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Introduction to ML in Julia | Functions, Dictionaries, Variables, Conditions & basics [Lecture 3]
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"Introduction to ML in Julia: Building Foundations"
As we dive deeper into ML using Julia, I am excited to share the latest lecture in our "Introduction to Machine Learning in Julia" series on Vizuara's YouTube channel. Here is the lecture:
This session focused on mastering the building blocks of Julia programming, which are essential as we progress toward more complex topics.
Here is what I cover:
1️⃣ Conditional Statements – Writing if-else structures to make decisions in your code.
2️⃣ Loops – Using for and while loops to automate repetitive tasks.
3️⃣ Functions – Creating reusable blocks of code, including anonymous functions for quick operations.
4️⃣ Data Structures – Exploring arrays, tuples, dictionaries, and sets, and how they make data handling efficient in Julia.
We also discussed set operations, variable scope, and common operations like push, pop, and delete on arrays.
💡 These concepts might seem basic, but they are crucial for building confidence and fluency in Julia. If you are coding along with me, you are laying the groundwork for the data science, machine learning, and deep learning topics that are coming soon.
💡 What’s Next?
In the next lecture, I will introduce you to Pluto Notebooks, Julia's interactive and intuitive notebook environment. It is packed with features and is perfect for visualizing and sharing your work.
💡 Find the full lecture here:
Julia may feel niche compared to Python, but its power in scientific computing and machine learning is unmatched. If you have made it this far, you are already on the path to mastering Julia!
This course is aimed at both students exploring ML and industry professionals looking to build impactful ML applications. Julia may be niche, but for anyone serious about scientific ML, it is a powerful tool to add to your skill set.
As we dive deeper into ML using Julia, I am excited to share the latest lecture in our "Introduction to Machine Learning in Julia" series on Vizuara's YouTube channel. Here is the lecture:
This session focused on mastering the building blocks of Julia programming, which are essential as we progress toward more complex topics.
Here is what I cover:
1️⃣ Conditional Statements – Writing if-else structures to make decisions in your code.
2️⃣ Loops – Using for and while loops to automate repetitive tasks.
3️⃣ Functions – Creating reusable blocks of code, including anonymous functions for quick operations.
4️⃣ Data Structures – Exploring arrays, tuples, dictionaries, and sets, and how they make data handling efficient in Julia.
We also discussed set operations, variable scope, and common operations like push, pop, and delete on arrays.
💡 These concepts might seem basic, but they are crucial for building confidence and fluency in Julia. If you are coding along with me, you are laying the groundwork for the data science, machine learning, and deep learning topics that are coming soon.
💡 What’s Next?
In the next lecture, I will introduce you to Pluto Notebooks, Julia's interactive and intuitive notebook environment. It is packed with features and is perfect for visualizing and sharing your work.
💡 Find the full lecture here:
Julia may feel niche compared to Python, but its power in scientific computing and machine learning is unmatched. If you have made it this far, you are already on the path to mastering Julia!
This course is aimed at both students exploring ML and industry professionals looking to build impactful ML applications. Julia may be niche, but for anyone serious about scientific ML, it is a powerful tool to add to your skill set.
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