For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

My recommendation to start learning how to deal with structured data: Kaggle's Pandas Tutorial.

• Creating, Reading, Writing
• Indexing, Selecting, Assigning
• Summary Functions, Maps
• Grouping, Sorting
• Data Types, Missing Values
• Renaming, Combining

100% Python 🐍.

Some of my machine learning books.

Hands-On Machine Learning from @aureliengeron and Deep Learning with Python from @fchollet.

1/ @werat asked about whether the debugger was using the C# compiler or language service in VS 2002. It was not. The debugger has a component called an ‘expression evaluator’ that is provided per language and is responsible for parsing and evaluating expressions when stopped at a

2/ breakpoint. For example, if you type into the immediate window, hover over a variable, type into the watch window, etc. the expression evaluator is involved. The debugger and the language service are actually deeply integrated in a number of scenarios in VS, which may

3/ initially seem surprising. I may talk about more of these scenarios in the future, but to give a flavor, when you set a breakpoint at design time the language service is involved, when you are using Edit and Continue the LS is involved, the range of what is being evaluated

4/ when you get a debugger data tip, completion lists when typing in the data windows or immediate window, etc. Prior to VS 2002, there were distinct debug environments tailored to each target customer (VB/VBA, C++, VJ, VBScript, TSQL, FoxPro, Fortran, etc.). The goals of the

5/ VS 2002 debugger included unifying the debugger backend components and providing a single UI stack that would enable new scenarios (for example, a callstack across languages). Expression evaluators are the architecture that separate the language specific pieces from the

Machine Learning for the Web developer in 2021.

The beginner's guide.​

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

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"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

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In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

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There are several skills needed for learning machine learning that no one talks about.

Here are some of them.
(from what I've learned over the past two years)
🧵👇

This thread aims to introduce to you some of the skills often ignored for learning machine learning but are actually very important.

These skills will enable you to learn concepts quickly and more efficiently in this field.

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1⃣ Reading

Probably one of the most underrated skills on this list.

In machine learning, you HAVE to read a lot of articles, papers, documentation, and whatnot.

It is mostly due to the theoretical nature of this field that reading is an important skill to have.

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2⃣ Strong fundamentals in programming

Machine learning is just a lot of programming mixed with math and data.

Having clear programming fundamentals is crucial for this field.

I highly suggest you learn about these if you are using Python for machine learning.👇

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- Object oriented programming in Python :Classes, Objects, Methods
- Lists & List functions
- Dunder Methods
- List comprehension
- List slicing
- String formatting
- List, Dictionaries & Tuples
- *args,**kwargs

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1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.