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ztdl-masterclasses

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# Zero to Deep Learning Masterclasses

## Learn with us: www.zerotodeeplearning.com

## Titles

### Data Manipulation with Pandas
Learn to analyze real world data using Python & Pandas. Import data from multiple sources, clean, reshape, impute and visualize your data.

### Machine Learning with Scikit Learn
Learn what machine learning is and use Python, Pandas and Scikit-Learn to build machine learning models on real world data.

### Neural Networks with Keras
Learn what deep learning is and learn to use Python, Keras and Tensorflow to build deep learning models on real world data.

### Pandas Matplotlib Seaborn
Recap of main functionality of Pandas, Matplotlib and Seaborn with lots of exercises.

### Regression with Scikit Learn
Linear regression with single feature and multiple features using Scikit Learn. Model comparison on housing data.

### Classification with Scikit Learn
Binary classification with Scikit Learn. Numerical and categorical features. Model comparison.

### Clustering with Scikit Learn
Clustering with Scikit Learn on Iris flower dataset. KMeans, DBScan, Silhouette Score.

### Feature Engineering with Scikit Learn
Feature engineering with Scikit Learn on Titanic Dataset. Use transformers to create new features and improve your model performance.

### Model Evaluation and Dimensionality Reduction with Scikit Learn
Evaluating model performance using Pipelines, Cross-Validation, Learning curves and Dimensionality Reduction.

### Introduction to Deep Learning with Keras
First steps with Neural Networks using Keras to solve classifiation and regression problems.

### Keras Functional API and Model Inspection
Functional API, Callbacks, Inner Layer visualization and Tensorboard.

### The Power of TensorFlow
Explore other APIs in TensorFlow. Dataset API, Feature Columns API, Layers and Model API.

### Hyperparameter tuning
Tune hyperparameters using Grid Search, Random Search and Keras Autotuner.

### Image Classification
Learn about several simple datasets for image classification and build a fully connected network that uses pixels as features

### Convolutional Neural Networks
Build a convolutional neural network to classify images and optimize its architecture.

### Pre-trained Models
Discover pre-trained models in Keras applications and Tensorflow Hub.

### Transfer Learning
Adapt pre-trained models to your use case.

### Data Augmentation
Flow data from a directory using data generators in Keras and Tensorflow

### Image search
Retrieve images by similarity using bottleneck features.

### Bag of Words Features for Text
Classify different languages using Scikit-Learn and Count Vectors

### Natural Language Processing
Classify sentiment using bag of words with Sciki-Learn and NLTK

### Word Embeddings
Classify sentiment using word embeddings and convolutional neural networks in Keras.

### Time Series Forecasting
Forecast energy demand in Ontario using Fully connected and recurrent networks

### Sentiment Classification with Recurrent Neural Networks
Classify sentiment using RNNs

### Machine Translation with Recurrent Neural Networks
Train a neural network to perform character translation using RNNs

### Model Serving with Flask
Serve Scikit Learn and Tensorflow models using Flask

### Word Embeddings with Gensim
Visualize word embeddings with Gensim, Glove, Matplotlib and Plotly Express

### Gradient Boosting with XGBoost and LightGBM
Exploration of binary classification with several boosting algorithms. Hyperparameter tuning, grid search and categorical features.

### Real World ML Car Prices Regression
End-to-End Machine Learning exercise on messy dataset. Data cleaning, feature engineering, model selection with Scikit Learn, XGBoost and LightGBM.

### Real World ML Ads Classification
End-to-End Machine Learning exercise on unbalanced text dataset. Data cleaning, model selection with Scikit Learn, XGBoost, LightGBM, FastText and Gensim.

### Attention_with_Keras
<a href="https://colab.research.google.com/github/zerotodeeplearning/ztdl-masterclasses/blob/master/notebooks/Attention_with_Keras.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

### Intro_to_Python_1

### Intro_to_Python_2

### Python Quick Recap

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