Objective:
Use Python for data analysis, visualization, machine learning, deep learning, and natural language processing to build intelligent applications and insights from data.
Topics and Examples:
1. Data Analysis: Pandas & NumPy
Python libraries Pandas and NumPy are essential for handling and analyzing large datasets efficiently.
Example:
import numpy as np
import pandas as pd
# NumPy array operations
arr = np.array([1, 2, 3, 4])
print(arr * 2) # [2 4 6 8]
# Pandas DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)
2. Visualization: Matplotlib, Seaborn, Plotly
Visualize data trends and patterns effectively.
Example (Matplotlib & Seaborn):
import matplotlib.pyplot as plt
import seaborn as sns
# Matplotlib
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.title("Line Plot")
plt.show()
# Seaborn
data = sns.load_dataset("tips")
sns.barplot(x="day", y="total_bill", data=data)
plt.show()
Example (Plotly):
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
fig.show()
3. Machine Learning: scikit-learn
Scikit-learn provides tools for regression, classification, clustering, and more.
Example (Linear Regression):
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])
model = LinearRegression()
model.fit(X, y)
print(model.predict([[5]])) # Predicts 10
4. Deep Learning: TensorFlow & PyTorch
Deep learning frameworks for neural networks and AI models.
TensorFlow Example:
import tensorflow as tf
from tensorflow.keras import layers, models
model = models.Sequential([
layers.Dense(10, activation='relu', input_shape=(5,)),
layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')
PyTorch Example:
import torch
import torch.nn as nn
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc = nn.Linear(5, 1)
def forward(self, x):
return self.fc(x)
model = SimpleNN()
x = torch.randn(1, 5)
print(model(x))
5. NLP: NLTK & spaCy
Python libraries for natural language processing tasks like tokenization, named entity recognition, and text analysis.
NLTK Example:
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
text = "Python is amazing for AI."
tokens = word_tokenize(text)
print(tokens)
spaCy Example:
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("Python is amazing for AI.")
for token in doc:
print(token.text, token.pos_, token.dep_)
This section covers Python for Data Science & AI, enabling learners to perform data analysis, visualize insights, implement machine learning and deep learning models, and work with natural language data for AI applications.
