Dive into Deep Learning

Advanced Feature Engineering

Learn advanced techniques for feature selection and extraction, and how to handle imbalanced datasets.

Recursive Feature Elimination
Lasso for Feature Selection
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Handling Imbalanced Datasets

Ensemble Methods

Explore ensemble methods such as bagging, boosting, and stacking to improve model performance.

Bagging (Bootstrap Aggregating)
Random Forest
AdaBoost
Gradient Boosting
XGBoost
Stacking and Blending

Model Tuning

Learn how to fine-tune models using hyperparameter tuning and regularization techniques.

Hyperparameter Tuning with Grid Search
Random Search for Hyperparameter Tuning
Regularization Techniques

Introduction to Deep Learning

Get introduced to the basics of neural networks, training techniques, and tools like TensorFlow and Keras.

Basics of Neural Networks
Training Neural Networks
Introduction to TensorFlow
Introduction to Keras

Natural Language Processing (NLP)

Learn the basics of NLP including text preprocessing, feature extraction, and building simple models for text classification and sentiment analysis.

Text Preprocessing
Bag of Words and TF-IDF
Basic NLP Models
Sentiment Analysis

Advanced Data Visualization

Create interactive and advanced visualizations using Plotly, Bokeh, and Seaborn.

Interactive Visualizations with Plotly
Bokeh for Interactive Visualizations
Advanced Seaborn Plots
Heatmaps and Pair Plots

Projects

Apply your advanced skills by implementing ensemble methods, building neural networks, conducting text classification, and creating advanced visualizations.

Ensemble Methods Project
Neural Networks Classification Project
NLP Text Classification Project
Advanced Data Visualization Project

Foundation

Advanced