Machine Learning: Transforming Data into Insights

This presentation, titled "Machine Learning: Transforming Data into Insights," offers an in-depth exploration of Machine Learning (ML), highlighting its critical role in modern technology and various industries. The presentation begins with a thorough introduction to ML, distinguishing it from traditional programming and emphasizing its importance in today's data-driven world. It then categorizes ML into three main types: Supervised, Unsupervised, and Reinforcement Learning, providing examples and use cases for each.

The ML workflow is meticulously detailed, covering every stage from data collection and preparation to model training, evaluation, and deployment. Emphasis is placed on the importance of high-quality data, effective data preprocessing techniques, and the selection of appropriate algorithms. The presentation also explores common challenges in ML, such as overfitting, underfitting, data privacy concerns, and the interpretability of complex models.
By providing a holistic view of ML, including its practical applications, technical workflow, challenges, and ethical implications, this presentation aims to educate and inspire the audience, highlighting the profound impact of ML on data analysis and decision-making processes in various fields.

1. Machine Learning 7/12/2024 Document reference 1

2. Machine Learning (ML) ML is a branch of artificial intelligence: 1. Learning from Data: Machine Learning involves creating algorithms that can learn patterns and make decisions based on data. 2. Automation of Tasks: ML enables automation by allowing systems to learn from data and perform tasks without explicit programming. 3. Adaptability and Improvement: One of the key features of ML is its ability to adapt and improve over time. As more data becomes available, the algorithms can continuously learn and refine their models, leading to improved performance and accuracy in making predictions or decisions. 2

3. ML in real-life 3

4. Types of machine learning models 1. Supervised Learning This involves training a model on a labeled dataset , where the algorithm learns the mapping between input features and corresponding output labels. The trained model can then make predictions on new, unseen data based on the learned patterns. Example: Linear Regression, Logistic regression, Decision Tree etc. 4

5. Types of machine learning models 2. Unsupervised Learning There are not predefined and known set of outcomes / unlabeled data. Look for hidden patterns and relations in the data A typical example: Clustering 5

6. Types of machine learning models 3. Reinforcement Learning In this, an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or punishments based on the actions it takes. The goal is for the agent to learn a policy , a strategy that maps observations to actions , in order to maximize the cumulative reward over time. Reinforcement learning is often used in scenarios like game playing , robotic control , and autonomous systems. 6

7. Types of Supervised Learning Classification Predicts which class a given sample of data (sample of descriptive features) is part of ( discrete value ). Regression Predicts continuous values. Predicting temperature. 7

8. Test and Train Dataset Train/Test is a method to measure the accuracy of your model . You  train  the model using the training set. You  test  the model using the testing set. 8

9. Techniques for dataset splitting: 1. Random Splitting 2. Stratified Splitting 3. Time-Based Splitting 4. K-Fold Cross-Validation 5. Leave-One-Out Cross-Validation (LOOCV) 6. Shuffle-Split Cross-Validation 7. Group-Based Splitting 9

10. Machine Learning as a Process 10 - Define measurable and quantifiable goals - Use this stage to learn about the problem - Normalization - Transformation - Missing Values - Outliers - Data Splitting Features Engineering Estimating Performance Evaluation and Model Selection Study models accuracy - Work better than the naïve approach or previous system Do the results make sense in the context of the problem It involves integrating the model into the operational system, ensuring scalability, and maintaining its performance over time.

11. Data Preparation 11 Needed for several reasons Some Models have strict data requirements Scale of the data, data point intervals, etc. Some characteristics of the data may impact dramatically on the model performance Time on data preparation should not be underestimated

12. Feature engineering 12 Determine the predictors (features) to be used is one of the most critical questions Feature engineering is the process of selecting, transforming, or creating features (input variables) in a dataset to improve the performance of a machine learning model. Types of Feature Selection Methods: Filter Methods Wrapper Methods Embedded Methods Hybrid Methods

13. Model Building Data Splitting Allocate data to different tasks model training performance evaluation Define Training, Validation and Test sets Feature Selection (Review the decision made previously) Estimating Performance Visualization of results – discovery interesting areas of the problem space Statistics and performance measures Evaluation and Model selection The ‘no free lunch’ theorem no a priory assumptions can be made Avoid use of favorite models if NEEDED 13

14. 1. Linear Regression 14 Linear regression uses the relationship between the data-points to draw a straight line through all them.

15. 2. Logistic Regression This type of statistical model (also known as  logit model ) is often used for classification and predictive analytics. Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given dataset of independent variables. Since the outcome is a probability, the dependent variable is bounded between 0 and 1.  15

16. 3. SVM 16 1. SVM works by finding the hyperplane that best separates different classes in the feature space , maximizing the margin between them. 2. The key idea behind SVM is to identify support vectors, which are the data points closest to the decision boundary, contributing to the determination of the optimal hyperplane. 3. SVM is widely used in various fields such as image classification, text categorization, and bioinformatics, owing to its ability to handle complex datasets and produce robust predictions.

17. 4. Decision Tree Document reference 17 1. The tree is built recursively by selecting the most informative features at each node, aiming to maximize the information gain or decrease impurity, resulting in a hierarchical set of decision rules. 2. can handle both numerical and categorical data. 3. Prone to overfitting. 4. robustness in complex datasets.

18. 5. K – nearest mean 18 In K Nearest Neighbors, the prediction for a new data point is based on the majority class or mean of the k-nearest data points in the feature space. The algorithm relies on the assumption that similar instances in the input space have similar output values

19. Other ML models Naive Bayes algorithm KNN algorithm Random forest algorithm Dimensionality reduction algorithms Gradient boosting algorithm and Ada-Boosting algorithm Neural Network 19

20. Conclusion: Journey into Machine Learning - Understanding the Landscape: Explored the vast landscape of machine learning, from supervised and unsupervised learning to reinforcement learning, gaining insights into its diverse applications. - Crucial Concepts: Grasped foundational concepts such as training, testing, feature engineering, and model deployment, laying the groundwork for effective implementation. - Real-world Impact: Witnessed the transformative power of machine learning through real-world examples, from personalized recommendations to predictive analytics in various industries. - Empowered to Apply: Equipped with the knowledge and tools to apply machine learning in problem-solving, decision-making, and innovation across diverse domains. 20

21. THANK YOU 7/12/2024 21