Part-2 Data Science Basic to Advance

big data data science deep learning machine learning (ml) python

Intermediate Machine Learning

Feature Engineering: Selecting and transforming features.

Hyperparameter Tuning: Using grid or random search to improve models.

Ensemble Methods: Combining models, e.g., Random Forest and XGBoost.

Advanced Topics

Deep Learning: Neural networks for tasks like image and text recognition.

NLP: Processing text data using algorithms like TF-IDF and BERT.

Time Series: Forecasting using methods like ARIMA.

Reinforcement Learning: Models learning through interaction, such as Q-learning.

Big Data and Cloud Computing

Big Data: Tools like Hadoop and Spark for processing large datasets.

Cloud Platforms: AWS, Google Cloud, and Azure provide infrastructure for large-scale data tasks.