9-Day Machine Learning Mastery

Course Overview

Day 1: Introduction to Machine Learning

Theory & Notes:
- What is ML? Types: Supervised, Unsupervised, Reinforcement Learning.
- Overview of common ML algorithms.
- Bias-Variance tradeoff, underfitting vs. overfitting.
- Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.
Technical Stuff:
- Install Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, and Scikit-Learn.
Hands-on Project:
- Load a dataset (e.g., Titanic dataset).
- Perform exploratory data analysis (EDA): missing values, distributions, correlations.

Day 2: Data Preprocessing & Feature Engineering

Theory & Notes:
- Data cleaning, handling missing values, encoding categorical variables.
- Feature scaling: MinMaxScaler, StandardScaler.
- Feature selection techniques.
Technical Stuff:
- Using Pandas & Scikit-Learn for preprocessing.
Hands-on Project:
- Work with the Titanic dataset: clean data, handle missing values, and engineer new features.

Day 3: Supervised Learning - Regression Models

Theory & Notes:
- Linear Regression, Polynomial Regression.
- Ridge, Lasso Regression.
- Gradient Descent, Cost Function.
Technical Stuff:
- Implement Linear Regression from scratch using NumPy.
- Use Scikit-Learn for regression models.
Hands-on Project:
- Predict house prices using the Boston Housing dataset.

Day 4: Supervised Learning - Classification Models

Theory & Notes:
- Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM).
- Hyperparameter tuning (GridSearchCV, RandomizedSearchCV).
Technical Stuff:
- Implement a decision tree and logistic regression classifier using Scikit-Learn.
Hands-on Project:
- Classify whether a person has diabetes using the Pima Indians Diabetes dataset.

Day 5: Unsupervised Learning - Clustering & Dimensionality Reduction

Theory & Notes:
- K-Means, Hierarchical Clustering, DBSCAN.
- PCA (Principal Component Analysis), t-SNE.
Technical Stuff:
- Implement K-Means and PCA using Scikit-Learn.
Hands-on Project:
- Customer segmentation using the Mall Customers dataset.

Day 6: Neural Networks & Deep Learning (Basics)

Theory & Notes:
- Introduction to Artificial Neural Networks (ANN).
- Activation functions (ReLU, Sigmoid, Softmax).
- Backpropagation and optimization.
Technical Stuff:
- Use TensorFlow and Keras to build a simple neural network.
Hands-on Project:
- Classify handwritten digits using the MNIST dataset.

Day 7: Advanced Deep Learning - CNNs & RNNs

Theory & Notes:
- Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- Long Short-Term Memory (LSTM) networks.
Technical Stuff:
- Build CNNs and LSTMs using TensorFlow/Keras.
Hands-on Project:
- Build an image classifier using the CIFAR-10 dataset.
- Sentiment analysis on movie reviews using LSTMs.

Day 8: Reinforcement Learning & Model Deployment

Theory & Notes:
- Introduction to Reinforcement Learning, Q-Learning.
- Model deployment using Flask/FastAPI.
Technical Stuff:
- Use OpenAI Gym for reinforcement learning.
- Deploy a trained model using Flask.
Hands-on Project:
- Train an agent to play CartPole using OpenAI Gym.

Day 9: Real-World Project & Final Review

Final Project:
- Pick a real-world dataset (Kaggle, UCI ML Repository).
- Apply everything learned (EDA, feature engineering, model training, evaluation).
- Deploy the model and document results.
Final Review:
- Revise notes, revisit concepts, and ensure a strong understanding.

Let's start with Day 1!

Day 1: Introduction to Machine Learning - Detailed Plan

1. Theory & Notes

Before diving into coding, start by understanding the core concepts of machine learning.

What is Machine Learning?

Machine learning is a field of artificial intelligence where computers learn from data to make predictions or decisions without being explicitly programmed.

Types of Machine Learning

Supervised Learning: Model learns from labeled data (input-output pairs).
- Example: Predicting house prices based on past sales data.
Unsupervised Learning: Model finds patterns in unlabeled data.
- Example: Clustering customers based on shopping behavior.
Reinforcement Learning: Model learns through rewards and penalties.
- Example: Training an AI to play chess.

Common ML Algorithms

Supervised Learning:
- Regression: Linear Regression, Decision Trees, Random Forests
- Classification: Logistic Regression, SVM, Neural Networks
Unsupervised Learning:
- Clustering: K-Means, DBSCAN
- Dimensionality Reduction: PCA
Reinforcement Learning:
- Q-Learning, Deep Q Networks (DQN)

Bias-Variance Tradeoff

Bias: Model makes strong assumptions, leading to underfitting.
Variance: Model is too complex and captures noise, leading to overfitting.
Goal: Find a balance between the two.

Evaluation Metrics for Supervised Learning

Classification:
- Accuracy = Correct Predictions / Total Predictions
- Precision = TP / (TP + FP) → How many selected items are relevant?
- Recall = TP / (TP + FN) → How many relevant items are selected?
- F1-Score = 2 × (Precision × Recall) / (Precision + Recall)
- ROC-AUC (Receiver Operating Characteristic - Area Under Curve): Measures the performance across thresholds.
Regression:
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- R² Score (Coefficient of Determination)

2. Technical Stuff - Setting Up Environment

Make sure you have the required libraries installed.

Install Python & Essential Libraries

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

Start Jupyter Notebook

jupyter notebook

Once the Jupyter Notebook opens, you can start coding in interactive cells.

3. Hands-on Project: Titanic Dataset EDA

Dataset: Titanic Survival Prediction

Dataset Source: Kaggle Titanic Dataset
Goal: Perform Exploratory Data Analysis (EDA) to understand the data.
Tasks:
1. Load the dataset.
2. Explore missing values.
3. Visualize distributions.
4. Find correlations.

Code Template: Titanic EDA

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

titanic = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv")
titanic.head()

Step 1: Check Missing Values

titanic.isnull().sum()

Step 2: Handle Missing Values

titanic['Age'].fillna(titanic['Age'].median(), inplace=True)
titanic['Embarked'].fillna(titanic['Embarked'].mode()[0], inplace=True)
titanic.drop(columns=['Cabin'], inplace=True)

Step 3: Data Distribution

sns.histplot(titanic['Age'], bins=30, kde=True)
plt.title('Age Distribution')
plt.show()

Step 4: Correlation Heatmap

plt.figure(figsize=(8,6))
sns.heatmap(titanic.corr(), annot=True, cmap="coolwarm")
plt.title('Feature Correlation')
plt.show()

Step 5: Survival Rate by Gender

sns.countplot(x='Survived', hue='Sex', data=titanic)
plt.title('Survival Rate by Gender')
plt.show()

Step 6: Conclusion from EDA

Missing values were handled.
Older people had a different survival rate than younger passengers.
Women had a higher survival rate than men.

Concepts to Learn for Other Days

To strengthen your foundation, make sure to learn:

Probability & Statistics: Important for understanding algorithms.
Linear Algebra: Needed for deep learning (e.g., matrices, vectors).
Gradient Descent: Core optimization technique in ML.
Overfitting & Regularization: L1/L2 regularization, dropout.
Feature Engineering: Handling categorical & numerical features effectively.

This foundation will help you understand supervised learning, deep learning, and advanced techniques in the coming days.