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Machine Learning: Level-02

Machine Learning Track

Machine Learning:
Level-02

4.8 (1,230 reviews)

The complete job-ready track: 120 hours covering Python, EDA, statistics, visualisation, supervised and unsupervised learning, feature engineering and a portfolio capstone.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Machine Learning Level-02 Course
3 Months Duration
Certificate On Completion
Level-02Level
8 ModulesSyllabus
3 Months • 120 HoursDuration
EnglishLanguage
CertificateIncluded
Introduction

Overview

The complete job-ready track: 120 hours covering Python, EDA, statistics, visualisation, supervised and unsupervised learning, feature engineering and a portfolio capstone.

Python Statistics Scikit-learn Clustering Feature Engineering Git & GitHub
Goal

Learning Outcome

Build, evaluate and communicate machine learning models end to end, and finish with portfolio-ready projects on real-world datasets.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on builds included.

  • Python setup, Jupyter notebooks, variables, data types, operators and type conversion
  • Strings, lists, tuples, dictionaries, sets and common collection operations
  • Conditions, loops, functions, parameters, return values and reusable code
  • Imports, reading CSV files, exceptions, debugging and writing readable scripts
Hands-on Build
Write a Python notebook that loads a small dataset, performs calculations and prints a clear summary
  • NumPy arrays, shape, dimensions, indexing, slicing, vectorised operations and aggregations
  • pandas Series and DataFrames; reading CSV/Excel; selecting, filtering, sorting and renaming
  • Missing values, duplicates, inconsistent labels, data types, date parsing and outlier checks
  • groupby, aggregation, merge, concatenation, pivot tables and export
  • EDA workflow: ask questions, inspect distributions, compare groups and document findings
Hands-on Build
Clean a messy dataset and create a transformation logProduce an EDA notebook with evidence-based findings
  • Descriptive statistics: mean, median, mode, range, variance, standard deviation and quartiles
  • Probability basics, common distributions, sampling and central limit theorem concepts
  • Population vs sample, sampling bias, correlation and covariance
  • Confidence intervals and hypothesis-testing concepts; p-values and practical interpretation
  • Correlation does not imply causation; assumptions, uncertainty and limitations
Hands-on Build
Prepare a statistical summary for a dataset and explain the main patterns, uncertainty and caveats
  • Matplotlib: line, bar, histogram, scatter, box plots, labels, titles and annotations
  • Seaborn: distribution plots, count plots, pair plots, heatmaps and categorical comparisons
  • Plotly interactive charts, hover details, filters and dashboard-style visuals
  • Cufflinks workflow for interactive pandas plotting where supported by the environment
  • Choosing appropriate charts, avoiding misleading scales and writing insight-led captions
Hands-on Build
Build a visual report containing static and interactive charts that answer a set of data questions
  • Machine learning workflow: define target, split data, establish baseline and avoid data leakage
  • Regression: linear and multiple regression, assumptions, residuals and error measures
  • Classification: logistic regression, k-nearest neighbours and decision trees
  • Feature encoding, scaling, train/validation/test sets and preprocessing pipelines
  • Evaluation: MAE, MSE/RMSE, R², accuracy, precision, recall, F1 and confusion matrix
  • Overfitting, underfitting, cross-validation, class imbalance and introductory hyperparameter tuning
Hands-on Build
Train and compare regression or classification modelsSelect suitable metrics and explain performance trade-offs
  • Supervised vs unsupervised learning and common use cases
  • Distance measures, feature scaling and similarity concepts
  • K-means clustering, choosing cluster count and interpreting centroids
  • Hierarchical clustering and dendrogram concepts
  • Dimensionality reduction with PCA: intuition, scaling and explained variance
  • Cluster validation concepts and responsible interpretation of segments
Hands-on Build
Create a clustering notebook and describe the segments foundExplain limitations of the approach
  • Missing-value strategies, categorical encoding, scaling and feature transformations
  • Feature selection concepts and leakage prevention
  • Pipelines, reproducibility, random states and repeatable experiments
  • Cross-validation, baseline comparisons and error analysis
  • Model interpretability basics, feature importance and documenting assumptions
Hands-on Build
Create a reproducible modelling pipelineBuild a comparison table for multiple candidate models
  • Choose a meaningful question and identify a public, synthetic or anonymised dataset
  • Define target or clustering objective, scope, success criteria and data limitations
  • Build an end-to-end workflow: cleaning, EDA, feature preparation, training and evaluation
  • Compare a baseline with candidate models; explain metric choices and limitations
  • Present results using clear charts, a README, notebook and concise project walkthrough
Hands-on Build
Complete a portfolio-ready project with a reproducible notebook and model evaluationDeliver a short project presentation
Results

What You Will Learn

Supervised Learning

Train and compare regression and classification models with suitable metrics.

Unsupervised Learning

Apply K-means, hierarchical clustering and PCA, and interpret segments responsibly.

Feature Engineering

Build reproducible pipelines with encoding, scaling, leakage prevention and error analysis.

Portfolio Capstone

Deliver an end-to-end project with notebook, README, evaluation and presentation.

Testimonials

What Our Students Say

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