Based in Toronto, Canada, we're a group of machine learning engineers, quantitative analysts, and crypto enthusiasts.
Below you'll find the two projects we're currently working on:
- MLQ App: AI investment Research Platform for Equities & Crypto
- MLQ VC: Discover Recently-Funded Tech Startups
MLQ App
We built an AI investment research platform, the MLQ app...
The platform combines fundamentals, alternative data, and ML-based insights for both equities and crypto.
You can learn more about the platform here and can register for a free account here.
MLQ VC
Each week, we publish a report of recently-funded tech companies. The report includes 100+ recently-funded tech companies including the industry, funding amount, verified CEO contact information, and more.
You can learn more about the MLQ VC service here.
MLQ Articles
The articles on MLQ are broadly categorized into Machine Learning, Quantum Computing, and Quantitative Finance. Below are a few top articles from each category.
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?
- Applications of AI and Machine Learning in Venture Capital
Contact Us
You can contact us with any queries relating to MLQ at support@mlq.ai.