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What you'll learn?
- Syntax of Julia (and differences from Python)
- Stength of Julia in terms of data science and machine learning
- DataFrames (equiv. to Pandas) in Julia
- Data science case studies including analysis and clustering
- Machine learning models both traditional and deep
- Create ML models from scratch in a way that helps you make modifications easily
- Generative Adversarial Networks (GANs)
Course Overview
In the fast-paced world of Data Science and Machine Learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for Data Science and Machine Learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!
That is the objective of this course: to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.
In this course, we take a code-oriented approach. We don't waste time with the theory of why Julia is fast. We jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.
We take a case-study-based approach. After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.
This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own! Take a look at the promo for this course (and contents list below) for the topics you will learn as well as the preview lectures to get an idea of the interactive style of learning.
Remember: The reason you pay for this course is support. I reply within the day. See any of my course reviews for proof of that. So make sure you post any questions you have or any problems you face. I want all my students to finish this course. Let’s get through this together.
Pre-requisites
- Basic understanding of programming
- Python would be helpful but not necessary
- Understanding of basic Data Science (reading CSVs etc) would be helpful
- Understanding of basic concepts of Deep Learning (such as classification) would be useful
Target Audience
- All level of Data Science practitioners
- All levels of Machine Learning practitioners
- Those aiming to enhance their abilities and skill level in DS and ML
- Developers who want to know how to harness the power of big data
Curriculum 23 Lectures 03:31:39
-
Section 1 : Introduction and Setting up
- Lecture 2 :
- Packages and Interactive Notebook
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Section 2 : Core Language Basics
- Lecture 1 :
- Basics Syntax , Variables & Operations
- Lecture 2 :
- Control Structures, Iterations and Ranges
- Lecture 3 :
- Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples
- Lecture 4 :
- Dictionaries ,Symbols in Julia
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Section 3 : Arrays and Matrices: Native Language Support
- Lecture 1 :
- Arrays
- Lecture 2 :
- Data Type, Casting Among Types
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Section 4 : Functions and Fun Stuff
- Lecture 1 :
- Defining Functions
- Lecture 2 :
- Anonymous Functions, Splatting and Slurping
- Lecture 3 :
- Functional Programming, Broadcasting
- Lecture 4 :
- Interfacing with Python and R
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Section 5 : Getting Started with Data Science
- Lecture 1 :
- Plotting Basics
- Lecture 2 :
- Data Wrangling, Reading CSV Files, Descriptive Case Study
- Lecture 3 :
- Further Data Manipulation, Apache Arrow, Grouping and analysis
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Section 6 : Case Studies in Data Science
- Lecture 1 :
- Case Study: Clustering for Housing/Map Data
- Lecture 2 :
- Classification with Decision Tree
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Section 7 : Deep Learning - Flux in Julia
- Lecture 1 :
- Writing a Natural Network From Scratch
- Lecture 2 :
- Multiple Layers, State-of-the-Art in a Few More Lines
- Lecture 3 :
- Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls
- Lecture 4 :
- MNIST Continued, Creating the Deep Model, Training and Testing
- Lecture 5 :
- Saving and Loading Models, Exploring More Options
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Section 8 : Parting Words
- Lecture 1 :
- Where to Go from Here: Pointers for Further Learning
Our learners work at
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Instructor
3994 Course Views
9 Courses
I have a PhD in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. I have been programming since early 2000 and have worked with many different languages, tools and platforms. I have an extensive research experience with many state-of-the-art models to my name. My research in Android security has led to some major shifts in the Android permission model.
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