Based in Toronto, Canada, we're a group of machine learning engineers, quantitative analysts, and quantum computing enthusiasts.
We built an AI investment research platform, the MLQ app. The platform combines fundamentals, alternative data, and ML-based insights.
You can learn more about the platform here and can register for a free account here.
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The articles on MLQ are broadly categorized into Machine Learning, Quantum Computing, and Quantitative Finance.
Machine Learning
- What is Machine Learning? A Complete Guide for Beginners
- The Ultimate Guide to Artificial Intelligence for Business
- What is TensorFlow & How Are Businesses Using It?
- What is Edge AI? Value Propositions & Industry Use Cases
- AI in Advertising: Real-Time Bidding & Reinforcement Learning
- Tensor Processing Units (TPUs) for Accelerated Machine Learning
- AI for Ecommerce: Optimizing Business Processes with Reinforcement Learning
- AI for Ecommerce: Maximizing Revenues with Thompson Sampling
- Directing App Users to Paid Subscriptions with Machine Learning
- An Introduction to Deep Learning with PyTorch
- Introduction to Transfer Learning with TensorFlow 2.0
- An Introduction to DeepDream with TensorFlow 2.0
- Introduction to Recommendation Systems with TensorFlow
- How to Build Production-Level Machine Learning Systems
Unsupervised Learning
Recurrent Neural Networks & LSTMs
- A Complete Guide to Recurrent Neural Networks & LSTMs
- Recurrent Neural Networks (RNNs) and LSTMs for Time Series Forecasting
Convolutional Neural Networks
- How to Build a Convolutional Neural Network in Python with Keras
- What are Convolutional Neural Networks? A Complete Guide to CNNs
- Introduction to Convolutional Neural Networks (CNNs) with TensorFlow
Generative Models
- Generative Modeling: What is a Variational Autoencoder
- What are Generative Adversarial Networks (GANs)
- Synthetic Financial Data with Generative Adversarial Networks (GANs)
- Introduction to Generative Adversarial Networks (GANs): Intuition & Theory
- Introduction to Deep Convolutional GANs (DCGANs)
- Introduction to Wasserstein GANs with Gradient Penalty
- Introduction to Conditional GANs (cGANs) & Controllable Generation
Reinforcement Learning
- What is Reinforcement Learning? A Complete Guide for Beginners
- The Ultimate Guide to Deep Reinforcement Learning
- An Overview of Deep Reinforcement Learning for Trading
- Deep Reinforcement Learning: Guide to Deep Q-Learning
- Deep Reinforcement Learning: Twin Delayed DDPG Algorithm
- Deep Reinforcement Learning for Trading with TensorFlow 2.0
- Implementing Deep Reinforcement Learning with PyTorch: Deep Q-Learning
- Deep Reinforcement Learning for Trading: Strategy Development & AutoML
- Fundamentals of Reinforcement Learning: Estimating the Action-Value Function
- Fundamentals of Reinforcement Learning: Policies, Value Functions & the Bellman Equation
- Fundamentals of Reinforcement Learning: Markov Decision Processes
- Fundamentals of Reinforcement Learning: Dynamic Programming
Natural Language Processing
- Introduction to Natural Language Processing (NLP) with Python
- Introduction to Deep Learning for Natural Language Processing
- Introduction to Natural Language Processing (NLP) with TensorFlow
- Naive Bayes for Sentiment Analysis & Natural Language Processing (NLP)
- Natural Language Processing (NLP) for Sentiment Analysis with Logistic Regression
Mathematics of Machine Learning
- Mathematics of Machine Learning: Introduction to Linear Algebra
- Mathematics of Machine Learning: Introduction to Multivariate Calculus
- Mathematics of Machine Learning: Introduction to Probability Theory
Model Deployment
Data Engineering
- Introduction to Data Engineering, Data Lakes, and Data Warehouses
- Data Lakes vs. Data Warehouses: Key Concepts & Use Cases with GCP
SQL for Data Science
- SQL for Data Science: Selecting and Retrieving Data
- SQL for Data Science: Filtering, Sorting, and Calculating Data
- SQL for Data Science: Subqueries and Joins
Other
Quantum Computing
- What is Quantum Computing? Key Concepts & Industry Use Cases
- Quantum Machine Learning: Introduction to Quantum Systems
- Quantum Machine Learning: Introduction to Quantum Computation
- Quantum Machine Learning: Introduction to Quantum Learning Algorithms
- Introduction to Quantum Programming with Google Cirq
- Quantum Programming with the D-Wave Quantum Annealer
- Introduction to Quantum Programming with Qiskit
- Advanced Mathematics of Quantum Computing
- Quantum Machine Learning: Introduction to TensorFlow Quantum
Quantitative Finance
- Machine Learning for Finance: Price Prediction with Linear Regression
- Classification-Based Machine Learning for Finance
- Introduction to Python for Finance and Algorithmic Trading
- Python for Finance: Data Visualization
- Python for Finance: Time Series Analysis
- Python for Finance: Portfolio Optimization
- Introduction to Algorithmic Trading with Quantopian
- Introduction to the Capital Asset Pricing Model (CAPM) with Python
- Introduction to Quantitative Modeling: Linear Models
- Introduction to Quantitive Modeling: Probabilistic Models
- Introduction to Quantitative Modeling: Regression Models
- Introduction to Portfolio Construction and Analysis: Risk & Returns
- Introduction to Markowitz Portfolio Optimization and the Efficient Frontier
- What is Quantamental Investing?
Contact Us
Please get in touch with any queries relating to MLQ at support@mlq.ai.