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The Machine Learning for Beginners Overview Bundle

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3
Lessons
108
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Access
Lifetime
Content
2.0 hours
Lessons
28

Machine Learning for Absolute Beginners: Level 1

Learn the Basics of Machine Learning & AI Even with No Prior Knowledge

By Idan Gabriella | in Online Courses

The concept of Artificial Intelligence and Machine Learning can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts. In this course, you're going to open the mysterious AI/ML black-box, and take a look inside, get more familiar with the terms being used in the industry. It is going to a super interesting story. It is important to mention that there are no specific prerequisites for starting this training, and it is designed for absolute beginners.

4.3/5 average rating: ★ ★ ★ ★

  • Access 28 lectures & 2 hours of content 24/7
  • Understand the difference between Applied & Generalized AI
  • Learn the process of training a model
  • Learn more about Machine Learning & Deep Learning
  • Understand clustering & dimension reduction
Idan Gabrieli | Entrepreneur | Cloud Expert
4.4/5 Instructor Rating: ★ ★ ★ ★

Idan Gabrieli is an experience solution engineer manager (B.Sc. and MBA) with a comprehensive technical background in a variety of technologies. Idan is working with hundreds of business companies worldwide while helping to transform business challenges, requirements, and opportunities into practical use cases.

As part of his passion for sharing years of experience and knowledge, he created multiple online courses about a variety of topics while teaching thousands of students worldwide.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program
  • Getting Started
    • Welcome! - 6:38
  • The Rise of Artificial Intelligence
    • AI is Coming... - 4:22
    • Artificial Intelligence - 6:14
    • Classical Programming - 3:12
    • Machine Learning - 7:13
    • Deep Learning - 7:51
    • Applied vs. Generalized AI - 4:19
    • Why Now? - 9:21
    • Quick Check-Point #1
  • Introduction to Machine Learning
    • Overview - ML Terminology - 1:20
    • “Black Box” Metaphor - 3:04
    • Features and Labels - 5:09
    • Training a Model - 4:49
    • Aiming for Generalization - 11:23
    • Quick Check-Point #2
  • Classification of ML Systems
    • The Degree of Supervision - 1:44
    • #1 - Supervised Learning - 6:16
    • Classification - 5:23
    • Regression - 7:13
    • Quick Check-Point #3
    • #2 - Unsupervised Learning - 3:40
    • Clustering - 5:09
    • Dimension Reduction - 5:48
    • Quick Check-Point #4
    • #3 - Reinforcement Learning - 5:57
    • Decision-Making Agent - 7:04
    • Quick Check-Point #5
  • Course Summary
    • Let's Recap and Thank You! - 6:40

View Full Curriculum


Access
Lifetime
Content
3.0 hours
Lessons
41

Machine Learning for Absolute Beginners: Level 2

Learn the Python Fundamentals & Pandas Library for Data Science Projects

By Idan Gabriella | in Online Courses

The Machine Learning for Absolute Beginners training program is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. The training is divided into multiple levels, and each level is covering a group of related topics for continuous step by step learning. The second course, as part of the training program, aims to help you start your practical journey. You will learn the Python fundamentals and the amazing Pandas data science library. Each section has a summary exercise as well as a complete solution to practice new knowledge.

4.3/5 average rating: ★ ★ ★ ★

  • Access 41 lectures & 3 hours of content 24/7
  • Develop data science projects using Python syntax
  • Use JupyterLab tool for Jupiter notebooks
  • Load large datasets from files using Pandas
  • Perform data analysis & exploration
  • Perform data cleaning & transformation as a pre-processing step before moving into machine learning algorithms
Idan Gabrieli | Entrepreneur | Cloud Expert
4.4/5 Instructor Rating: ★ ★ ★ ★

Idan Gabrieli is an experience solution engineer manager (B.Sc. and MBA) with a comprehensive technical background in a variety of technologies. Idan is working with hundreds of business companies worldwide while helping to transform business challenges, requirements, and opportunities into practical use cases.

As part of his passion for sharing years of experience and knowledge, he created multiple online courses about a variety of topics while teaching thousands of students worldwide.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program
  • Getting Started
    • Welcome! - 2:19
    • Anaconda Installation - 4:22
    • JupyterLab Overview - 3:51
    • Working with a Jupyter Notebook - 14:16
  • Python Fundamentals for Data Science
    • Overview - 2:52
    • Variables and Data Types - 7:22
    • Strings - 7:44
    • Lists - 9:44
    • IF and For-Loop Statements - 7:08
    • Functions - 7:59
    • Dictionaries - 11:01
    • Classes, Objects, Attributes, and Methods - 7:28
    • Importing Modules - 7:34
    • Libraries for Data Science Projects - 7:03
    • Exercise #1 - Python Fundamentals - 0:33
  • Introduction to the Pandas Library
    • Overview - 2:59
    • Series Data Structure (1D) - 12:41
    • DataFrame Data Structure (2D) - 4:52
    • Data Selection in a DataFrame - 14:55
    • Exercise #2 – Pandas Series and DataFrame - 0:34
  • Loading Data into a DataFrame
    • Overview - 1:14
    • Kaggle and the Titanic Dataset - 5:48
    • Loading a Tabular Data File - 6:35
    • Adjusting the Loading Parameters - 13:06
    • Preview the DataFrame - 8:14
    • Using Summary Statistics - 4:48
    • The Concept of Methods Chaining - 5:17
    • Sorting and Ranking - 3:13
    • Filtering - 4:53
    • Grouping - 4:52
    • Exercise #3 – Data Loading and Analysis - 0:34
  • Data Cleaning and Transformation
    • Overview - 2:07
    • Removing Columns or Rows - 4:17
    • Removing Duplicate Rows - 8:37
    • Renaming Column Labels - 3:32
    • Dropping Missing Values - 7:29
    • Filling-in Missing Values - 3:31
    • Creating Dummy Variables - 8:16
    • Exporting Data into Files - 2:39
    • Exercise #4 – Data Cleaning and Transformation - 0:27
  • Course Summary
    • Let's Recap and Thank You! - 2:45

View Full Curriculum


Access
Lifetime
Content
2.0 hours
Lessons
39

Machine Learning for Absolute Beginners - Level 3

Master Data Visualization & Perform Exploratory Data Analysis (EDA) Using Python, Matplotlib, and Seaborn

By Idan Gabriella | in Online Courses

This third course as part of the training program aims to help you to perform Exploratory Data Analysis (EDA) by visualizing a dataset using a variety of charts. You will learn the fundamentals of data visualization in Python using the well-known Matplotlib and Seaborn data science libraries, including Matplotlib and Seaborn fundamentals, charts, and more. Each section has a summary exercise as well as a complete solution to practice new knowledge.

4.2/5 average rating: ★ ★ ★ ★

  • Access 39 lectures & 2 hours of content 24/7
  • Perform Exploratory Data Analysis (EDA) for any dataset
  • Visualize data using a variety of chart types
  • Learn Matplotlib & Seaborn fundamentals
  • Create bar, grouped bar, stacked bar & lollipop charts
  • Create pie, tree-map charts
  • Create line, area, stacked area charts
  • Create histogram, density, box-and-whisker & swarm charts
  • Create scatter, correlogram, heat-map, hexbin-map charts
Idan Gabrieli | Entrepreneur | Cloud Expert
4.4/5 Instructor Rating: ★ ★ ★ ★

Idan Gabrieli is an experience solution engineer manager (B.Sc. and MBA) with a comprehensive technical background in a variety of technologies. Idan is working with hundreds of business companies worldwide while helping to transform business challenges, requirements, and opportunities into practical use cases.

As part of his passion for sharing years of experience and knowledge, he created multiple online courses about a variety of topics while teaching thousands of students worldwide.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program
  • Getting Started with Level 3!
    • Welcome! - 3:21
    • Our Overall Learning Path - 2:13
    • How to Practice? - 2:31
  • Data Visualization with Matplotlib and Seaborn
    • Overview - 2:11
    • Matplotlib – Overview - 8:23
    • Matplotlib – Figures, Axes - 3:23
    • Matplotlib – The OO and Pyplot Interfaces - 6:59
    • Matplotlib – APIs Reference Review - 5:37
    • Seaborn – Overview - 3:50
    • Seaborn – Figure and Axes-level Functions - 6:21
    • Seaborn - Chart Customization - 4:44
    • Seaborn – API Reference Review - 4:00
    • A little bit about NumPy - 6:40
    • The Right Chart for the Right Job - 2:50
  • Ranking and Proportion Charts
    • Overview - 1:20
    • Bar Chart - 10:20
    • Grouped Bar Chart - 9:07
    • Lollipop Chart - 4:03
    • Stacked Bar Chart - 3:34
    • Pie Chart - 3:51
    • Treemap - 6:25
    • Optimizing Colors - 6:32
    • Exercise 1 - Ranking and Proportion Charts
  • Trend and Distribution Charts
    • Overview - 1:56
    • Line Chart - 6:59
    • Area Chart - 1:58
    • Stacked Area Chart - 3:43
    • Histogram Chart - 6:43
    • Density Curve Chart - 5:38
    • Box-and-Whisker Chart - 9:30
    • Bee-swarm Chart - 2:30
    • Exercise 2 – Trend and Distribution Charts
  • Correlation Charts
    • Overview - 2:41
    • Scatter Chart - 10:26
    • Correlogram - 3:29
    • Heat-Map - 8:34
    • Hexbin-Map - 4:21
    • Exercise 3 – Correlation Charts
  • Course Summary
    • Let’s Recap and Thank You! - 2:55

View Full Curriculum



Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.