- Intuition Behind Linear Regression
- Logistic Regression From Scratch
- Feature Scaling
- Gradient Boosting vs. Random Forest Machines
- Multicollinearity
- Generative vs. Discriminative Classifiers
- L1 and L2 Regularization
- Decision Trees and Random Forest Fundamentals
- Boosting
- SMOTE (Synthetic Minority Oversampling Technique)
- Cross-Validation
- Model Selection and Hyperparameter Tuning
- Stats for ML & DS
- Advanced Data Preprocessing
- K-Means From Scratch
- Elbow & Silhouette Method for Optimal Value of K in KMeans
- PCA (Principal Component Analysis)
- Introduction to t-SNE
- Comprehensive data exploration with Python
- Stats for ML & DS
- Everthing You Can Do With Time Series
- Intro to RNN, LSTM, and GRU Using Time Series
- Artificial Neural Network
- ANNs
- Deep Neural Networks
- Learning XOR
- Feedforward Neural Networks
- Neural Networks, Manifolds, and Topology
- Regularization for Deep Learning
- L1 and L2 Regularization
- Optimization for Training DeepModels
- Stochastic Gradient Descent, Momentum, and Adaptive Learning rates
- Convolutional Networks
- The Convolution Operation and Pooling
- Convolutional Neural Network (CNN) Tutorial
- Conv Nets: A Modular Perspective
- Sequence Modeling: Recurrent and Recursive Nets
- Teacher Forcing
- LSTM Networks
- Recurrent Neural Network Guide: a Deep Dive in RNN
- Transformer Architecture
- Attention Is All You Need
- Solving Transformer by Hand: A Step-by-Step Math Example
- A Large Language Model From Scratch
- How to Do A/B Testing
- A/B Testing with Python
- Free Trial Screener
- Check Which Newsletter Brings Higher Traffic
- Conversion Rate Optimization
- Predicting Wine Type and Quality With Keras
- Handwritten Digit Recognition
- Learning OR Operator
- Chatbots (Open and Closed Domain)
- Retrieval Chatbot Training and Implementation(closed domain)
- Generative Chatbot Training and Implementation with seq2seq Models(open domain)
- Predict Car Fuel Efficiency
- 1970-1982
- 1983-2017
- How to Identify Overfitting Machine Learning Models in Scikit-Learn
- Intro to Sequence to Sequence Learning in Keras
- How to Develop an Encoder-Decoder Model for Seq2seq Prediction in Keras
- Simulation Modeling Introduction
- Monte Carlo Simulation
- Simulate the Spread of an Infectious Disease
- Simulate Rolling Die and Drawing From a 52 Deck of Cards
- Expected Number of Trials to See All N Numbers in an N-sided Dice.
- Random Forest Not Ideal For Extrapolating Data Points
- Predicting stock prices with an MA, EMA, and LSTM models
- Intro to Economic Modeling and Data Science
- Intersection of Lists
- K Closest Points
- K-th Smallest Value in Matrix
- Max Product of Three Values in Arrays
- Binary Tree Mirror Image
- Binary Tree Generator
- Sum of Largest Contiguous Subarray
- Peak Element in Array
- Calculate Correlation
- Calculate Diameter of Binary Tree
- Binary Tree Generator 2.0
- Social Graph of Facebook
- Graph Generator
- Count Number of Triangles
- Anagram Substring between 2 Strings
- Non-overlapping Intervals
- List of List of Anagrams
- Number of Friend Groups
- Remove K-th Element in Linked List
- Linked List Generator
- Estimate
using Monte Carlo Method - Remove the Minimum Number of Parentheses
- Generate All Permutations
- Sample From List of Weighted Categories
- Maximum Length of a Common Subarray
- Maximum Sum of Increasing Subsequence
- Smallest Number of Perfect Squares Sum to N
- All Combinations of K Numbers From 1 to N
- More