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Written Edition English Tutorial
Machine Learning Course with Python – Free Training | Vista Academy
Curriculum
12 Sections
49 Lessons
10 Weeks
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Module 1: Introduction to Machine Learning
Give students a clear foundation in what Machine Learning is, how it differs from related fields, and where it’s applied in real life.
6
1.1
What Is Machine Learning? A Complete Beginner’s Guide with Real-Life Examples
1.2
Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
1.3
Quiz Introduction to Machine Learning & Its Types
6 Questions
1.4
AI vs Machine Learning vs Deep Learning: Complete Beginner’s Guide
1.5
Machine Learning Lifecycle: From Data Collection to Model Deployment
1.6
Quiz: AI vs ML vs DL + Machine Learning Lifecycle
5 Questions
Module 2: Python Essentials for ML
5
2.1
Python Basics for Machine Learning: Variables, Data Types, Loops & Functions
2.2
Jupyter Notebook vs Google Colab: Complete Beginner’s Guide for Machine Learning
2.3
NumPy vs Pandas: Python Libraries for Machine Learning (2026)
2.4
Data Preprocessing for Machine Learning: Data Cleaning and Preparation
2.5
Quiz: Python Essentials for Machine Learning – Module 2 Recap
10 Questions
Module 3: Data Wrangling & Visualization, starting with:
5
3.1
How to Read and Explore Datasets in Python with Pandas
3.2
Summary Statistics in Pandas: Using describe(), info(), shape & dtypes
3.3
Matplotlib Tutorial for Beginners: Complete Guide to Data Visualization in Python
3.4
Lesson 4: Data Visualization with Seaborn – Bar, Line & Histograms
3.5
Data Wrangling & Visualization Quiz – Pandas, Matplotlib & Seaborn
5 Questions
Module 4: Data Preprocessing
5
4.1
Feature Scaling in Machine Learning Using Python
4.2
Lesson 2: Encoding Categorical Variables
4.3
Lesson 3: Feature Scaling – Standardization & Normalization
4.4
Lesson 4: Train-Test Split & Introduction to Cross Validation
4.5
Data Preprocessing Quiz – Missing Values, Encoding, Scaling & Split
10 Questions
Module 5: Regression Algorithms!
This module introduces the foundational concept of regression in machine learning — perfect for prediction tasks like house prices, salaries, etc.
5
5.1
Lesson 1: Introduction to Regression Problems
5.2
Lesson 2: Linear Regression – Theory + Implementation
5.3
Lesson 3: Model Evaluation – MAE, MSE, R² Explained
5.4
Lesson 4: Mini Project – Predict Housing or Salary Data
5.5
Regression Algorithms Quiz – Linear Regression & Model Evaluation
10 Questions
Module 6: Classification Algorithms
5
6.1
Logistic Regression in Machine Learning – Theory & Python Implementation
6.2
Lesson 2: K-Nearest Neighbors (KNN)
6.3
Naive Bayes in Machine Learning – Bayes’ Theorem & Python Example
6.4
Titanic Survival Prediction – Machine Learning Classification Case Study
6.5
Classification Algorithms Quiz – Logistic, KNN, Naive Bayes
10 Questions
Module 7: Decision Trees
6
7.1
Decision Tree Classifier in Machine Learning – Gini vs Entropy, Pruning & Python Code
7.2
Random Forest in Machine Learning – Bagging, Key Parameters & Python Code
7.3
Decision Tree vs Random Forest – Hands-on Comparison with Python
7.4
Interpreting Decision Trees & Random Forests – Feature Importance, Permutation
7.5
Customer Churn Prediction – Decision Tree & Random Forest Mini Project
7.6
Decision Trees & Random Forest Quiz – Gini, Entropy, Ensembles
10 Questions
Module 8: Model Evaluation & Tuning
5
8.1
Accuracy vs Precision vs Recall vs F1 (ML Metrics)
8.2
Confusion Matrix & ROC-AUC Explained (with Python)
8.3
Overfitting vs Underfitting & Regularization (L1/L2)
8.4
Cross Validation & Grid Search in Python (Scikit-learn)
8.5
Model Evaluation & Tuning Quiz – Metrics, ROC/PR, CV & Grid Search
10 Questions
Module 9: Unsupervised Learning
5
9.1
K-Means Clustering in Machine Learning – Intuition & Python
9.2
Elbow Method to Choose K in K-Means
9.3
Hierarchical Clustering (Agglomerative) – Linkage & Dendrograms
9.4
Customer Segmentation with K-Means (RFM) – Real Use Case
9.5
Unsupervised Learning – Quiz
10 Questions
Module 10: Dimensionality Reduction
5
10.1
Curse of Dimensionality in Machine Learning – Explained
10.2
Introduction to PCA – Principal Component Analysis
10.3
PCA in Scikit-Learn – Pipelines, n_components & Explained Variance
10.4
Visualizing High-Dimensional Data with PCA
10.5
Dimensionality Reduction & PCA in Machine Learning – Easy Guide
10 Questions
Module 11: Capstone Project Preparation
5
11.1
Project Guidelines and Dataset Selection – Capstone Preparation
11.2
Define Problem Statement and Goals – ML Capstone Prep
11.3
Exploratory Data Analysis and Cleaning – ML Capstone Prep
11.4
Initial Modeling and Evaluation Planning – Capstone Project Prep
11.5
Capstone Project Preparation – Quiz
10 Questions
Module 12: Capstone Project Execution
5
12.1
Final Model Development – ML Capstone Execution
12.2
Model Evaluation and Tuning – ML Capstone
12.3
Final Report and Presentation – ML Capstone
12.4
Group Presentation and Viva – ML Capstone Execution
12.5
Capstone Project Execution – Model Development to Final Presentation
10 Questions
Final Report and Presentation – ML Capstone
📝 Final Report & Slide Deck
Tell a clear, business-oriented story: problem, data, approach, results, risks, and next steps.
Report Structure (6–10 pages)
Executive Summary
– problem, ROI, headline metric
Data
– sources, schema, quality, ethics
Method
– features, algorithms, validation
Results
– metrics, charts, threshold justification
Risks & Bias
– limitations, error analysis, mitigation
Deployment Plan
– inputs, outputs, monitoring, fallback
Slides (8–12 max)
1–2: Problem & success metric
3–4: Data summary & key EDA visuals
5–6: Approach & model comparison
7–8: Final results & business impact
9–10: Risks, next steps, demo link
Artifacts
Appendix: full metrics, confusion matrix, feature importance
Link to repo & README (run instructions)
Model card v1.0 (summary of use & limits)
Deliverables (Lesson 3):
PDF report, 10-slide deck, model card, repository link.
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