Data Science & AI Mastery: 100 Days to Career Success

Tackle messy datasets by cleaning missing values, duplicates, and outliers using pandas tools, imputation, deduplication, normalization, and validation to ensure accurate analysis and robust ai models.

Transform raw data into model-ready features through feature engineering, encoding, scaling, and transformations, and learn how numerical and categorical feature types drive effective pre-processing for machine learning.

Explore exploratory data analysis (EDA) with case studies to understand distributions, detect outliers, visualize relationships, and guide feature engineering and model selection.

Master the machine learning workflow from data to evaluation. Split data into training and testing sets; assess models with accuracy, precision, recall, F1, MAE, RMSE, R2, and cross-validation.

Master regression models for predicting continuous outcomes and classifying with logistic regression. Learn linear, ridge, and lasso regression, regularization to prevent overfitting, and when to use each for reliable predictions.

Learn how classification models predict categorical outcomes with decision trees, random forests, svm, and k-nn, and evaluate them using accuracy, precision, recall, and f1 score, with cross-validation to ensure generalization.

Explore unsupervised learning on unlabeled data, using clustering (k-means) and dimensionality reduction (PCA) to reveal hidden patterns, segment markets, detect anomalies, and visualize insights.

Predict customer churn with a full machine learning workflow—from data understanding and exploratory data analysis to preprocessing, model building, and evaluation—driving targeted retention strategies.

Forecast sales with time series analysis, capturing trend, seasonality, and cycles, and compare arima, random forest, gradient boosting, and lstm models to inform inventory and staffing plans.

Explore collaborative filtering to turn user interaction data into personalized recommendations, covering user-based and item-based methods and challenges like sparsity and cold starts.

Explore how neural networks mimic the brain with layers of perceptrons, learning non-linear patterns through forward propagation, activation functions, and backpropagation guided by loss and gradient descent.

Explore how TensorFlow and Keras enable quick neural network building with sequential, functional, and subclassing APIs, then train, evaluate, and save mNIST models for deployment on CPU, GPU, or TPU.

Apply convolutional neural networks to image classification, learning patterns from pixels to objects, and deploy LSTMs for text processing in natural language processing tasks like sentiment analysis and translations.

Explore how large language models like GPT, BERT, and T5 use transformers and self-attention to understand and generate text, enabling few-shot and zero-shot learning across healthcare, aviation, and business.

Craft prompts to guide large language models toward accurate outputs, exploring zero-shot, one-shot, and few-shot prompting and role-based strategies. Fine-tuning adapts models to domain-specific data for precise results.

Explore how automation, NLP, and chatbots streamline repetitive tasks, boost efficiency, cut costs, and improve customer experiences while enabling data-driven decisions across industries.

Explore how AI tailors to aviation, healthcare, and finance with predictive maintenance, diagnostic tools, and fraud detection. Address ethics and responsible scaling of AI across industries.

Design and build a full AI pipeline from raw data to a business-ready dashboard, delivering a portfolio-worthy end-to-end project that demonstrates data cleaning, model training, and storytelling.

Build a standout data science and AI portfolio that showcases end-to-end projects, clean code on GitHub, dashboards, and measurable business impact. Create an ATS-friendly resume with job keywords and structure.

Present your capstone project on day 100 and describe the end-to-end data pipeline, model, and dashboard that turn data into business value through storytelling.