The Bright Academy of Machine Learning

At Bright, we run internal sessions on Data Science and Machine Learnings.

These sessions are run by our Founders and Data Science Leaders: Avi Patchava, Amit Bendale, Varun Modi. Raghava D, Shashank S.

We have structured our curriculum around 26 sessions, each 1.5 hours long. We run these sessions using a lot of whiteboard space. We write out all our examples and proofs, where we need to.

These sessions are open to all Team Members at Bright. They are mandatory for all tech team members regardless of which sub-team (e.g., front-end, back-end, database, etc.) they work in. We believe all Tech leaders at Bright need to have a decent understanding of data science and machine learning.

• Bias Variance Trade-off
• Hypothesis testing
• Naive Bayes and Bayes's Theorem
• Linear Regression
• Logistic Regression
• Clustering algorithms
• Decision Trees
• Linear Discriminant Analysis
• Support Vector Machines
• Ensemble principles
• Association Rules
• Time-series
• KNN and nearest neighbour algorithms
• Anomaly Detection and Fraud
• Neural Networks
• RNNs and series data
• CNNs and image processing
• Deep Learning principles
• Gradient Boosting Trees
• Dimensionality Reduction and PCA
• Linear programming and Optimization
• Recommendation systems
• Network analysis
• NLP
• Reinforcement Learning