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.

Best Resources to learn Generative AI 👇👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Deep Learning Nanodegree Program with Real-world Projects

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

If you're into deep learning, then you know that students usually one of the two paths:

- Computer vision
- Natural language processing (NLP)

If you're into NLP, here are 5 fundamental concepts you should know:

Before we start, What is NLP?

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.

It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.

Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.

Tokenization

Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.

Part-of-Speech Tagging:

This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.

Stemming and Lemmatization

These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.

Named Entity Recognition (NER)

NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.

Sentiment Analysis

This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.

Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)

Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)

Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets

Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)

Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference

Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)

Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data

Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring

🖥 deepface - Python library for facial recognition and more

- pip install deepface

⏩ deepface is a lightweight Python library that allows you to find faces and analyze various attributes from photographs: age, gender, emotions.
It incorporates the best of the VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet models.

⏩ This is how you can compare the similarity of 2 faces, the result is in the image:

from deepface import DeepFace
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

🖥 GitHub
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