Machine Learning - Learning Path

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Machine learning is one of the most popular technical fields today, enabling computers to learn from data and make predictions or decisions.

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For beginners, faced with a vast array of algorithms, mathematical theories, and programming tools, it's easy to feel lost and unsure where to begin.

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This article will introduce a learning roadmap from zero knowledge to practical machine learning proficiency.

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Machine Learning - Learning Course List
Basic Introduction	Machine Learning Tutorial
Basic Introduction	Machine Learning Introduction
Basic Introduction	Machine Learning Lifecycle
Basic Introduction	How Machine Learning Works
Basic Introduction	Machine Learning Basic Terms
Basic Introduction	Python for Machine Learning
Basic Introduction	Python Machine Learning Libraries
Basic Introduction	Common Data Types
Basic Introduction	Machine Learning Applications
Data Processing & Statistics	Data Understanding
Data Processing & Statistics	Data Cleaning
Data Processing & Statistics	Feature Engineering
Data Processing & Statistics	Data Visualization
Data Processing & Statistics	Train-Test Set Splitting
Data Processing & Statistics	Foundations of Statistics
Data Processing & Statistics	Probabilistic Thinking
Data Processing & Statistics	Loss Function and Gradient
Data Processing & Statistics	Overfitting, Underfitting, Bias, and Variance
Supervised Learning	Machine Learning Algorithms
Supervised Learning	Linear Regression (Linear Regression)
Supervised Learning	Multiple Linear Regression
Supervised Learning	Polynomial Regression
Supervised Learning	Logistic Regression (Logistic Regression)
Supervised Learning	Regression Model Evaluation
Supervised Learning	Decision Tree (Decision Tree)
Supervised Learning	Support Vector Machine
Supervised Learning	K-Nearest Neighbors Algorithm
Supervised Learning	Ensemble Learning
Supervised Learning	Naive Bayes
Supervised Learning	Random Forest
Supervised Learning	Classification Metrics
Unsupervised Learning	Clustering
Unsupervised Learning	Dimensionality Reduction
Reinforcement Learning	Basic Framework of Reinforcement Learning
Reinforcement Learning	Reinforcement Learning: Exploration vs Exploitation
Reinforcement Learning	Reinforcement Learning: Q-learning and SARSA
Reinforcement Learning	Deep Reinforcement Learning
Deep Learning	Basic Structure of Neural Networks
Deep Learning	Forward and Backward Propagation
Deep Learning	Deep Learning vs Traditional Machine Learning
Deep Learning	Common Network Types
Model Optimization & Engineering	Cross-Validation
Model Optimization & Engineering	Regularization
Model Optimization & Engineering	Data Leakage
Model Optimization & Engineering	Integrated Approaches
Model Optimization & Engineering	Hyperparameter Search
Model Optimization & Engineering	MLOps Concepts
Model Optimization & Engineering	Common Issues Troubleshooting
Limitations & Boundaries of Machine Learning	Interpretability Problem
Limitations & Boundaries of Machine Learning	Hypothesis Limitations
Limitations & Boundaries of Machine Learning	Data Bias
Limitations & Boundaries of Machine Learning	Real-World Cost of Models
Practical Cases	Titanic Survival Prediction
Practical Cases	House Price Prediction
Practical Cases	Customer Segmentation
Practical Cases	PCA Visualization
Practical Cases	Reinforcement Learning Example

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Phase 1: Foundation - Build a Solid Base

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Before diving into complex algorithms, you need to lay the groundwork for your knowledge structure. The goal of this phase is to master essential mathematical, programming, and data analysis skills.

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Core Skill One: Programming Language (Python)

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Python is the universal language in machine learning, favored for its simple syntax and rich ecosystem of libraries.

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Learning Goals: Master Python basics, data structures, functions, and object-oriented programming.

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Key Libraries:

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• NumPy: For efficient numerical computation, the foundation of nearly all scientific computing libraries.
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• Pandas: For data cleaning, analysis, and processing — a powerful tool for manipulating data tables (DataFrames).
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• Matplotlib / Seaborn: For data visualization, turning data into intuitive charts.
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Next, let's look at a case.

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Content of the test data file house_prices.csv:

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\\nArea,Price,House Age,Number of Bedrooms,City\\n45,120,15,1,Beijing\\n60,180,12,2,Beijing\\n75,260,8,2,Beijing\\n90,320,6,3,Beijing\\n110,420,5,3,Beijing\\n130,520,3,4,Beijing\\n50,80,20,1,Chengdu\\n70,120,15,2,Chengdu\\n85,150,12,3,Chengdu\\n100,190,10,3,Chengdu\\n120,240,8,4,Chengdu\\n140,300,5,4,Chengdu\\n55,150,18,1,Shanghai\\n70,220,14,2,Shanghai\\n85,300,10,2,Shanghai\\n100,380,8,3,Shanghai\\n120,480,6,3,Shanghai\\n150,650,4,4,Shanghai\\n40,60,22,1,Wuhan\\n65,95,16,2,Wuhan\\n80,130,12,2,Wuhan\\n95,170,9,3,Wuhan\\n115,220,7,3,Wuhan\\n135,280,5,4,Wuhan\\n

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Example

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\\n# Example：use Pandas and Matplotlib performbasicdata analysis\\n\\nimport pandas as pd\\n\\nimport matplotlib.pyplot as plt\\n\\n# -------------------------- Set Chinese font start --------------------------\\n\\n plt.rcParams['font.sans-serif']=[\\n\\n# Windows Priority\\n\\n'SimHei','Microsoft YaHei',\\n\\n# macOS Priority\\n\\n'PingFang SC','Heiti TC',\\n\\n# Linux Priority\\n\\n'WenQuanYi Micro Hei','DejaVu Sans'\\n\\n]\\n\\n# Fix issue where minus signs display as squares\\n\\n plt.rcParams['axes.unicode_minus']=False\\n\\n# -------------------------- Set Chinese font end --------------------------\\n\\n# 1. Read Data\\n\\n data = pd.read_csv('house_prices