😎 Docker Main Components - Clearly explained

Docker is a tool for simplifying application development and deployment.

Let's dive into essential components. 🧵👇🏽 1️⃣ Our Docker-based products for you!

🔹 MLOps: Free MLflow Workspace On-Premise with Docker Deployment


🔹 Enterprise-Level Plotly Dash App Template with Docker Deployment


🔹 Plotly Dash + LangChain: Chatbot App Template with Docker Deployment
shop.tinztwins.de/l/mlflow-works…
shop.tinztwins.de/l/plotly-dash-…
shop.tinztwins.de/l/chatbot-temp…

📈 Analyzing time series data like #Tesla stock is crucial for making informed trading decisions.

But have you ever wondered how you can shift time series data in Pandas to make it more suitable for machine learning models, for example?

Let's dive into it.👇🏽 1️⃣ First things first, you need to prepare your environment with tools such as conda and OpenBB as your starting point.

This will enable you to access Tesla's stock market data.

🤖 Are you interested in credit card fraud detection with an autoencoder?

Let's explore this together. 🧵👇🏽 1️⃣ Use Case: Credit Card Fraud Detection

Every day, countless credit card transactions occur.

Yet, only a tiny fraction of these are fraudulent. These rare, fraudulent transactions are anomalies that we need to detect.

🤔 How does an autoencoder work (with code)?

Don't worry, we have an easy-to-understand explanation for you!

Let's explore the world of autoencoders together. 🧵👇🏽 1️⃣ In General

Autoencoders are artificial neural networks. They are often used in anomaly detection.

In addition, they belong to the semi-supervised methods because you train them only with the normal state of the data.

🧐 How do Support Vector Machines work?

Here is an easy-to-understand explanation.

Let's take a look at it together. 🧵👇🏽 1️⃣ Intuitive explanation:

Imagine you have a set of points on a piece of paper, and you want to draw a line that separates them into two groups.

A Support Vector Machine (SVM) is like finding the best line that creates the widest gap between these groups.

🤖 Do you know how neural networks work in general?

Don't worry, we explain it clearly.

Let's go over it together. 🧵👇🏽 1️⃣ Basic idea

An artificial neural network (ANN) consists of artificial neurons (also called nodes) and connections (also called edges) between these neurons. In addition, an ANN has one or more hidden layers, each layer consisting of several neurons.

Each neuron in each layer receives the output of each neuron in the previous layer as input. Each input to the neuron is weighted. The connections between the nodes are acyclic.

🤔 What is the Bias and Variance Tradeoff?

Don't worry, we have an easy-to-understand explanation for you!

Let's explore the Bias and Variance Tradeoff together. 🧵👇🏽 1️⃣ Motivation

Many aspiring data scientists don't understand what bias and variance are and why it is a tradeoff.

In the following, we explain visually how the concepts work.

🤔 Have you trouble to understand the ROC Plot?

Don't worry, we have an easy-to-understand explanation for you!

Let's explore the ROC plot together. 🧵👇🏽 1️⃣ What is the ROC plot?

You can use a Receiver Operating Characteristics (ROC) curve to evaluate the results of a classifier.

The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).

📊 Struggling to understand Univariate Discrete Distributions?

Don't worry, we have an easy-to-understand explanation for you.

Let's explore univariate discrete distributions together! 🧵👇🏽 1️⃣ Bernoulli Distribution

This distribution models a single experiment with two possible outcomes - success or failure. It is characterized by a single parameter, the probability of success (denoted as p).

Example: Tossing a coin (Heads or Tails).

‼️ Not enough test data to evaluate your machine learning model?

Don't worry, we can help you.

Explore the importance of cross-validation. 👇🏽 1️⃣ What is cross-validation?

Cross-validation (CV) is a statistical testing procedure based on resampling. It is a crucial tool in modern statistics and machine learning.

Resampling involves repeatedly taking samples from a training dataset and fitting a model to each sample. This method enables you to gather valuable information about the fitted model.

🤖 Have you trouble understanding ensemble methods?

Don't worry, you're in the right place!

Let's explore the world of ensemble methods together. 👇🏽 1️⃣ What are ensemble methods?

Ensemble methods are a powerful approach in the world of machine learning that involve combining multiple models to create an even more robust and accurate model.

💡 The primary idea behind ensemble methods is that the aggregation of multiple models helps to mitigate the weaknesses of the individual models.

This assumption is based on the belief that each model brings its unique strengths to the table, and by combining them, we can create a more comprehensive and reliable model.

🤖 Struggling to understand Linear Regression?

Don't worry, you're in the right place!

Let's explore the world of Linear Regression together. 👇🏽 0️⃣ Linear Regression

Linear Regression is a simple statistical regression method, making it perfect for beginners in predictive analysis.

You can perform Linear Regression with multiple variables or just one.

Let's keep things simple and fun for now! We'll focus on using a single variable today, so it's easier to grasp. 😊

As known as Simple Linear Regression or Univariate Linear Regression.

What is the best way to allocate your money across various financial investments? 📈

Don't worry, we've got you covered. 🤓

Discover how to determine the statistically optimal allocation in this post.

A thread 🧵 1️⃣ Investment returns (R1, R2, ...) are random variables, which means they have an expected value, variance, distribution, and so on. Below, you can see the distribution for the return on a security.

This security has a positive expected return, but a loss cannot be ruled out. The loss probability is represented by the light blue color. We can easily calculate this probability as the area under the density curve.

📈 Candlestick Charts - Clearly explained

Candlestick charts are a popular type of financial chart used to represent the price movement of assets such as stocks over time. They offer valuable insights into market trends and investor sentiment. You can use @OpenBB to plot candlestick charts. For us, it is the best tool for investment research.

A thread 🧵
1/5: Each candlestick represents a specific time period (e.g., 1 day, 1 hour, 1 day) and consists of a body and wicks. The body displays the opening and closing prices, while the wicks signify the highest and lowest prices achieved during that period.

🤔 Using @LangChainAI to Chat with Earnings Reports of Companies

In our article, we build a plotly dash web app (@plotlygraphs) to compare the Q1 interim reports of Tesla, Mercedes Benz and BMW. 

The steps are as follows:
Document Loading → Document Splitting → Vector… https://t.co/zHaZO8EwYgtwitter.com/i/web/status/1…
0️⃣ Link to our article:
towardsfinance.com/langchain-use-…

🤔 Use the Power of LLMs on your data with @llama_index!

With LlamaIndex, you can connect your custom data sources to LLMs. There are a high-level API and a low-level API for advanced developers. In our article, we focus on the high-level API. We'll show you an example of how to… https://t.co/5OBUMAilfDtwitter.com/i/web/status/1…
0️⃣ Link to our article:

pub.towardsai.net/llamaindex-use…