degree into Radians and Radians into Degrees in Python; Printing Functions in Python.

This course will also discuss Random Selection; Random Array in Python; Random Array and Scattering; Scattering Plot; Jupyter Notebook Setup and Problem; Random Array in Python; Printing Several Function in Python; Exponential and Logarithmic Function in Python.

Next, you will learn about Simple Line Graph with Matplotlib; Color Scheme with Matplotlib; Dot and Dashed Graph; Scattering 1-Data visualization; Labelling-Data Visualization; Color Processing-Data Visualization; Seaborn Scatter Plot; Import DataFrame by Pandas.

Who are the Instructors?

Allah Dittah from Tech 100 is your lead instructor – a professional making a living from his teaching skills with expertise in Machine Learning. He has joined with content creator Peter Alkema to bring you this amazing new course.

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

Enrol now, and master Machine Learning!

Peter and Allah

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduce Yourself to Your Fellow Students And Tell Everyone What are Your Goals

Lecture 3: Machine Learning Workbook

Lecture 4: All code files for this course

Lecture 5: Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!

Chapter 2: Introduction to Machine Learning

Lecture 1: What is Machine Learning

Lecture 2: Supervised Machine Learning

Lecture 3: Unsupervised Machine Learning

Lecture 4: Semi-Supervised Machine Learning

Lecture 5: Example of Supervised Machine Learning

Lecture 6: Example of Un-Supervised Machine Learning

Lecture 7: Example of Semi-Supervised Machine Learning

Lecture 8: Types of Supervised Learning: Classification

Lecture 9: Regression

Lecture 10: Types of Unsupervised Learning: Clustering

Lecture 11: Association

Lecture 12: You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>

Chapter 3: Machine Learning Steps

Lecture 1: Data Collection

Lecture 2: Data Preparation

Lecture 3: Selection of a Model

Lecture 4: Data Training and Evaluation

Lecture 5: HPT in Machine Learning

Lecture 6: Prediction in ML

Lecture 7: DPP in ML

Lecture 8: Need of DPP

Lecture 9: Steps in DPP

Lecture 10: Python Libraries

Lecture 11: Missing, Encoding, and Splitting Data in ML

Chapter 4: Machine Learning Models

Lecture 1: Feature Scaling for ML

Lecture 2: How to Select Features for ML

Lecture 3: Filter Method

Lecture 4: LDA in ML

Lecture 5: Chi Square Method

Lecture 6: Forward Selection

Lecture 7: Training and Testing Data Set for ML

Lecture 8: Selection of Final Model

Lecture 9: You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>

Lecture 10: ML Applications

Lecture 11: Practical Skills in ML: Mastery

Lecture 12: Process of ML

Lecture 13: What is Extension in ML

Lecture 14: ML Tradeoff

Lecture 15: ML Variance Error

Lecture 16: What is Regression

Lecture 17: Logistic Regression

Chapter 5: Languages for ML

Lecture 1: Python, Java, R,and C ++

Chapter 6: Python

Lecture 1: Code files for this section

Lecture 2: How to install python and anaconda?

Lecture 3: Interface of Jupyter Notebook

Lecture 4: Mathematics in Python

Lecture 5: Euler's Number and Variables

Lecture 6: Degree into Radians and Radians into Degrees in Python

Lecture 7: Printing Functions in Python

Lecture 8: You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>

Chapter 7: Introduction to NumPy

Lecture 1: Code files for this section

Lecture 2: Random Selection

Lecture 3: Random Array in Python

Lecture 4: Random Array and Scattering

Lecture 5: Scattering Plot

Lecture 6: Jupyter Notebook Setup and Problem

Lecture 7: Random Array in Python

Lecture 8: Printing Several Function in Python

Lecture 9: Exponential and Logarithmic Function in Python

Chapter 8: Data Visualization with Matplotlib

Lecture 1: Code files for this section

Lecture 2: Simple Line Graph with Matplotlib

Lecture 3: Color Scheme with Matplotlib

Lecture 4: Dot and Dashed Graph

Lecture 5: Scattering 1-Data visualization

Lecture 6: Labelling-Data Visualization

Lecture 7: Color Processing-Data Visualization

Chapter 9: Pandas and Seaborn-Library for ML

Lecture 1: Code files for this section

Lecture 2: Seaborn Scatter Plot

Lecture 3: Import DataFrame by Pandas

Lecture 4: You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!

Instructors

Rating Distribution

  • 1 stars: 5 votes
  • 2 stars: 1 votes
  • 3 stars: 12 votes
  • 4 stars: 12 votes
  • 5 stars: 24 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.


Can I take my courses with me wherever I go?


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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