Go from Python and EDA to statistics, visualisation and supervised learning. Train, evaluate and compare regression and classification models.
Introduction
Go from Python and EDA to statistics, visualisation and supervised learning. Train, evaluate and compare regression and classification models.
Learning outcome
Prepare data, apply statistics, visualise insights, and train and evaluate regression and classification models using a proper ML workflow.
Curriculum
Click any module to expand and view topics and hands-on labs included.
Python setup, data types, collections, functions, files • 14 hrs
Hands-on lab
Results
Summarise data, reason about uncertainty and interpret p-values, correlation and sampling bias.
Create static and interactive charts with Matplotlib, Seaborn, Plotly and Cufflinks.
Build linear, multiple, logistic, k-NN and decision-tree models with scikit-learn.
Choose metrics, use cross-validation and avoid leakage, overfitting and class-imbalance traps.
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Join live, hands-on classes at our Goa centres, with a certificate and placement support. See every Machine Learning course we offer, or visit the centre closest to you.
Build the Python and data-analysis foundations for machine learning: Python for data work, NumPy, pandas, data cleaning and exploratory data analysis.
The complete job-ready track: 120 hours covering Python, EDA, statistics, visualisation, supervised and unsupervised learning, feature engineering and a portfolio capstone.