Machine Learning & Artificial Intelligence | Data Science Free Courses

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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science. Basically, there are 3 different layers in a neural network : Input Layer (All the inputs are fed in the model through this layer) Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers) Output Layer (The data after processing is made available at the output layer) Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

If you want to become an expert data scientist, practice these 5 habits on your learning journey 1) Spend at least 30 minutes a day reading a tech book or a blog 2) Learning Python is great, but writing effective SQL queries makes you stand out. 3) Practice storytelling daily If you can't explain your insights, do you really understand them? 4) Test your knowledge and understanding of concepts by building personal projects. 5) Refine problem-solving abilities through case studies. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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AI Engineer Deep Learning: Neural networks, CNNs, RNNs, transformers. Programming: Python, TensorFlow, PyTorch, Keras. NLP: NLTK, SpaCy, Hugging Face. Computer Vision: OpenCV techniques. Reinforcement Learning: RL algorithms and applications. LLMs and Transformers: Advanced language models. LangChain and RAG: Retrieval-augmented generation techniques. Vector Databases: Managing embeddings and vectors. AI Ethics: Ethical considerations and bias in AI. R&D: Implementing AI research papers.

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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. 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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Basics of Machine Learning 👇👇 Free Resources to learn Machine Learning: https://t.me/free4unow_backup/587 Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types: 1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location. 2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing. 3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications. Key concepts include: - Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training. - Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance. - Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns. - Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks. In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task. Join @datasciencefun for more ENJOY LEARNING 👍👍

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🔹 Supervised Learning - Key Algorithms 🔹 1️⃣ Linear Regression – Predicts continuous values by fitting a straight line. (📈 House prices) 2️⃣ Logistic Regression – Classifies data into categories (yes/no). (📩 Spam detection) 3️⃣ SVM (Support Vector Machine) – Finds the best boundary to separate classes. (🚀 Image classification) 4️⃣ Decision Tree – Splits data based on conditions to classify. (🌳 Diagnosing diseases) 5️⃣ Random Forest – Multiple decision trees combined for accuracy. (🏦 Loan predictions) 6️⃣ k-NN (k-Nearest Neighbors) – Classifies based on the nearest neighbors. (🛒 Product recommendations) 7️⃣ Naive Bayes – Uses probability to classify data. (📨 Spam filter) 8️⃣ Gradient Boosting – Combines weak models to build a strong one. (📊 Customer churn prediction) 9️⃣ XGBoost – Faster and more efficient gradient boosting. (🏆 Machine learning competitions) ✨ Key Tip: Choose algorithms based on data type (classification/regression) 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 😊

Libraries for Data Science in Python