# machine learning tutorial for beginners

### Summary

In today's session, we discussed various aspects of machine learning and related concepts, including AI, ML, DL, data science, supervised ML, unsupervised ML, regression, classification, clustering, and dimensionality reduction.

### Facts

• 🤖 AI (Artificial Intelligence) involves creating applications that can perform tasks without human intervention.

• 🧠 ML (Machine Learning) is a subset of AI that uses statistical tools for data analysis, visualization, and prediction.

• 🌐 DL (Deep Learning) is a subset of ML that aims to mimic human brain functions using multi-layered neural networks.

• 📊 Data science encompasses various roles and involves working on AI and ML projects to create AI applications.

• In supervised ML, there are independent features and a dependent feature, which can be used for regression or classification tasks.

• Regression predicts continuous values, while classification categorizes data into classes.

• Unsupervised ML includes clustering to group similar data points and dimensionality reduction to reduce the number of features.

• Linear regression, ridge, lasso regression, logistic regression, and decision trees are some of the algorithms covered in the session.- ⚙️ Covered machine learning algorithms:

• Random forest
• XGBoost
• A bias
• K-means
• DB scan
• Hierarchical clustering
• K-nearest neighbor clustering
• PCA
• LDA
• SVM
• KNN
• 📈 Linear regression:

• Linear regression involves creating a best-fit line to predict one variable based on another.
• Parameters in linear regression:
• Intercept (Theta 0): Y-axis intercept of the line.
• Slope (Theta 1): Indicates the unit change in Y for a unit change in X.
• Objective: Minimize the cost function, which is the squared error function.
• Cost function (J):
• J(theta 0, theta 1) = (1/2m) * Σ(i=1 to m) [h(theta(xi)) - yi]^2
• Squared error function is used to measure the accuracy of the model.
• Derivation involves adjusting parameters (theta 0 and theta 1) to minimize the cost function.
• 🧮 Example with theta 0 = 0:

• If theta 0 is set to 0, the line passes through the origin.
• The slope (theta 1) determines the line's angle and how well it fits the data points.
• A smaller theta 1 would result in a steeper line, while theta 1 = 1 makes it pass through all data points.🧮 Bulletpoints:
• The text discusses the computation of a cost function in a specific scenario involving mathematical calculations.

• It explores different values of theta 1 (0, 0.5, 1) and their impact on the cost function.

• It introduces the concept of gradient descent and its role in finding the optimal theta values.

• The learning rate (alpha) is discussed, emphasizing the importance of choosing an appropriate value.

• The text briefly mentions the possibility of encountering local minima in deep learning but notes that it's not a concern in linear regression.

• The gradient descent algorithm is outlined, emphasizing the goal of convergence to minimize the cost function.- 📈 Derivative Calculation: Explained the process of finding derivatives for a function.

• 🧐 Exploring Derivatives: Considered a specific example of derivative calculation.

• 📊 Derivative with Respect to Theta 0: Derived the derivative with respect to theta 0.

• 🔄 Repeat Until Convergence: Discussed the convergence algorithm and updates for theta 0 and theta 1.

• 📉 Performance Metrics: Introduced and explained R-squared and adjusted R-squared as performance metrics.

• 🔄 R-squared Impact of Features: Discussed how adding features can impact R-squared.

• 🔄 Adjusted R-squared: Explained how adjusted R-squared compensates for the number of features.

• 🔀 Transition to New Topics: Mentioned upcoming topics to be discussed, including ridge and lasso regression, assumptions of linear regression, logistic regression, confusion matrix, and practical applications.- 🔍 The main aim is to minimize the cost function.

• 📊 Overfitting occurs when the model performs well with training data but poorly with test data.

• 📉 Overfitting leads to low bias and high variance.

• 📊 Underfitting occurs when the model performs poorly with both training and test data.

• 📉 Underfitting leads to high bias and high variance.

• 🧱 Ridge regression (L2 regularization) helps prevent overfitting by adding a regularization term.

• 📏 Lambda in ridge regression controls the steepness of the slope and prevents overfitting.

• 🧩 Lasso regression (L1 regularization) helps with feature selection by shrinking unimportant feature coefficients to zero.- 📊 Ridge regression (L2 regularization) importance:

• Prevents overfitting.
• Controls the contribution of features with lambda.
• 📊 Lasso regression (L1 regularization) importance:

• Prevents overfitting.
• Performs feature selection by setting unimportant feature coefficients to nearly zero.
• 📊 Cross-validation:

• Helps find the optimal lambda hyperparameter.
• Involves trying different lambda values.
• 📊 Linear regression assumptions:

• Features should follow a normal or Gaussian distribution.
• Standardization (scaling) of data is beneficial.
• Assumes linearity in data; works well for linearly separable data.
• Checks for multicollinearity (correlation between features).
• 📊 Logistic regression:

• Used for binary classification.
• Utilizes the sigmoid activation function to squash the linear output.
• The sigmoid function: 1 / (1 + e^(-z)), where z is the linear combination of features and coefficients.
• Prevents outliers from significantly affecting the decision boundary.
• 📊 Decision boundary:

• Created by applying the sigmoid function to the linear regression output.
• Sigmoid function: 1 / (1 + e^(-z)), where z is the linear combination of features and coefficients.- 📊 The sigmoid or logistic function is discussed for binary classification.
• 📈 The sigmoid function is represented graphically with values ranging between 0 and 1.

• 🧐 Assumptions are made that g(z) is greater than or equal to 0.5 when z is greater than or equal to 0.

• 🤖 Logistic regression helps overcome outlier issues by squashing the best-fit line.

• 📚 Training set and hypothesis function are introduced for logistic regression.

• 🧮 The logistic regression cost function is presented as a solution to non-convexity issues.

• 📉 The cost function includes a log term and addresses scenarios for y=0 and y=1.

• 🔀 The confusion matrix is explained for evaluating binary classification model performance.📊 Data Loading:

• Loaded the Boston house pricing dataset using scikit-learn.

📚 Libraries Import:

• Imported necessary libraries: NumPy, pandas, seaborn, matplotlib.pyplot.

🔢 Data Exploration:

• Examined the type of the loaded dataset, which is of type 'sklearn.utils.Bunch'.
• Reviewed the dataset contents, including data, target values, and feature names.

Now, let's continue with the analysis.- 🔍 The speaker is discussing data manipulation and analysis using Python's pandas library.

• 🧾 They are creating a data frame using the `pd.DataFrame` function.

• 📊 They mention working with a dataset that has multiple features (feature one, feature two, feature three, etc.).

• 🏠 The dataset appears to be related to house pricing.

• 🎯 They talk about setting up independent and dependent features for linear regression.

• 🧱 They describe the process of splitting the data into training and testing sets.

• 🧮 They calculate mean squared error (MSE) as a metric for model evaluation.

• 🧐 They introduce ridge regression and mention the importance of hyperparameter tuning.

• 🧾 They define a range of alpha values for hyperparameter tuning.

• 📊 They discuss using grid search cross-validation (`GridSearchCV`) to find the best hyperparameters for ridge regression.- ⚙️ Execute the code for logistic regression with different C values: 1, 5, 10, 20.

• 📊 Check if the dataset is imbalanced (357 ones and 212 zeros).

• 📈 Perform train-test split with the data.

• 📝 Import logistic regression from sklearn.linear_model.

• 🧮 Use L1 and L2 norms for regularization.

• ⚖️ Consider class weight balancing for handling imbalanced datasets.

• 📚 Refer to theory videos for more details on logistic regression parameters.📊 Summary of Text:

• The text discusses various aspects of model training and evaluation.

• It mentions defining a set of values, specifically for grid search.

• It talks about logistic regression modeling with parameters.

• F1 scoring is mentioned as a performance metric.

• The process of model fitting and predictions is explained.

• Confusion matrix and classification report are discussed.

• Accuracy score calculation is mentioned.

• The text briefly touches on the Bayes theorem and its application in classification problems.

Now, let me know if you have any specific questions or need further details about any part of the text.- 🌳 Decision trees are used for solving classification and regression problems.

• 🌳 Classification involves dividing data into classes or categories.
• 🌳 Decision trees represent conditions as nodes and branches for decision-making.
• 🌳 Conditions in decision trees lead to either "yes" or "no" outcomes.
• 🌳 Decision trees can be constructed based on if-else conditions or data.
• 🌳 Decision trees are useful for visualizing and interpreting complex decision-making processes.
• 🌳 Decision trees can be prone to overfitting, especially in deep trees.
• 🌳 Decision trees can handle both numerical and categorical data.
• 🌳 Decision trees are used for tasks like predicting outcomes, such as playing a game or making a decision.📊 Key Points:
• The data set in question is important for research papers and algorithms.
• The problem statement involves a classification task, specifically predicting whether a person will play tennis based on weather conditions.
• Features include outlook, temperature, humidity, and wind.
• The decision tree algorithm is used to make predictions.
• The tree is built by selecting features and creating nodes based on their categories.
• Entropy is used to measure the impurity of a split, and a pure split has entropy equal to zero.
• The decision tree algorithm aims to maximize information gain when selecting features for splitting.

❓ If you have any specific questions or need further clarification on any topic, feel free to ask!- 🔍 The decision-making process: Should I take F1, F2, or F3 first, or any other feature first?

• 🧩 The role of Information Gain in feature selection.
• 📈 Calculating Information Gain with a specific formula.
• 📊 Comparing Guinea Impurity and Entropy for decision tree speed.
• 📉 Handling continuous features in decision trees.
• 🤖 Decision Tree Regressor: Predicting continuous outputs using Mean Squared Error.
• 🌲 The impact of unlimited depth in decision trees (overfitting).- 🌲 Bagging: Bagging involves creating multiple models (e.g., decision trees, logistic regression) trained on different subsets of data and then combining their predictions through majority voting in classification or taking the mean in regression.
• 🚀 Boosting: Boosting is a sequential ensemble technique where models are trained one after the other. Each subsequent model focuses on correcting the errors made by the previous ones.
• 📊 Random Forest: Random Forest is an example of a bagging technique that uses decision trees as base models and aggregates their outputs.
• 🚗 AdaBoost: AdaBoost is a boosting technique that sequentially trains models, assigning higher weights to data points that were misclassified by previous models.
• 🚀 XGBoost: XGBoost is another boosting algorithm known for its efficiency and accuracy in handling large datasets.

The provided text discusses ensemble techniques, bagging, boosting, and some specific algorithms related to these concepts.✨ Summary of Text ✨

• 📋 Sequential models are used one after another: M1, M2, M3, M4, leading to the final output.
• 🚀 Boosting involves combining weak learners (M1, M2, M3, M4) sequentially to create a strong learner.
• 🤝 Combining diverse expertise helps solve complex problems efficiently.
• 📊 Bagging techniques include Random Forest Classifier and Regression.
• 👥 Random Forest uses decision trees, addressing overfitting with aggregation.
• 📈 Random Forest doesn't require normalization.
• 📉 KNN requires standardization due to distance metrics.
• 🎯 AdaBoost starts with equal weights for all data points.
• 🌳 AdaBoost uses decision trees (stumps) as weak learners.
• 👶 Stumps are weak learners as they're simple one-level decision trees.
• ✅ Errors are calculated after training, and more weight is assigned to misclassified points.
• 🔄 AdaBoost iteratively refines the model, focusing on misclassified data.
• 🔄 Each model in AdaBoost aims to correct the errors of the previous models.
• 🔃 The process continues until a specified number of models or a desired accuracy level is reached.
• 💪 AdaBoost creates a strong learner by combining these weak learners.

(Note: Due to the length of the text, this summary provides an overview of the key points.)- 📊 K-means clustering is an unsupervised machine learning technique used to group data into clusters based on similarity.

• 🎯 The goal is to find centroids (representative points) for each cluster.

• 🧮 To determine the number of clusters (k), various values are tried and evaluated.

• ✨ The algorithm starts with an initial guess for k centroids.

• 📈 Data points are assigned to the nearest centroid, forming clusters.

• 🌟 Centroids are updated based on the mean of points in each cluster.

• 🔄 Iterations continue until centroids converge or a set number of iterations is reached.

• 🤔 Selecting the optimal k value can involve techniques like the elbow method.

• 🚀 K-means is often used as a preliminary step in machine learning for feature engineering or as a basis for further analysis.- 📊 K-Means Clustering:

• Initialize k centroids randomly 🏢.
• Calculate distances between data points and centroids using Euclidean distance 📏.
• Assign data points to the nearest centroids 📌.
• Recalculate centroids as the average of data points assigned to them 🧮.
• Repeat the assignment and recalculation steps until centroids no longer change.
• Determine the optimal k value using the elbow method 🤔.
• 🌲 Hierarchical Clustering:

• Find the two closest clusters and merge them into one 🔄.
• Repeat the merging process until all data points belong to a single cluster.
• Determine the number of clusters based on the longest vertical line in the dendrogram 📈.
• Hierarchical clustering typically takes more time compared to K-means clustering ⏳.📊 Summary:
• Hierarchical clustering and K-means clustering have different time requirements.

• For smaller datasets, hierarchical clustering is suitable.

• For larger datasets, K-means clustering is recommended despite longer execution time.

• Silhouette score is used to validate clustering models, with values ranging from -1 to +1.

• Silhouette score measures the proximity of data points within clusters and separation between clusters.

• DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an effective clustering algorithm.

• DBSCAN categorizes points as core points, border points, or noise points.

• Core points are points with at least a specified number of nearby points within a defined radius (epsilon).

• Border points are points within the epsilon radius of a core point but don't meet the core point criteria.

• Noise points are isolated points that don't belong to any cluster.

• DBSCAN is a robust clustering method that effectively identifies outliers and creates meaningful clusters compared to traditional methods like K-means.- 💻 Practical Focus:

• The speaker is shifting attention to the practical aspect of the session, likely involving code demonstrations.

• 🐍 Anaconda & Jupyter:

• Mention of using Anaconda and Jupyter Notebook for the practical problem.
• 📈 Clustering Approach:

• Explains the absence of underfitting or overfitting concerns in clustering and outlines the approach.
• 📚 Libraries & Tools:

• Lists the libraries and tools to be imported, including K-means clustering, silhouette scores, and DBSCAN.
• 📊 Data Generation:

• Describes the generation of sample data using the `make_blobs` function.
• 🔢 Choosing Cluster Count:

• Explains the process of determining the optimal number of clusters using the elbow method (WCSS).
• 📈 Silhouette Scoring:

• Demonstrates how to use silhouette scores to validate the clustering model.
• 🔍 Cluster Comparison:

• Compares different cluster counts and evaluates their suitability using silhouette scores.
• 📉 Negative Silhouette Scores:

• Highlights the importance of avoiding negative silhouette scores.
• 🧠 Understanding Bias & Variance:

• Provides definitions for bias and variance in the context of model performance.
• 🎯 Low vs. High Bias:

• Explains that high bias occurs when a model favors the training data, while low bias suggests poor training data performance.
• 🎯 Low vs. High Variance:

• Clarifies that low variance indicates consistent and accurate predictions, while high variance suggests inconsistent and inaccurate predictions.👩‍🏫 Summary:
• 📊 Training accuracy is better than test accuracy, indicating low bias/high variance.

• 🧠 Understanding the definitions of bias and variance is essential for data scientists.

• 📉 High bias and high variance are undesirable performance scenarios.

• 📈 Low bias and low variance are the ideal scenario for a generalized model.

• 🌳 XGBoost (Extreme Gradient Boosting) is used for classification and regression problems.

• 🌳 XGBoost constructs decision trees sequentially and starts with a base model.

• 🌳 Residuals are calculated based on the difference between predicted and actual values.

• 🤔 Residuals are squared and used to calculate similarity weights.

• 📏 Similarity weights are calculated for each node in the decision tree.

• 📚 Information gain is determined by subtracting the similarity weights.

• 📈 Information gain helps select features for further splitting in the tree.- 🌲 XGBoost Classifier:

• Split data into good, normal, and bad categories.
• Use binary splits based on conditions like "less than or equal to 50."
• Calculate residuals (e.g., -0.5) for data points.
• Compute similarity weight using residual squares and lambda.
• Calculate information gain for each split.
• Combine multiple decision trees based on information gain.
• Use learning rate and sigmoid activation for inference.
• 📈 XGBoost Regressor:

• Start with a base model using the average of output values.
• Calculate residuals (observed - predicted).
• Construct decision trees based on feature splits (e.g., experience).
• Compute similarity weight using residuals and lambda.
• Calculate information gain for splits.
• Sequentially add decision trees to improve predictions.📊 Summary:
• Data points are being divided into two categories using a decision tree.

• Calculation of similarity weights is performed based on squared differences.

• Decision tree construction involves alpha values for each node.

• The inference process involves applying weights and choosing the appropriate path in the decision tree.

• SVM (Support Vector Machine) aims to create a marginal plane for data separation.

• SVM seeks to maximize the distance between the marginal planes.

• Classification in SVM is based on whether the output of w transpose x plus b is greater than or equal to 1 or less than or equal to -1.

• The goal is to increase the margin between these two planes for better classification.

• 💡 The main objective is to ensure that y_i multiplied by (w^T * x_i + b) is always greater than or equal to 1 for correct points.

• 🎯 The goal is to minimize the cost function, which is equivalent to maximizing the magnitude of w while considering the optimization of w and b.

• 📊 Two additional parameters, c_i and η_i, are introduced. c_i represents the number of errors we can tolerate, while η_i is the summation of distances from the margin for misclassified points.

• 🛤️ Support Vector Regression (SVR) is briefly mentioned, where the only difference is a specific parameter.

• 🌀 SVM kernel is introduced for nonlinear data, where the data is transformed into higher dimensions to create a separating plane.

• 📺 Reference to a video explaining the practical implementation of SVM kernel for non-linear data.

• 👍 Overall, the session covers various aspects of SVM and SVM kernel for classification and regression tasks in machine learning.

Note: The text contains a mix of technical content, concepts, and instructions. If you need further details or clarification on specific points, please provide additional context or questions.