Artificial Intelligence

Understand the Basics of Programming:

Learn a programming language: Start with Python due to its popularity in AI and data science.
Basics of programming: Understand variables, data types, loops, and functions.

Learn Mathematics:

Linear algebra: Focus on vectors, matrices, and operations like dot product.
Calculus: Learn about derivatives and integrals, especially for understanding optimization algorithms.

Study Statistics:(At first watch the youtube videos)

Descriptive statistics: Learn measures like mean, median, mode, and standard deviation.
Inferential statistics: Understand hypothesis testing, confidence intervals, and regression.
Probability and statistics: Understand concepts like probability distributions, mean, median, standard deviation.

Explore Data Science:

Data cleaning: Learn techniques to handle missing data and outliers.
Data visualization: Use libraries like Matplotlib or Seaborn to create plots for data exploration.

Introduction to Machine Learning:

Supervised learning: Understand the basics of regression and classification.
Unsupervised learning: Explore clustering and dimensionality reduction techniques.

Deepen Machine Learning Knowledge:

Ensemble methods: Learn about bagging and boosting techniques.
Dimensionality reduction: Understand methods like Principal Component Analysis (PCA).
Feature engineering: Explore techniques to create new features for better model performance.

Introduction to Neural Networks:

Basics of neural networks: Understand the structure, activation functions, and the concept of weights and biases.
Backpropagation: Learn the algorithm used for training neural networks.

Deep Learning:

Deep learning frameworks: Familiarize yourself with TensorFlow or PyTorch.
Convolutional Neural Networks (CNNs): Learn about image-related tasks.
Recurrent Neural Networks (RNNs): Explore sequential data and time-series analysis.

Natural Language Processing (NLP):

Tokenization: Understand the process of breaking down text into words or phrases.
Word embeddings: Learn about techniques like Word2Vec or GloVe for representing words numerically.
Language models: Understand the basics of models like GPT (Generative Pre-trained Transformer).

Computer Vision:

Image processing: Learn about basic operations on images like blurring, edge detection, etc.
Object detection: Understand techniques for identifying and locating objects in images.
Image classification: Learn methods for categorizing images into different classes.
Hands-on Projects: Apply your knowledge in practical projects, such as building a simple machine learning model or a neural network for a specific task. Use platforms like Kaggle for real-world datasets and competitions.