Mlearning.ai

FEBRUARY 15, 2023

Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same. In addition, each tree in the forest is made up of a random selection of the best attributes.

Cross Validation 52

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Towards AI

JULY 19, 2023

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

DrivenData Labs

MAY 24, 2023

Several additional approaches were attempted but deprioritized or entirely eliminated from the final workflow due to lack of positive impact on the validation MAE. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

Machine Learning 225

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Mlearning.ai

FEBRUARY 10, 2024

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability. Take the Naive Bayes algorithm, for example.

Algorithm 52

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Pickl AI

MAY 24, 2023

List of Python Libraries and Their Uses Given below are the Python Libraries that can be identified to be important working Python Libraries used by programmers in the industry: TensorFlow It is a computational library useful for writing new algorithms involving large number of tensor operations.

Data Science 52

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Pickl AI

NOVEMBER 2, 2023

These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats. Expertise in mathematics and statistical fields is essential for deciding algorithms, drawing conclusions, and making predictions. Let’s delve into the world of cheat sheets and understand their importance.

Data Scientist 52

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Pickl AI

FEBRUARY 4, 2024

Predictive Analytics: Leverage machine learning algorithms for accurate predictions. This makes Alteryx an indispensable tool for businesses aiming to glean insights and steer their decisions based on robust data. Predictive modeling Alteryx elevates predictive modeling with integrated machine learning algorithms and AutoML.

Data Preparation 52

Data Preparation Tableau Data Visualization SQL 52

Pickl AI

APRIL 26, 2024

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Then, I would explore forecasting models such as ARIMA, exponential smoothing, or machine learning algorithms like random forests or gradient boosting to predict future sales.

Data Analyst 52

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Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. Decision trees are more prone to overfitting.

Data Science 52

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. So buckle up!

Machine Learning 52

Machine Learning Machine Learning Decision Trees Data Science 52

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This one is a widely used ML algorithm that is mostly focused on capturing complex patterns within tabular datasets.

Machine Learning 52

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DagsHub

APRIL 21, 2024

Autonomous Vehicles: Automotive companies are using ML models for autonomous driving systems including object detection, path planning, and decision-making algorithms. MLOps ensures the reliability and safety of these models through rigorous testing, validation, and continuous monitoring in real-world driving conditions.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

JUNE 6, 2023

Experimentation: With a structured pipeline, it’s easier to track experiments and compare different models or algorithms. The preprocessing stage involves cleaning, transforming, and encoding the data, making it suitable for machine learning algorithms. Perform cross-validation using StratifiedKFold. Create the ML model.

ML 52

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Dataconomy

SEPTEMBER 1, 2023

The time has come for us to treat ML and AI algorithms as more than simple trends. Hybrid machine learning techniques can help with effective heart disease prediction by combining the strengths of different machine learning algorithms and utilizing them in a way that maximizes their predictive power.

Decision Trees 195

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PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. We use 500 trees, with a value of 0 and a maximum depth of each tree of 5.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

Mlearning.ai

APRIL 13, 2023

Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. When and where each kernel is used?

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Heartbeat

MAY 29, 2023

BERT model architecture; image from TDS Hyperparameter tuning Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning algorithm. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

Machine Learning 78

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Mlearning.ai

JULY 14, 2023

Random forests inherit the benefits of a decision tree model whilst improving upon the performance by reducing the variance. —  Jeremy Jordan Random Forest is a popular and powerful ensemble learning algorithm that combines multiple decision trees to generate accurate and stable predictions.

Machine Learning 52

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