Prerequisite of ML

[] Python
Pandas
Numpy
Matplotlib
Seaborn

Basic DataAnalysis

Data Collection and preparation

Webscrapping.
Load data from API and clean it in below step.
Load data from database and clean it in below step.
Keep data in json, xml, text and different formats(Just to practice.)

Data Manipulation

Cleaning
Transformation
Normalization

Dimensionality Reduction

Learn Eigen values and Eigen vectors.
PCA
LDA

Machine Learning

Machine Learning Basics:

Supervised Learning (regression, classification)
Unsupervised Learning (clustering, dimensionality reduction)
Model evaluation metrics
Underfitting & Overfitting, Hyperparameter Tuning
Popular Algorithms:
- Scikit-Learn: Linear Regression, Logistic Regression, Decision Trees, Random Forests

Deep Learning

Neural Networks:

Perceptrons, Neurons, Layers, Activation Functions
Loss Functions, Regularization
TensorFlow or PyTorch basics
Build an Artificial Neural Network (ANN)

Explore Neural Networks:

CNNs for Computer Vision
RNNs & LSTMs for Natural Language Processing

Specialization

Choose one path:

Computer Vision (CV):
- Deep learning for image recognition, object detection, etc.
- Explore OpenCV library
- Consider VR/AR/XR applications
Natural Language Processing (NLP):
- Deep learning for text analysis, sentiment analysis, etc.
- Explore Transformer architecture, BERT models
- Consider Large Language Models (LLMs)