Python Machine Learning Guide: From zero basics to master level, your AI dream starts here-Python Tutorial-php.cn

Python Machine Learning Guide: From zero basics to master level, your AI dream starts here

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Release： 2024-02-23 17:20:38

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Python 机器学习指南：从零基础到大师级，你的 AI 梦想从此起航

Chapter 1: Python Basics

Before starting machine learning, you need to master some python basic knowledge. This chapter covers the basic syntax, data types, control structures and functions of Python. If you are already familiar with Python, you can skip this chapter.

# 注释

# 变量

x = 5
y = "Hello, world!"

# 数据类型

print(type(x))# 
print(type(y))# 

# 控制结构

if x > 0:
print("x is positive.")
else:
print("x is not positive.")

# 函数

def my_function(x):
return x * 2

print(my_function(5))# 10

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Chapter 2: Basics of Machine Learning

This chapter will introduce the basic knowledge of machine learning, including the definition, classification, and evaluation methods of machine learning. You'll learn what machine learning can do and how to choose the right machine learning algorithm.

# 导入必要的库

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 加载数据

data = pd.read_csv("data.csv")

# 划分训练集和测试集

X = data.drop("target", axis=1)# 特征数据
y = data["target"]# 标签数据

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练模型

model = LinearRegression()
model.fit(X_train, y_train)

# 评估模型

score = model.score(X_test, y_test)
print("准确率：", score)

# 预测

predictions = model.predict(X_test)

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Chapter 3: Commonly used machine learning algorithms

This chapter will introduce some commonly used machine learning algorithms, including linear regression, logistic regression, decision trees, support vector machines, random forests, etc. You will learn the principles and characteristics of each algorithm, and how to use these algorithms to solve practical problems.

# 导入必要的库

from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LoGISticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

# 加载数据

data = pd.read_csv("data.csv")

# 划分训练集和测试集

X = data.drop("target", axis=1)# 特征数据
y = data["target"]# 标签数据

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练模型

models = [
LinearRegression(),
LogisticRegression(),
DecisionTreeClassifier(),
SVC(),
RandomForestClassifier()
]

for model in models:
model.fit(X_train, y_train)

# 评估模型

score = model.score(X_test, y_test)
print(model.__class__.__name__, "准确率：", score)

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Chapter 4: Deep Learning

This chapter will introduce the basic knowledge of

deep learning

, including the structure and principles of neural network, commonly used activation functions, loss functions and optimization algorithms, etc. You'll learn what deep learning can do and how to use deep learning to solve real-world problems.

# 导入必要的库

import Tensorflow as tf

# 定义神经网络模型

model = tf.keras.Sequential([
tf.keras.layers.Dense(100, activation="relu"),
tf.keras.layers.Dense(10, activation="softmax")
])

# 编译模型

model.compile(optimizer="adam", loss="sparse_cateGorical_crossentropy", metrics=["accuracy"])

# 训练模型

model.fit(X_train, y_train, epochs=10)

# 评估模型

score = model.evaluate(X_test, y_test)
print("准确率：", score[1])

# 预测

predictions = model.predict(X_test)

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