Machine Learning A-Z From Foundations to Deployment

Explore logistic regression as a classification technique with nonlinear transformation and sigmoid loss, using regularization, cross-validation, and data preprocessing for churn prediction and binary or multi-class tasks.

Explore natural language processing basics, including tokenization, bag of words, tf-idf, and word2vec, using coronavirus datasets and matplotlib visuals, with transfer learning and pretrained vectors like glove and google news.

Explore the machine learning lifecycle with a focus on text preprocessing for word embedding, including tokenization, cleaning, stopwords, stemming, and tf-idf with Amazon reviews.

Explore text feature extraction with bag of words and tf-idf, apply logistic regression with regularization, and demonstrate a linear regression example on Boston housing data.

Explore how a decision tree classifier and regressor build predictive models by splitting data at root and internal nodes using entropy, information gain, Gini impurity, and majority voting.

Learn ensemble models with random forest, bootstrap sampling, and model aggregation for robust predictions. Understand bias-variance trade-off and bagging, then deploy a Flask-based loan prediction app.

Explore the k-means algorithm for unsupervised learning, including initialization methods (k-means++ vs random), centroid updates, and elbow method to choose k, with iris data as example.

Learn how mlops and devops for data scientists enable containerized machine learning deployments from development and testing to production, with version control, automation, ci/cd, and monitoring.

Learn how DevOps practices empower data scientists to collaborate with engineers, deploy machine learning models via CI/CD, and use infrastructure as code for reliable, reproducible workflows.

Apply DevOps and MLops principles to data science workflows, ensuring reproducibility and version control, collaboration with data engineers, and automated CI/CD from development to production.

Analyze Docker, Singularity, Kubernetes, and Portman container types for data science in DevOps and MLOps; learn container images, CI/CD, and infrastructure as code with Terraform and Kubernetes.

Explore how to monitor and manage containers in production using Kubernetes, Prometheus, Grafana, and rolling updates to ensure high availability and efficient resource utilization.

Optimize resource usage and scalable deployment by embracing containerization, reproducible environments with explicit dependency versions, Git-based version control, and serverless container technologies and microservices for data science applications.

Learn feature engineering for regression by deriving depart and arrival time features, converting date times, encoding stops and airline categories, then compare models from elastic net to XGBoost with cross-validation.

Explore a flight fare prediction project using a Kaggle dataset with train and test splits. Learn data cleaning and preprocessing, perform feature exploration, and build models to predict airline prices.

Learn to deploy a flight price predictor by integrating a trained XGBoost model into a Flask app, using templates and GET/POST forms to input route details and return the price.