Krishnakanth Allika

Join me on my AI adventure! This website is a living document of my learning journey, filled with detailed notes and practical code examples for data science, machine learning, and AI enthusiasts.

Data Science Focus Areas

Data Science

Data Scraping, Data Pre-processing, Missing values, Imputation, Rescaling, Data Manipulation, Dimension reduction, Data Visualization, Descriptive Statistics, Exploratory Data Analysis (EDA), ...

Machine Learning

Feature Engineering, Principal Component Analysis (PCA), Classification, Clustering, Linear Regression, Logistic Regression, Naive Bayes, KNN, Decision Tree, K-Means, Supervised learning, Unsupervised learning, Support Vector Machines (SVM), ...

Deep Learning

Artificial Neural Networks (ANN), Activation functions (ReLu, Softmax, etc), Feed Forward Networks, Convolution Neural Networks (CNN), Recurring Neural Networks (RNN), Convolution Graph Network (CGN), Natural Language Processing, Speech to text, Reinforcement Learning, Machine Translation, ...

Generative AI

Generative AI, Diffusion Models, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Mode Collapse, Hallucination, LLM (Large Language Model), Transformer, Attention Mechanism, Tokenization, Fine-tuning, Prompt Engineering, Context Window, Temperature Parameter, Bias, Misinformation, Copyright and Ownership, ...

Python

Matplotlib, Seaborn, NumPy, SciPy, Pandas, Scikit-Learn, Statsmodels, NLTK, PyTorch, pyTesseract, Keras, BeautifulSoup, TensorFlow, XGBoost, ...

R

Swirl, Tidyverse (dplyr, tidyr, etc), ggplot2, Shiny, Caret, Knitr, Lubridate, BioConductor, mlr3, XGBoost, ...

Julia

DataFrames, Plots, ScikitLearn, PyCall, RCall, Knet, TensorFlow, MXNet, DecisionTree, Clustering, Merlin, MachineLearning, MLDatasets, MLKernels, ...