**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.

We also write a weekly AI newsletter that includes news, resources, and more. You can sign up for our This Week in AI newsletter here.

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.