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10 ChatGPT Prompts To Transform Your Life

1. Use the 80/20 principle to learn faster

Prompt: "I want to learn about [insert topic].
Identify and share the most important 20% of learnings from this topic to help me understand 80%."

2. Improve your writing

Prompt: [Paste your writing] "Proofread my writing above. Fix grammar and spelling mistakes. And make suggestions that will improve the clarity of my writing."

3. Turn ChatGPT into your intern

Prompt: "I am creating a report about [insert topic].
Research and create an in-depth report with a step-by-step guide that will help readers understand how to [insert outcome]."

4. Learn any new skill

Prompt: "I want to learn [insert desired skill].
Create a 30-day learning plan to help a beginner like me learn and improve this skill."

5. Strengthen your learning

Prompt: "I am learning about [insert topic].
Ask me a series of questions that will test my knowledge. Identify knowledge gaps in my answers and give me better answers to fill those gaps."

6. Train ChatGPT to generate prompts

Prompt: "You are an Al designed to help [insert profession]. Generate a list of the 10 best prompts for yourself. The prompts should be about [insert topic]."

7. Mastering a hobby

Prompt: "Create structured learning paths for [Hobby]. Break it down into daily skill-building exercises. Design a system for validating progress.
Include a relationship between enjoyment and effort. Create opportunities for skill demonstration."

8. Learn any complex topic in seconds

Prompt: "Explain [insert topic] in simple and easy terms that even a 8 year old kid can understand."

9. Generate new ideas

Prompt: "I want to [insert task or goal]. Generate [insert desired outcome] for [insert task or goal]."

10. Summarize long documents

Prompt: "Summarize the text below and give me a list of bullet points with key insights and the most important facts." [Paste your text]

Are you looking to become a machine learning engineer?

I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

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61 steps to learn Machine Learning

Basics of Machine Learning 👇👇

Free Resources to learn Machine Learning: https://news.1rj.ru/str/free4unow_backup/587

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.

2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.

Key concepts include:

- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.

- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.

- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.

- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.

In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.

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Python for Data Engineering role 👇

➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity

➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming

➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops

➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance

➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes

➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()

➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks

➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics

➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets

➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases

⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses

⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐛𝐲 𝐬𝐭𝐮𝐝𝐲𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐛𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭

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Everything you need to become Data Scientist