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Machine Learning: Level-01

Machine Learning Track

Machine Learning:
Level-01

4.8 (1,230 reviews)

Go from Python and EDA to statistics, visualisation and supervised learning. Train, evaluate and compare regression and classification models.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Machine Learning Level-01 Course
2 Months Duration
Certificate On Completion
Level-01Level
5 ModulesSyllabus
2 Months • 82 HoursDuration
EnglishLanguage
CertificateIncluded
Introduction

Overview

Go from Python and EDA to statistics, visualisation and supervised learning. Train, evaluate and compare regression and classification models.

Python pandas Statistics Visualisation Scikit-learn Supervised ML
Goal

Learning Outcome

Prepare data, apply statistics, visualise insights, and train and evaluate regression and classification models using a proper ML workflow.

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
Results

What You Will Learn

Statistics for ML

Summarise data, reason about uncertainty and interpret p-values, correlation and sampling bias.

Data Visualisation

Create static and interactive charts with Matplotlib, Seaborn, Plotly and Cufflinks.

Regression & Classification

Build linear, multiple, logistic, k-NN and decision-tree models with scikit-learn.

Model Evaluation

Choose metrics, use cross-validation and avoid leakage, overfitting and class-imbalance traps.

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