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Complete Roadmap to learn Generative AI in 2 months 👇👇

Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.

Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.

Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.

Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.

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⌨️ Python Lambda Function

Python Tip 🚀

Normally we use Square brackets to access a dictionary value using it's key.

To perform the above operation we can also make use of the python get method, which returns None if the input key is not part of the given dictionary.
This will save you from run time error (KeyError) if the key is not found and also you don't need to do extra coding to deal with unidentified keys.

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⌨️ Python String Formatting Based on placeholder

⌨️ QR code generation in Python

In general, the Python standard library includes many built-in functions that are available to use in your code without needing to import any additional modules. Some common examples of built-in functions include:

👉🏻 abs() : Returns the absolute value of a number.

👉🏻 all() : Returns True if all elements of an iterable are True, and False otherwise.

👉🏻 any() : Returns True if any element of an iterable is True, and False otherwise.

👉🏻 bin() : Converts an integer to a binary string.

👉🏻 bool() : Converts a value to a Boolean.

👉🏻 chr() : Returns the string representation of a Unicode character.

👉🏻 dir() : Returns a list of attributes and methods for an object.

👉🏻enumerate(): Returns an enumerate object, which contains a sequence of tuples containing the index and value of each element of an iterable.

👉🏻 filter() : Returns an iterator for elements of an iterable for which a condition is True.

👉🏻 float() : Converts a value to a floating-point number.

👉🏻 format(): Formats a string using format specifiers.

👉🏻 hash() : Returns the hash value of an object.

👉🏻 int() : Converts a value to an integer.

👉🏻 isinstance(): Returns True if an object is an instance of a given type, and False otherwise.

👉🏻 len() : Returns the length of an object.

👉🏻 list() : Converts an iterable to a list.

👉🏻 map() : Returns an iterator that applies a function to each element of an iterable.

👉🏻 max() : Returns the maximum value of an iterable.

👉🏻 min() : Returns the minimum value of an iterable.

👉🏻 next() : Returns the next element of an iterator.

👉🏻 open() : Opens a file and returns a file object.

👉🏻 ord() : Returns the Unicode code point for a character.

👉🏻 print() : Prints a message to the standard output.

👉🏻 range() : Returns a sequence of numbers.

👉🏻 repr() : Returns a string representation of an object.

👉🏻 round() : Rounds a number to a specified number of decimal places.

👉🏻 set() : Creates a set object.

👉🏻 sorted() : Returns a sorted list from an iterable.

👉🏻 str() : Converts a value to a string.

👉🏻 sum() : Returns the sum of elements in an iterable.

👉🏻 type() : Returns the type of an object.

👉🏻 zip() : Returns an iterator that combines elements from multiple iterables.

While loop in Python

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⌨️ Top Python Frameworks and Libraries

Python Customer Segmentation Tool Roadmap

Stage 1 - Learn Python (Basics, Pandas, Scikit-learn)
Stage 2 - Study Clustering Methods (K-means, DBSCAN)
Stage 3 - Clean & Prepare Data (Normalization, Feature Engineering)
Stage 4 - Apply Clustering Algorithms (Scikit-learn)
Stage 5 - Analyze & Visualize Results (Heatmaps, Charts)
Stage 6 - Add User Input Options (GUI, CLI)
Stage 7 - Test and Tune Models (Cross-validation)
Stage 8 - Deploy Tool (Web or Local Use)

🏆– Python Customer Segmentation Tool

Loop

Python Statistical Analysis Suite Roadmap

Stage 1 - Learn Python (Basics, Pandas, SciPy)
Stage 2 - Study Statistics (Regression, Hypothesis Testing)
Stage 3 - Explore Libraries (Statsmodels, Scikit-learn)
Stage 4 - Implement Basic Statistical Methods (ANOVA, T-tests)
Stage 5 - Build Analysis Pipelines (Reusable Code)
Stage 6 - Add Visualization (Plotly, Matplotlib)
Stage 7 - Validate Results (Real Datasets, Testing)
Stage 8 - Create UI (Dash, Streamlit)

🏆 – Python Statistical Analysis Suite

Python Automated Report Generator Roadmap

Stage 1 - Learn Python (Syntax, Jupyter, Pandas)
Stage 2 - Study Report Structure (Sections, Visualizations)
Stage 3 - Automate Data Processing (Scripts, Pipelines)
Stage 4 - Generate Reports (Markdown, Notebooks)
Stage 5 - Add Export Options (PDF, HTML)
Stage 6 - Enhance Visuals (Plotly, Matplotlib)
Stage 7 - Integrate Feedback Loops (Adjust Insights)
Stage 8 - Deploy Automation (Schedulers, Web Access)

🏆 – Python Automated Report Generator

Python Interactive Data Dashboard Roadmap

Stage 1 - Learn Python (Basics, Pandas, Plotly/Bokeh)
Stage 2 - Study Data Visualization (Charts, Graphs)
Stage 3 - Build Basic Dashboard (Plotly/Bokeh)
Stage 4 - Add Interactivity (Filters, Tooltips)
Stage 5 - Handle Large Datasets (Aggregation, Caching)
Stage 6 - Develop Responsive UI (CSS, JavaScript)
Stage 7 - Host on Web Framework (Flask/Dash)
Stage 8 - Deploy Online (Cloud, User Feedback)

🏆 – Python Interactive Data Dashboard