The files in the repo itself are not directly related to the questions, just practicing most of ML topics in general.
This is not from the source it is constructed by me, I also put it in the repo as "CourseOverviewML" files to be clearer than the readme display.
By the way in first part which is web scrapping I did nice example here Jumia-Scrapping that I got the data from famous store and organize it to deal with it with SQL Server.
This README contains concise answers to fundamental questions across Machine Learning, Algorithms, Deep Learning, and Data Evaluation.
Types of Machine Learning?
The three main types are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Difference between AI, ML, and Deep Learning?
- AI (Artificial Intelligence): The broad concept of machines mimicking human intelligence.
- ML (Machine Learning): A subset of AI where algorithms learn patterns from data without explicit programming.
- Deep Learning: A subset of ML that uses multi-layered artificial neural networks to solve complex problems.
What is supervised learning?
Training a model on labeled data, meaning the input data is paired with the correct output/target.
What is unsupervised learning?
Training a model on unlabeled data, allowing the algorithm to find hidden patterns or groupings on its own.
What is reinforcement learning?
A type of ML where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards (trial and error).
What is overfitting?
When a model learns the training data too well, including its noise and outliers, resulting in poor performance on unseen/new data.
What is underfitting?
When a model is too simple to capture the underlying patterns in the data, performing poorly on both training and testing data.
What is bias and variance?
- Bias: Error introduced by approximating a real-world problem with a too-simple model (high bias leads to underfitting).
- Variance: Error introduced by a model's sensitivity to small fluctuations in the training set (high variance leads to overfitting).
What is train-test split?
Dividing a dataset into two subsets: one to train the model (training set) and one to evaluate its performance on unseen data (testing set).
What is Linear Regression?
A supervised algorithm used for predicting a continuous numerical output by fitting a straight line (or hyperplane) that minimizes the error between predicted and actual values.
What is Logistic Regression?
A supervised algorithm used for binary classification tasks, predicting the probability that an instance belongs to a specific class using a logistic curve.
Difference between classification and regression?
- Classification: Predicts discrete class labels (e.g., Spam or Not Spam).
- Regression: Predicts continuous numerical values (e.g., House prices).
What is Decision Tree?
A flowchart-like model that splits data into branches based on feature values, leading to a final decision/prediction at the leaves.
What is Random Forest?
An ensemble learning method that builds multiple decision trees and merges their outputs to get a more accurate and stable prediction.
What is KNN (K-Nearest Neighbors)?
An algorithm that classifies a new data point based on the majority class among its 'K' closest neighbors in the feature space.
What is SVM (Support Vector Machine)?
A classification algorithm that finds the optimal hyperplane which maximizes the margin (distance) between different classes.
What is Naive Bayes?
A probabilistic classifier based on Bayes' Theorem, assuming that all features are independent of each other given the class label (It is called naive for that).
What is K-Means clustering?
An unsupervised algorithm that partitions unlabeled data into 'K' distinct clusters based on feature similarity and distance to cluster centroids.
What is PCA (Principal Component Analysis)?
A dimensionality reduction technique that transforms a large set of features into a smaller one while retaining as much variance (information) as possible.
What is Deep Learning?
A specialized subset of machine learning based on artificial neural networks with multiple layers (deep networks) capable of learning complex representations from vast amounts of data.
What is a Neural Network?
A computational model inspired by the human brain, consisting of interconnected layers of artificial neurons (nodes) that process information.
What is gradient descent?
An optimization algorithm used to minimize the model's loss function by iteratively adjusting weights in the opposite direction of the gradient.
What is backpropagation?
The core mechanism in neural networks that calculates the gradient of the loss function backwards through the network layers to update the weights.
What are activation functions?
Mathematical equations attached to each neuron that introduce non-linearity into the network, allowing it to learn complex patterns.
Difference between ReLU and Sigmoid?
- Sigmoid: Maps outputs to a range between 0 and 1. Prone to the vanishing gradient problem.
- ReLU (Rectified Linear Unit): Outputs the input directly if positive, else outputs zero. It is faster to compute and helps mitigate the vanishing gradient problem.
What is CNN (Convolutional Neural Network)?
A type of deep neural network optimized for processing grid-like data, predominantly used for image recognition and computer vision tasks.
What is RNN (Recurrent Neural Network)?
A type of neural network designed for sequential data (like text or time series) because it has internal memory loops to remember previous inputs.
What is transfer learning?
A technique where a model developed for one task is reused as the starting point for a model on a second, related task, saving computational resources and training time.
What is dropout?
A regularization technique in neural networks where randomly selected neurons are ignored ("dropped out") during training to prevent overfitting.
What is data preprocessing?
The process of cleaning, transforming, and organizing raw data into a format that is understandable and usable by machine learning algorithms.
What is feature engineering?
The process of using domain knowledge to select, modify, or create new variables (features) from raw data to improve model performance.
How do you handle missing values?
Common methods include removing the missing rows/columns, or imputing them with the mean, median, mode, or using advanced predictive imputation techniques.
What is normalization?
Scaling numerical data so that all values fall within a specific range, typically between 0 and 1 (Min-Max scaling).
What is standardization?
Scaling numerical data so that it has a mean of 0 and a standard deviation of 1 (Z-score scaling), handling outliers better than normalization.
What is accuracy?
An evaluation metric representing the ratio of correctly predicted observations to the total observations.
What is precision?
The ratio of correctly predicted positive observations to the total predicted positives. It answers: "Of all instances predicted as positive, how many were actually positive?"
What is recall?
The ratio of correctly predicted positive observations to all actual positives. It answers: "Of all actual positive instances, how many did we correctly identify?"
What is F1-score?
The harmonic mean of Precision and Recall. It is especially useful for imbalanced datasets where you need a balance between precision and recall.
What is confusion matrix?
A table used to describe the performance of a classification model, displaying True Positives, True Negatives, False Positives, and False Negatives.
For each topic in the labs, you can quickly take a look at the background math from this book (NOT from a to z):
Note that you need to download the labs files to be manage to run it (on VS Code as example).
Now that you have grasped these concepts, you are ready to apply them to a large-scale project. To see how these principles are integrated into a real-world machine learning application, explore the Enterprise RAG System.