Machine Learning FULL Course with Practical (10 HOURS) | Learn Free ML in 2024 | Part-1

Introduction

This tutorial provides a comprehensive guide to Machine Learning (ML) based on the "Machine Learning FULL Course with Practical" video by WsCube Tech. It covers foundational concepts, data preprocessing techniques, regression analysis, and classification methods, making it suitable for beginners looking to learn ML in 2024.

Step 1: Understand Machine Learning

• Define Machine Learning
  • Machine Learning is a subset of artificial intelligence that involves training algorithms to learn patterns from data and make predictions.
• Identify its applications in various fields, like finance, healthcare, and marketing.

Step 2: Explore the Roadmap to Learn Machine Learning

• Familiarize yourself with the following key areas:
  • Statistics and Probability
  • Programming (Python is commonly used)
  • Data Manipulation and Cleaning
  • Model Selection and Evaluation

Step 3: Understand Types of Variables in Machine Learning

• Identify the different variable types:
  • Continuous
  • Categorical
• Learn how these types affect data analysis and model selection.

Step 4: Perform Data Cleaning

• Importance of data cleaning:
  • Ensures the quality and accuracy of your data.
• Steps involved in data cleaning include:
  • Identifying and handling missing values.
  • Removing duplicates.
  • Correcting data types.

Step 5: Handle Missing Values

• Understand how to find and handle missing values:
  • Use Pandas to identify missing values in your dataset.
  • Decide between dropping missing values or imputing them.

Dropping Missing Values

• Use the following code snippet to drop missing values:

  df.dropna(inplace=True)
  

Imputing Missing Values

• For categorical data, use the mode for imputation:

  df['column_name'].fillna(df['column_name'].mode()[0], inplace=True)
  

Step 6: Apply Encoding Techniques

• Learn about One Hot Encoding and Label Encoding:
  • One Hot Encoding: Converts categorical variables into binary vectors.
  • Label Encoding: Converts categories into numerical values.

Example of One Hot Encoding

df = pd.get_dummies(df, columns=['categorical_column'])

Step 7: Handle Outliers

• Understand what outliers are and their impact on models.
• Techniques to detect and remove outliers:
  • Interquartile Range (IQR)
  • Z-Score

Removing Outliers using IQR

Q1 = df['column'].quantile(0.25)
Q3 = df['column'].quantile(0.75)
IQR = Q3 - Q1
df = df[(df['column'] >= (Q1 - 1.5 * IQR)) & (df['column'] <= (Q3 + 1.5 * IQR))]

Step 8: Feature Scaling

• Learn the importance of feature scaling in ML models.
• Types of scaling:
  • Standardization: Centers the data around the mean.
  • Normalization: Scales data to a range of [0, 1].

Standardization Example

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)

Step 9: Train-Test Split

• Understand the significance of splitting your dataset into training and testing sets:
  • Typically, use an 80-20 or 70-30 split.

Example Code for Train-Test Split

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 10: Explore Regression Analysis

• Understand the basics of regression:
  • Linear Regression: Establishes a relationship between dependent and independent variables.

Simple Linear Regression Code Example

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

Step 11: Classification Techniques

• Overview of classification and its types:
  • Logistic Regression: Used for binary classification.

Example of Logistic Regression

from sklearn.linear_model import LogisticRegression
log_model = LogisticRegression()
log_model.fit(X_train, y_train)

Step 12: Evaluate Model Performance

• Learn about evaluation metrics:
  • Confusion Matrix
  • Sensitivity, Precision, Recall, F1-Score

Example of Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predictions)

Conclusion

This tutorial has provided a structured approach to understanding and implementing Machine Learning concepts, from data cleaning to model evaluation. As a next step, consider practicing with real datasets and projects to solidify your knowledge and skills. Explore additional resources or the complete course for deeper insights into advanced topics.