Data Science & AI Mastery: From Basics to Deployment

Learn Python fundamentals for data science with simple syntax, versatile tools like pandas, NumPy, and matplotlib, and a hands-on workflow from input to output.

Explore how Python libraries like NumPy, pandas, Matplotlib, Seaborn, and scikit-learn accelerate data handling, visualization, and machine learning, turning raw data into insights.

Analyze a Netflix dataset of movies and TV shows, import, clean, and prepare data with pandas and NumPy, then visualize trends by genre and release year with matplotlib and seaborn.

Identify and fix common data issues such as missing values, duplicates, outliers, data types, and formatting to transform messy data into accurate, reliable foundations for meaningful analysis and decision making.

Explore feature engineering to transform and create meaningful features, apply encoding and transformation techniques, perform feature selection and dimensionality reduction, and iteratively improve model accuracy through domain knowledge and creativity.

Explore a Netflix user engagement case study by cleaning data, engineering features such as average watch time per day and genre preference, and visualizing insights across countries and genres.

Learn descriptive statistics as the foundation of exploratory data analysis, summarizing data with mean, median, mode, variability, percentiles, skewness, kurtosis, histograms, and box plots to reveal patterns and anomalies.

Explore how data visualization turns raw numbers into clear visuals, using bar, line, histogram, box plot, and scatter plots to reveal patterns, correlations, and insights.

Analyze Amazon customer reviews using descriptive statistics and visualizations like histograms, bar charts, and heat maps to reveal trends by rating, country, and time, turning feedback into actionable business insights.

Explore machine learning fundamentals, including supervised, unsupervised, and reinforcement learning, and follow the data-driven workflow from collection to evaluation to build smarter, adaptive models.

Explore regression techniques, including linear, multiple, and polynomial regression, with regularization via ridge; learn to assess models using R2, MSE, and RMSE for forecasting and data analysis.

Explore supervised classification models, from binary and multi-class to multi-label and imbalanced tasks, and compare logistic regression, decision trees, random forests, and SVMs with accuracy, precision, recall, and F1.

Explore unsupervised learning through clustering, including hard and soft clustering, and key methods like K-means, hierarchical clustering, and DBSCAN, with applications in marketing, anomaly detection, and healthcare.

Reduce high-dimensional data with dimensionality reduction techniques like PCA and t-SNE, addressing the curse of dimensionality, improve speed, prevent overfitting, and enable 2–3D visualization.

Explore how dimensionality reduction simplifies high-dimensional data using PCA and t-SNE to improve visualization, efficiency, and interpretability in real-world datasets like mNIST and movie ratings.

Explore ensemble methods, including bagging, boosting, and stacking, to improve accuracy, reduce overfitting, and build robust predictive models across real world problems.

Select the model for your data and problem, balance bias and variance to avoid underfitting and overfitting, and tune hyperparameters with cross-validation, grid or random search, Bayesian optimization, and AutoML.

Deploy machine learning models to production with batch, real-time, or hybrid deployment, using rest APIs and pipelines, while monitoring drift, data quality, and retraining needs.

Learn the basics of neural networks, from perceptrons and activation functions to feedforward, CNNs, and RNNs, and how training with backpropagation enables real-world AI applications.

Explore TensorFlow and Keras, the open source frameworks for scalable deep learning, with a high level API for building, training, and deploying models across CPU, GPU, and TPU.

Apply a case study on image classification to build, train, and evaluate a convolutional neural network in TensorFlow and Keras, using cats and dogs Kaggle dataset with preprocessing and augmentation.