Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. What is Feature Engineering?

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

MAY 17, 2023

However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. K-fold Cross Validation ML experts use cross-validation to resolve the issue. As a part of this course, you will learn in-depth about the concepts of Data science, Machine Learning and AI.

Machine Learning 52

Machine Learning Machine Learning Cross Validation ML 52

Pickl AI

JULY 26, 2023

Here are some examples of variance in machine learning: Overfitting in Decision Trees Decision trees can exhibit high variance if they are allowed to grow too deep, capturing noise and outliers in the training data. Regular cross-validation and model evaluation are essential to maintain this equilibrium.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Towards AI

JULY 19, 2023

Last Updated on July 19, 2023 by Editorial Team Author(s): Anirudh Chandra Originally published on Towards AI. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

Machine Learning 40

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

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

Machine Learning Machine Learning ML ML 52

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

Machine Learning Machine Learning Decision Trees Data Science 225

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

Data Science Python Machine Learning Machine Learning 52

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

Data Scientist Data Science Data Visualization Machine Learning 52

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

Data Analyst Data Analysis Data Analysis Machine Learning 52

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