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BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras

NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!

Data science, machine learning, and artificial intelligence in Python for students and professionals

A guide for writing your own neural network in Python and Numpy, and how to do it in Google's TensorFlow.

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences

Autoencoders and Restricted Boltzmann Machines for Deep Neural Networks in Theano / Tensorflow, plus t-SNE and PCA

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More Tensorflow, Keras, and Python

Complete Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression

The Most-Used and Practical Data Science Techniques in the Real-World

For Operations Research, Quantitative Finance, Economics, and More

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

Time Series Analysis with the Facebook Prophet Library

Practical applications of NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.

Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning

Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

Numpy, Scipy, Pandas, Matplotlib, and Scikit-Learn: prep for deep learning, machine learning, and artificial intelligence

Complete guide to MATLAB for beginners and experts

Master SQL for Job Interview Preparation, Data Analysis, Big Data, and Business Intelligence

Fundamentals of Bayesian Machine Learning Parametric Models

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