Machine Learning for Absolute Beginners – Level 3
Machine Learning for Absolute Beginners – Level 3, available at $49.99, has an average rating of 4.48, with 40 lectures, based on 873 reviews, and has 15021 subscribers.
You will learn about Perform Exploratory Data Analysis (EDA) for any Dateset Visualise Data using a Variety of Chart Types Learn Matplotlib and Seaborn Fundamentals Creating Bar, Grouped Bar, Stacked Bar, Lollipop charts Creating Pie, Tree-map charts Creating Line, Area, Stacked Area charts Creating Histogram, Density, Box-and-Whisker, Swarm charts Creating Scatter, Correlogram, Heat-Map, Hexbin-Map charts This course is ideal for individuals who are Developers curious about Data Science projects or Beginner Data Scientists or AI Product Managers or ML Engineers or AI/ML Consultants It is particularly useful for Developers curious about Data Science projects or Beginner Data Scientists or AI Product Managers or ML Engineers or AI/ML Consultants.
Enroll now: Machine Learning for Absolute Beginners – Level 3
Summary
Title: Machine Learning for Absolute Beginners – Level 3
Price: $49.99
Average Rating: 4.48
Number of Lectures: 40
Number of Published Lectures: 40
Number of Curriculum Items: 40
Number of Published Curriculum Objects: 40
Original Price: $189.99
Quality Status: approved
Status: Live
What You Will Learn
- Perform Exploratory Data Analysis (EDA) for any Dateset
- Visualise Data using a Variety of Chart Types
- Learn Matplotlib and Seaborn Fundamentals
- Creating Bar, Grouped Bar, Stacked Bar, Lollipop charts
- Creating Pie, Tree-map charts
- Creating Line, Area, Stacked Area charts
- Creating Histogram, Density, Box-and-Whisker, Swarm charts
- Creating Scatter, Correlogram, Heat-Map, Hexbin-Map charts
Who Should Attend
- Developers curious about Data Science projects
- Beginner Data Scientists
- AI Product Managers
- ML Engineers
- AI/ML Consultants
Target Audiences
- Developers curious about Data Science projects
- Beginner Data Scientists
- AI Product Managers
- ML Engineers
- AI/ML Consultants
Unleash the Power of ML
Machine Learningis one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide!
Machine Learning for Absolute Beginners
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 a continuous step-by-step learning path.
Level 3 – Data Visualization with Matplotliband Seaborn
The 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:
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Matplotlib fundamentals
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Seaborn fundamentals
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Selecting the right chart for the right job
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Bar, Grouped Bar, Stacked Bar, Lollipop charts
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Pie, Three-map charts
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Line, Area, Stacked Area charts
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Histogram, Density, Box-and-Whisker, Swarm charts
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Scatter, Correlogram, Heatmap, Hexbin charts
Each section has a summary exercise as well as a complete solution to practice new knowledge.
The Game just Started!
Enroll in the training program and start your journey to become a data scientist!
Course Curriculum
Chapter 1: Getting Started with Level 3!
Lecture 1: Welcome!
Lecture 2: Our Overall Learning Path
Lecture 3: How to Practice?
Chapter 2: Data Visualization with Matplotlib and Seaborn
Lecture 1: Overview
Lecture 2: Matplotlib – Overview
Lecture 3: Matplotlib – Figures, Axes
Lecture 4: Matplotlib – The OO and Pyplot Interfaces
Lecture 5: Matplotlib – APIs Reference Review
Lecture 6: Seaborn – Overview
Lecture 7: Seaborn – Figure and Axes-level Functions
Lecture 8: Seaborn – Chart Customization
Lecture 9: Seaborn – APIs Reference Review
Lecture 10: A little bit about NumPy
Lecture 11: The Right Chart for the Right Job
Chapter 3: Ranking and Proportion Charts
Lecture 1: Overview
Lecture 2: Bar Chart
Lecture 3: Grouped Bar Chart
Lecture 4: Lollipop Chart
Lecture 5: Stacked Bar Chart
Lecture 6: Pie Chart
Lecture 7: Treemap Chart
Lecture 8: Optimizing Colors
Lecture 9: Exercise 1 – Ranking and Proportion Charts
Chapter 4: Trend and Distribution Charts
Lecture 1: Overview
Lecture 2: Line Chart
Lecture 3: Area Chart
Lecture 4: Stacked Area Chart
Lecture 5: Histogram Chart
Lecture 6: Density Curve Chart
Lecture 7: Box-and-Whisker Chart
Lecture 8: Bee-swarm Chart
Lecture 9: Exercise 2 – Trend and Distribution Charts
Chapter 5: Correlation Charts
Lecture 1: Overview
Lecture 2: Scatter Chart
Lecture 3: Correlogram
Lecture 4: Heat-Map
Lecture 5: Hexbin-Map
Lecture 6: Exercise 3 – Correlation Charts
Chapter 6: Course Summary
Lecture 1: Let’s Recap and Thank You!
Lecture 2: *** BONUS ***
Instructors
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Idan Gabrieli
Online Teacher | Data, Cloud, AI
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 12 votes
- 3 stars: 108 votes
- 4 stars: 326 votes
- 5 stars: 423 votes
Frequently Asked Questions
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Can I take my courses with me wherever I go?
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