Data Science & Machine Learning

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What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Supervised vs. Unsupervised Learning - Overfitting and Underfitting - Cross-validation - Bias-Variance Tradeoff - Accuracy vs Interpretability - Accuracy vs Latency 𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 - Logistic Regression - Decision Trees - Random Forest - Support Vector Machines - K-Nearest Neighbors - Naive Bayes - Linear Regression - Ridge and Lasso Regression - K-Means Clustering - Hierarchical Clustering - PCA 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀 - EDA - Data Cleaning (e.g. missing value imputation) - Data Preprocessing (e.g. scaling) - Feature Engineering (e.g. aggregation) - Feature Selection (e.g. variable importance) - Model Training (e.g. gradient descent) - Model Evaluation (e.g. AUC vs Accuracy) - Model Productionization 𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 - Grid Search - Random Search - Bayesian Optimization 𝗠𝗟 𝗖𝗮𝘀𝗲𝘀 - [Capital One] Detect credit card fraudsters - [Amazon] Forecast monthly sales - [Airbnb] Estimate lifetime value of a guest I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Many data scientists don't know how to push ML models to production. Here's the recipe 👇 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time 🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model 🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github 🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API 🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker 🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs 🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling. 𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility. 𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment 𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker. 𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

A-Z of essential data science concepts A: Algorithm - A set of rules or instructions for solving a problem or completing a task. B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently. C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics. D: Data Mining - The process of discovering patterns and extracting useful information from large datasets. E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance. F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance. G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively. H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data. I: Imputation - The process of replacing missing values in a dataset with estimated values. J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously. K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups. L: Logistic Regression - A statistical model used for binary classification tasks. M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time. N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks. O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points. P: Precision and Recall - Evaluation metrics used to assess the performance of classification models. Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data. R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables. S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks. T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations. U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes. V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets. W: Weka - A popular open-source software tool used for data mining and machine learning tasks. X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks. Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters. Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data. Data Science Interview Resources 👇👇 https://topmate.io/analyst/1024129 Like for more 😄

Top three most required tech stack for the following roles: 1. Data Analyst: SQL, Excel, Tableau/Power BI 2. Data Scientist: Python, R, SQL 3. Quantitative Analyst: Python, R, MATLAB 4. Business Analyst: SQL, Business Requirements Gathering, Agile Methodologies, Power BI/Tableau 5. Data Engineer: Python/Scala, SQL, Cloud, Apache Spark 6. Machine Learning Engineer: Python, TensorFlow/PyTorch, Docker/Kubernetes.

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Essential Python Libraries to build your career in Data Science 📊👇 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Seaborn: - Statistical data visualization built on top of Matplotlib. 5. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 6. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 7. PyTorch: - Deep learning library, particularly popular for neural network research. 8. SciPy: - Library for scientific and technical computing. 9. Statsmodels: - Statistical modeling and econometrics in Python. 10. NLTK (Natural Language Toolkit): - Tools for working with human language data (text). 11. Gensim: - Topic modeling and document similarity analysis. 12. Keras: - High-level neural networks API, running on top of TensorFlow. 13. Plotly: - Interactive graphing library for making interactive plots. 14. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. 15. OpenCV: - Library for computer vision tasks. As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch. Free Notes & Books to learn Data Science: https://t.me/datasciencefree Python Project Ideas: https://t.me/dsabooks/85 Best Resources to learn Python & Data Science 👇👇 Python Tutorial Data Science Course by Kaggle Machine Learning Course by Google Best Data Science & Machine Learning Resources Interview Process for Data Science Role at Amazon Python Interview Resources Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING👍👍

Data Science Learning Plan Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra) Step 2: Python for Data Science (Basics and Libraries) Step 3: Data Manipulation and Analysis (Pandas, NumPy) Step 4: Data Visualization (Matplotlib, Seaborn, Plotly) Step 5: Databases and SQL for Data Retrieval Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning) Step 7: Data Cleaning and Preprocessing Step 8: Feature Engineering and Selection Step 9: Model Evaluation and Tuning Step 10: Deep Learning (Neural Networks, TensorFlow, Keras) Step 11: Working with Big Data (Hadoop, Spark) Step 12: Building Data Science Projects and Portfolio Data Science Interview Resources 👇👇 https://topmate.io/analyst/1024129 Like for more 😄

Resume key words for data scientist role explained in points: 1. Data Analysis: - Proficient in extracting, cleaning, and analyzing data to derive insights. - Skilled in using statistical methods and machine learning algorithms for data analysis. - Experience with tools such as Python, R, or SQL for data manipulation and analysis. 2. Machine Learning: - Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks. - Experience in model development, evaluation, and deployment. - Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models. 3. Data Visualization: - Ability to present complex data in a clear and understandable manner through visualizations. - Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts. - Understanding of best practices in data visualization for effective communication of findings. 4. Big Data: - Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink. - Knowledge of distributed computing principles and tools for processing and analyzing big data. - Ability to optimize algorithms and processes for scalability and performance. 5. Problem-Solving: - Strong analytical and problem-solving skills to tackle complex data-related challenges. - Ability to formulate hypotheses, design experiments, and iterate on solutions. - Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making. Resume key words for a data analyst role 1. SQL (Structured Query Language): - SQL is a programming language used for managing and querying relational databases. - Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role. 2. Python/R: - Python and R are popular programming languages used for data analysis and statistical computing. - Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning. 3. Data Visualization: - Data visualization involves presenting data in graphical or visual formats to communicate insights effectively. - Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends. 4. Statistical Analysis: - Statistical analysis involves applying statistical methods to analyze and interpret data. - Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making. 5. Data-driven Decision Making: - Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings. - Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations. Data Science Interview Resources 👇👇 https://topmate.io/analyst/1024129 Like for more 😄

Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Top Platforms for Building Data Science Portfolio Build an irresistible portfolio that hooks recruiters with these free platforms. Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job. 1. GitHub 2. Kaggle 3. LinkedIn 4. Medium 5. MachineHack 6. DagsHub 7. HuggingFace #datascienceprojects

Programming languages are the backbone of data science. Such languages allow professionals to automate some work, analyze the most complex datasets, and thus provide insights that lead to strategic business decisions. With so many choices available, the decision on which language to learn seems like an extremely daunting task. This article tries to demystify that decision by giving readers the best programming languages for data science and why these count. Read more.....

10 commonly asked data science interview questions along with their answers 1️⃣ What is the difference between supervised and unsupervised learning? Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data. 2️⃣ Explain the bias-variance tradeoff in machine learning. The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance. 3️⃣ What is the Central Limit Theorem and why is it important in statistics? The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes. 4️⃣ Describe the process of feature selection and why it is important in machine learning. Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy. 5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them? Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data. 6️⃣ What is regularization and why is it used in machine learning? Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features. 7️⃣ How do you handle missing data in a dataset? Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly. 8️⃣ What is the difference between classification and regression in machine learning? Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome. 9️⃣ Explain the concept of cross-validation and why it is used. Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting. 🔟 What evaluation metrics would you use to evaluate a binary classification model? Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem. Best Data Science & Machine Learning Resources👇 https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊

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5 EDA Frameworks for Statistical Analysis every Data Scientist must know 🧵⬇️ 1️⃣ Understand the Data Types and Structure: Start by inspecting the data’s structure and types (e.g., categorical, numerical, datetime). Use commands like .info() or .describe() in Python to get a summary. This step helps in identifying how different columns should be handled and which statistical methods to apply. Check for correct data types Identify categorical vs. numerical variables Understand the shape (dimensions) of the dataset 2️⃣ Handle Missing Data: Missing values can skew analysis and lead to incorrect conclusions. It’s essential to decide how to deal with them—whether to remove, impute, or flag missing data. Identify missing values with .isnull().sum() Decide to drop, fill (imputation), or flag missing data based on context Consider imputing with mean, median, mode, or more advanced techniques like KNN imputation 3️⃣ Summary Statistics and Distribution Analysis: Calculate basic descriptive statistics like mean, median, mode, variance, and standard deviation to understand the central tendency and variability. For distributions, use histograms or boxplots to visualize data spread and detect potential outliers. Summary statistics with .describe() (mean, std, min/max) Visualize distributions with histograms, boxplots, or violin plots Look for skewness, kurtosis, and outliers in data 4️⃣ Visualizing Relationships and Correlations: Use scatter plots, heatmaps, and pair plots to identify relationships between variables. Look for trends, clusters, and correlations (positive or negative) that might reveal patterns in the data. Scatter plots for variable relationships. Correlation matrices and heatmaps to see correlations between numerical variables. Pair plots for visualizing interactions between multiple variables. 5️⃣ Feature Engineering and Transformation: Enhance your dataset by creating new features or transforming existing ones to better capture the patterns in the data. This can include handling categorical variables (e.g., one-hot encoding), creating interaction terms, or normalizing/scaling numerical features. Create new features based on domain knowledge. One-hot encode categorical variables for modeling. Normalize or standardize numerical variables for models that require scaling (e.g., KNN, SVM) Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Like if you need similar content 😄👍 Hope this helps you 😊 #datascience

Data science interview questions 👇 𝗦𝗤𝗟 - How do you write a query to fetch the top 5 highest salaries in each department? - What’s the difference between the HAVING and WHERE clauses in SQL? - How do you handle NULL values in SQL, and how do they affect aggregate functions? 𝗣𝘆𝘁𝗵𝗼𝗻 - How do you handle large datasets in Python, and which libraries would you use for performance? - What are context managers in Python, and how do they help with resource management? - How do you manage and log errors in Python-based ETL pipelines? 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - Explain the difference between bias and variance in a machine learning model. How do you balance them? - What is cross-validation, and how does it improve the performance of machine learning models? - How do you deal with class imbalance in classification tasks, and what techniques would you apply? 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - What is the vanishing gradient problem in deep learning, and how can it be mitigated? - Explain how a convolutional neural network (CNN) works and when you would use it. - What is dropout in neural networks, and how does it help prevent overfitting? 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 - How would you handle outliers in a dataset, and when is it appropriate to remove or keep them? - Explain how to merge two datasets in Python, and how would you handle duplicate or missing entries in the merged data? - What is data normalization, and when should you apply it to your dataset? 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 - How do you create a dual-axis chart in Tableau, and when would you use it? - How would you filter data in Tableau to create a dynamic dashboard that updates based on user input? - What are calculated fields in Tableau, and how would you use them to create a custom metric? #datascience #interview

Becoming a data scientist is not scary 1. Making the leap is harder than the work itself – Overcoming the initial fear of freelancing was more challenging than the actual projects. 2. Specialization matters more than general knowledge – Having a broad skillset is good, but focusing on a niche brings more opportunities. 3.Clients are diverse – Their expectations, work standards, and communication styles vary greatly, so adaptability is key. 4. Learning never stops – You will have to continuously learn and Upskill yourself to grow 5. Big data makes a big difference – The more complex the data, the more valuable my skills become. 6. Your network is your lifeline – Building connections is critical for finding opportunities and advancing. 7. Keep visualizations simple – Clear, straightforward visuals communicate insights more effectively than complicated ones. I know that starting your career in data can be terrifying. But the more you think and brainstorm, the harder it gets. You’ll postpone it more, blame AI for your lack of enthusiasm and initiative. And at the end of the day, when the last train leaves, you’ll hate on yourself even more for not clenching your teeth and going all in! Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Like if you need similar content 😄👍 Hope this helps you 😊 #datascience

ML Interview Question ⬇️ ➡️ Logistic Regression The interviewer asked to explain Logistic Regression along with its: 🔷 Cost function 🔷 Assumptions 🔷 Evaluation metrics Here is the step by step approach to answer: ☑️ Cost function: Point out how logistic regression uses log loss for classification. ☑️ Assumptions: Explain LR assumes features are independent and they have a linear link. ☑️ Evaluation metrics: Discuss accuracy, precision, and F1-score to measure performance. Knowing every concept is important but more than that, it is important to convey our knowledge💯 I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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