Odunayo Ojo’s Post

The workplace is becoming increasingly data-driven, including in defense and intelligence environments. All roles will require at least a basic level of data literacy. How is your data science knowledge? Don't become obsolete in the new world of work. Enroll in the FedLearn course, Basic Data Management (AIDATA108)—part of our artificial intelligence and data science catalog Gain a solid understanding of the data management process, how to prepare data for use and how to interpret outputs for more informed decision making Learn more and register today: https://lnkd.in/eWHtCASW #fedlearn #departmentofdefense #dod #intelligencecommunity #govcon #trainingprovider #traininganddevelopment #training #onlinelearning #onlinetraining #onlinecourses #asynchronous #artificialintelligence #ai #datamanagement #datascience #datasciencetraining

Ever felt overwhelmed by the jargon of Machine Learning? or like Machine Learning is some mystical realm only accessible to tech wizards? 🤔 Don't worry, you're not alone! Let's break it down together with a simple analogy: Imagine you're teaching a kid to differentiate between apples and oranges. 🍎🍊 You show them various fruits, highlighting the differences. Now, think of this process as "Supervised Learning" in Machine Learning. You provide labeled examples (apples and oranges) for the algorithm to learn from. But what if you want the algorithm to figure it out on its own, without labels? That's where "Unsupervised Learning" comes in. It's like asking the kid to sort a mixed bag of fruits without any prior knowledge. And just like in real life, sometimes things don't fit neatly into categories. That's when "Clustering" algorithms shine. They group similar items together, whether it's sorting fruits or segmenting customer data. Now, let's talk about "Overfitting." Ever tried too hard to fit into a pair of jeans that were too small? 🙈 That's what happens when a model learns the training data too well but struggles with new, unseen data. It's like those jeans—perfect for one situation but not so great for others. It's important to know that, Machine Learning isn't magic—it's a powerful tool rooted in logic and statistics. By understanding these key concepts, you'll unlock the potential to harness data in exciting new ways! 🌟 Keep exploring, keep learning! #MachineLearning #DataScience #DataAnalysis #Innovation #Technology #ArtificialIntelligience

🧩 Building a Machine Learning project is like solving a puzzle. Each step must seamlessly fit into the next to unlock insights from data. But the important thing is how those steps are lined up. In this post, I have explained how we should start a Machine Learning project as a beginner. From data preprocessing to model training and evaluation, I'll walk you through each stage, offering insights and best practices. 🔍 Using real-world data from the Titanic tragedy, let's explore how a logistic regression algorithm can predict passenger survival based on various features. Discover the power of this method in predicting outcomes and gain insights into interpreting the results effectively. Here's the roadmap I followed👇🏻 💡 Analyze Dataset - Identifying the dataset and its context sets the stage for understanding the problem. 🔍 Describe Dataset - Understanding the characteristics and structure of the dataset. 🗑️ Dropping Columns - Streamlining focus by removing irrelevant columns. ➕ Create Column - Engineering new features to enhance model performance. 🔍 Checking NULL Values - Ensuring data integrity by handling missing values. ✅ Filling Missing Values - Strategically imputing missing data to maintain dataset completeness. 🔍 Outlier Detection - Identifying and addressing outliers to prevent skewed results. 📊 Feature Scaling - Normalizing features to ensure uniformity in model training. 🔢 Data Encoding - Converting categorical data into numerical format for model compatibility. 🧮 Feature Selection - Choosing relevant features to optimize model performance. ⚖️ Data Balancing - Addressing class imbalances to prevent biased model predictions. 🔄 Pre-processed Dataset - Ensuring the dataset is ready for model training. ➖ Data Splitting - Dividing data into training, validation, and testing sets for evaluation. 🧠 Model Training - Utilizing machine learning algorithms to train predictive models. 🔄 Modification using K-Fold Cross-Validation for Fine-tuning model performance. 🔄 Modification Add Hyper Parameter Tuning for Enhancing model performance. 📈 Performance Evaluation - Assessing model accuracy, precision, recall, and F1-score to gauge effectiveness. For the source code👇🏻 https://lnkd.in/eWwMg5kJ #MachineLearning #DataScience #LogisticRegression #TitanicDataset #DataAnalysis #LinkedInLearning #AI #MLProject #DataAnalytics #PredictiveModeling #STEM #TechCommunity

🌟 Building Blocks of a Learning Algorithm in Machine Learning🌟 Every machine learning algorithm, regardless of its complexity, fundamentally consists of three core components. Understanding these building blocks is essential for grasping how learning algorithms operate and how they can be optimized for various tasks. 🔍 Key Building Blocks: 1. 📊 Loss Function:   - The loss function measures the discrepancy between the predicted values and the actual values. It quantifies how well the model is performing. Common examples include squared error loss for regression and cross-entropy loss for classification. 2. 🎯 Optimization Criterion:   - The optimization criterion is based on the loss function and defines the goal of the learning algorithm. It typically involves minimizing the loss function to improve model accuracy. This criterion can be seen as a cost function that the algorithm seeks to optimize. 3.💡 Optimization Routine:   - The optimization routine is the process by which the algorithm adjusts its parameters based on the training data to find a solution to the optimization criterion. Techniques like gradient descent are commonly used to iteratively update the model parameters to minimize the loss. 🌐 Practical Insights: Understanding these fundamental components empowers data scientists and machine learning practitioners to design, evaluate, and improve learning algorithms effectively. By focusing on the loss function, optimization criterion, and optimization routine, we can build robust models that deliver impactful solutions across various domains. 🔍 #MachineLearning #DataScience #LearningAlgorithms #LossFunction #Optimization

Data Science Week 10 with Digital Skola This week I learned about Unsupervised learning and Model Deployment. Unsupervised learning models don't need supervision while training data sets, making it an ideal ML technique for discovering patterns, groupings and differences in unstructured data. It's well-suited for processes such as customer segmentation, exploratory data analysis or image recognition. During model deployment, the trained models are integrated into the existing production systems or applications, allowing businesses to leverage the models' insights and predictions to drive data-driven decision-making and automate processes. These two fields play an important role in the industry's evolution in the understanding and application of artificial intelligence. By deepening our understanding of model deployment and unsupervised learning, we can bring significant innovation and added value to the data field. Let's continue to learn and collaborate to meet these challenges and push the boundaries of possibility in the world of machine learning. For more information I have attached the material. Let's see😊 #DigitalSkola #LearningProgressReview #DataScience

Top Machine Learning Algorithms: A Quick Guide for Enthusiasts and Practitioners. Machine Learning has revolutionized industries by enabling systems to learn from data and make predictions. Here’s a snapshot of some of the most commonly used algorithms and their applications: 🔹 K-Means Clustering: An unsupervised learning algorithm used for clustering similar data points into groups—widely applied in customer segmentation and pattern recognition. 🔹 Linear Regression: A fundamental supervised learning algorithm for predicting continuous outcomes based on input variables—common in forecasting and trend analysis. 🔹 Decision Trees: An interpretable algorithm for classification and regression tasks, ideal for decision-making systems in finance and healthcare. 🔹 Logistic Regression: A statistical method for binary classification problems, such as spam detection and disease diagnosis. 🔹 Support Vector Machines (SVM): A robust algorithm for classification tasks that works well for both linear and non-linear data. 🔹 Naive Bayes: A probabilistic classifier based on Bayes' theorem, widely used in text classification, sentiment analysis, and recommendation systems. 🔹 K-Nearest Neighbors (KNN): A simple yet effective algorithm for classification and regression by comparing data to its nearest neighbors. 🔹 Random Forest: A powerful ensemble method for classification and regression tasks that combines the predictions of multiple decision trees. 🔹 Dimensionality Reduction Algorithms: Techniques like PCA and t-SNE help simplify high-dimensional data for visualization and analysis. These algorithms are the backbone of many AI-driven solutions, each suited for specific types of problems. Understanding their strengths and limitations is crucial for designing impactful applications. 💡 Which algorithm is your favorite, and how have you applied it in your work? Share your thoughts below! #MachineLearning #DataScience #ArtificialIntelligence #Algorithms #Innovation #Career

Hello LinkedIn #datascience enthusiast Do you know Regression vs Classification in #Machinelearning Let's find out 👇 👉In machine learning, regression and classification are the two major methods 📝Classification 🔸️ It is a process in which data is divided into different categories or groups. It is used to identify different categories, such as “Male” or “Female”, “Spam” or “Not Spam” etc. For example, applications like Gmail use classification algorithms to automatically divide emails into inbox or spam folder. 📝Regression 🔹️This is another type of supervised learning, in which the output is modeled to predict continuous numerical values. For example, regression is used to predict house prices. Follow Shivam Raj for more such information . . . #datascientist #ML #Technology #learning

🌟 Boosting in Machine Learning: A Game-Changer for Predictive Models🌟 Have you ever wondered how machine learning algorithms achieve higher accuracy by combining weak learners? Let me introduce you to Boosting—a powerful ensemble learning technique! 🚀 What is Boosting? Boosting is an iterative approach that combines multiple weak models (typically decision trees) to create a strong learner. Each model focuses on the errors of its predecessor, improving performance with every iteration. One of the most popular boosting techniques is Gradient Boosting Regression. What is Gradient Boosting Regression? Gradient Boosting is a supervised learning algorithm designed for regression tasks. It builds models sequentially, minimizing errors using a gradient descent approach. Each new model corrects the residual errors from the previous ones. Why is Gradient Boosting Better? ✅ Accuracy: Gradient Boosting outshines many traditional methods by effectively reducing bias and variance. ✅ Flexibility: It supports various loss functions, making it adaptable to different problem types. ✅ Handles Non-linear Data: Its ability to capture complex patterns in data makes it a go-to choice for high-performance regression tasks. ✅ Customizable Learning Rate: Fine-tuning hyperparameters like learning rate and tree depth allows for optimal model performance. Use Cases Gradient Boosting is widely used in finance, healthcare, and e-commerce for predictions, fraud detection, and recommendation systems. 💡 Pro tip: Although Gradient Boosting delivers impressive results, it requires careful tuning to avoid overfitting and computational overhead. Let me know your thoughts or share your experiences with Gradient Boosting! Let's learn together! 💬 #MachineLearning #Boosting #GradientBoosting #DataScience #Regression

🌟 Understanding the Cold Start Problems in LLMs 🌟 It refers to the challenges that arise when you set out to fine-tune your LLM model on a specific task, based on a limited amount of labeled data. 🛠️ Absence of data can lead to the generation of suboptimal performances. 📉 🔑 Key Aspects of the Cold Start Problem in Fine-Tuning LLMs a. Lack of Training Data 📊 The new dataset should be representative of the new task or domain. Without sufficient labeled data, the model struggles to learn task-specific nuances, reducing accuracy and effectiveness. ❌🎯 b. Overfitting Risk ⚠️ When training from a small dataset, there is a high risk that the model memorizes the data instead of generalizing from it, leading to poor performance on unseen examples. 🤔➡️❌ c. Knowledge Gaps 📚 Even though trained on massive data, their knowledge may not extend to specific details or styles of the new domain, causing them to fail on out-of-box data samples. 🚪🔒 d. Domain Shift 🌐 If the new domain is significantly divergent from the model's pre-training data, the model may fail to adapt. 🔄❄️ 🛠️ Strategies to Mitigate Cold Start Issues Few-shot learning ✏️, prompt engineering 📝, data augmentation 🔄, transfer learning 🔗, and active learning 🧠 can help address the challenges arising from the cold start problem. 🚀✨ #AI #MachineLearning #LLMs #FineTuning #DataScience #ArtificialIntelligence #DeepLearning #PromptEngineering #TransferLearning #DataAugmentation #KnowledgeGaps #Overfitting #AITraining #ModelOptimization #AIChallenges

Hello Everyone, This week on Digital Skola we will learn about Introduction to machine learning dan data preprocessing 1. on Machine learning we learn about how algorithm works and we can make predictions and learn about the data pattern, model, generalization, supervised, unsupervised and reinforced learning. with machine learning we can forecast with data, fraud detection, recommendation something, and face detection. Meanwhile on data preprocessing we learn essential technique to clean dan prepare data for analysis, this step is very important to make sure the accuracy of the data and how machine learning wil work, on data preprocessing we handle missing value, duplicate data and, incosistent data. we learn about one-hot encoding and label encoding,and handle Imbalance data. Check out my learning progress review #DigitalSkola #LearningProgressReview #DataScience