In this lesson, we’ll cover essential Python libraries for machine learning: NumPy, Pandas, Matplotlib, and Scikit-Learn.

#### NumPy

NumPy is a library for numerical computations in Python. It provides support for arrays, matrices, and many mathematical functions.

**Installation**:

```
pip install numpy
```

**Basic Operations**:

```
import numpy as np
# Create an array
arr = np.array([1, 2, 3, 4, 5])
print(arr)
# Basic operations
print(arr + 5) # Add 5 to each element
print(arr * 2) # Multiply each element by 2
# Array slicing
print(arr[1:4])
# Multidimensional arrays
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix)
print(matrix[1, 2]) # Access element at row 1, column 2
```

#### Pandas

Pandas is a powerful library for data manipulation and analysis.

**Installation**:

`pip install pandas`

**Basic Operations**:

```
import pandas as pd
# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)
# Access columns
print(df['Name'])
# Access rows
print(df.iloc[0]) # First row
# Filtering
print(df[df['Age'] > 25])
```

#### Matplotlib

Matplotlib is a plotting library for creating static, animated, and interactive visualizations.

**Installation**:

```
pip install matplotlib
```

**Basic Operations**:

```
import matplotlib.pyplot as plt
# Basic plot
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.title('Basic Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()
# Bar plot
plt.bar(['A', 'B', 'C'], [10, 20, 15])
plt.title('Bar Plot')
plt.show()
```

#### Scikit-Learn

Scikit-Learn is a machine learning library in Python.

**Installation**:

```
pip install scikit-learn
```

**Basic Operations**:

```
from sklearn.linear_model import LinearRegression
import numpy as np
# Create dataset
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 4, 9, 16, 25])
# Create and train model
model = LinearRegression()
model.fit(X, y)
# Make predictions
predictions = model.predict(np.array([[6], [7]]))
print(predictions)
```

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