Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Dataconomy

AUGUST 22, 2023

And lastly, integrating Bayesian techniques with deep learning, which has gained tremendous popularity, presents additional challenges. Combining the flexibility of deep learning architectures with Bayesian updating can be intricate and require specialized knowledge.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. Things to be learned: Ensemble Techniques such as Random Forest and Boosting Algorithms and you can also learn Time Series Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

APRIL 19, 2024

Through techniques like deep learning and reinforcement learning, AI systems simulate the ability to learn from experience and improve their performance over time, similar to how humans learn from trial and error. Statistics: Statistical methods are vital in AI, particularly in data analysis and machine learning.

Natural Language Processing 52

Natural Language Processing Machine Learning Machine Learning AI 52

Dataconomy

MAY 2, 2023

What do machine learning engineers do: ML engineers design and develop machine learning models The responsibilities of a machine learning engineer entail developing, training, and maintaining machine learning systems, as well as performing statistical analyses to refine test results.

ML 110

ML ML Machine Learning Machine Learning 110

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. It forms the basis for many statistical tests and estimators used in hypothesis testing and confidence interval estimation.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

MAY 29, 2024

This knowledge allows the design of experiments, hypothesis testing, and the derivation of conclusions from data. The curriculum includes subjects like linear algebra, calculus, probability, and statistics, essential for understanding Machine Learning and Deep Learning Models.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 21, 2023

Concepts like probability, hypothesis testing, and regression analysis empower you to extract meaningful insights and draw accurate conclusions from data. Step 3: Dive into Machine Learning and Deep Learning Master the realm of machine learning algorithms, from linear regression to neural networks.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

AI, particularly Machine Learning and Deep Learning uses these insights to develop intelligent models that can predict outcomes, automate processes, and adapt to new information. Deep Learning: Advanced neural networks drive Deep Learning , allowing AI to process vast amounts of data and recognise complex patterns.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Pickl AI

JUNE 19, 2024

Here are some of the most common backgrounds that prepare you well: Mathematics and Statistics These disciplines provide a rock-solid understanding of data analysis, probability theory, statistical modelling, and hypothesis testing – all essential tools for extracting meaning from data.

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

Pickl AI

AUGUST 9, 2024

Students should learn about data wrangling and the importance of data quality. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should learn about neural networks and their architecture.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

MAY 23, 2023

What is deep learning? What is the difference between deep learning and machine learning? Deep learning is a paradigm of machine learning. In deep learning, multiple layers of processing are involved in order to extract high features from the data. What is a computational graph?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc. Big Data Technologies: Hadoop, Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

NOVEMBER 27, 2024

Scikit-learn and TensorFlow dominate the Machine Learning landscape, providing easy-to-implement models for everything from simple regressions to deep learning. Statistical Analysis and Testing R’s rich ecosystem for hypothesis testing, regression modelling, and Bayesian analysis makes it ideal for statistical tasks.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Are there any areas in data analytics where you want to improve or learn more? I am keen on enhancing my skills in machine learning algorithms and deep learning techniques to tackle more complex data analysis projects and leverage advanced predictive modelling capabilities.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Data Science Dojo

AUGUST 30, 2023

Statistical analysis and hypothesis testing Statistical methods provide powerful tools for understanding data. Hypothesis testing, correlation, and regression analysis, and distribution analysis are some of the essential statistical tools that data scientists use.

Data Science 195

Data Science Hypothesis Testing Machine Learning Machine Learning 195

Mlearning.ai

JUNE 6, 2023

Photo by Google DeepMind on Unsplash Introduction Large language models, or LLMs, are powerful deep learning algorithms that are capable of a range of tasks, including recognizing, summarizing, translating, predicting, and generating text and other content. Energy consumption of some large deep learning models.

Hypothesis Testing 52

Hypothesis Testing Deep Learning Deep Learning Natural Language Processing 52