Mlearning.ai

FEBRUARY 23, 2023

Perceptron Implementation in Python: Understanding the Basics of Artificial Neural Networks Photo by Jeremy Perkins on Unsplash Perceptron is the most basic unit of an artificial neural network. Python Let’s code a perceptron in Python. It takes several inputs and outputs a single binary decision. A Perceptron.

Python 52

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Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation 98

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PyImageSearch

JANUARY 23, 2023

In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects.

Deep Learning 109

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation. during the forecast period.

Machine Learning 52

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Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

Machine Learning 195

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost.

Machine Learning 52

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Pickl AI

NOVEMBER 2, 2023

Data Scientists use a wide range of tools and programming languages such as Python and R to extract meaningful patterns and trends from data. Proficiency in programming languages like Python and R is essential for data manipulation, analysis, and visualization. Machine Learning Machine learning is at the heart of Data Science.

Data Scientist 52

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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. What is cross-validation, and why is it used in Machine Learning?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 6, 2023

MNIST examples Experiment on MNIST Figure 3 shows the 2D CNN architecture that was trained and validated using 10-fold cross-validation on the MNIST dataset. The answer is … almost , and I will show you this in an experiment on the well-known MNIST dataset (Figure 2 shows examples from the MNIST dataset).

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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

Heartbeat

DECEMBER 19, 2023

With the advent of Deep Learning, recommender systems have seen significant advancements. Integration of Comet: Unleashing the Power of Experiment Management and Performance Tracking We need to install and set Comet up to get started with the library in our Python environment. We pay our contributors, and we don't sell ads.

Deep Learning 52

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Pickl AI

AUGUST 9, 2024

Apache Spark A fast, in-memory data processing engine that provides support for various programming languages, including Python, Java, and Scala. Students should learn about Spark’s core concepts, including RDDs (Resilient Distributed Datasets) and DataFrames. Students should learn about neural networks and their architecture.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Heartbeat

MAY 29, 2023

Hyperparameters are the configuration variables of a machine learning algorithm that are set prior to training, such as learning rate, number of hidden layers, number of neurons per layer, regularization parameter, and batch size, among others.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Pickl AI

NOVEMBER 18, 2024

Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold. On the other hand, overfitting arises when a model is too complex, learning noise and irrelevant details rather than generalisable trends.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

DECEMBER 12, 2024

You can use either pip or conda , depending on your package management preference: Using pip Using conda Ensure you have the latest version of Python and a functional environment for seamless installation. Following these tips, you can implement and optimise XGBoost for any Machine Learning project.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

DagsHub

APRIL 21, 2024

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. How do you handle large datasets in Python?

Data Analyst 52

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Pickl AI

SEPTEMBER 18, 2024

offer specialised Machine Learning and Artificial Intelligence courses covering Deep Learning , Natural Language Processing, and Reinforcement Learning. Here are the core technical skills you need: Programming Languages Python and R are the most commonly used programming languages in Machine Learning.

Machine Learning 52

Machine Learning Machine Learning Algorithm Natural Language Processing 52

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

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Mlearning.ai

FEBRUARY 15, 2024

There are many algorithms which can be used from this task ranging from Logistic regression to Deep learning. This cross-validation results shows without regularization. Since our target variable has two classes, we are working with a Binary Classification problem. So we will use accuracy to optimize our hyperparameters.

Data Science 52

Data Science Decision Trees Exploratory Data Analysis ML 52

Mlearning.ai

FEBRUARY 2, 2023

Normalized age distribution in training and test set [3] The model was implemented in Python and stored in a public GitHub repository (containing source code and the trained models). The use of Jupyter Notebooks was done in order to make it possible to train and validate the models on Google Colab in order to get access to free GPUs.

Cross Validation 52

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How to Learn Machine Learning

MAY 21, 2025

Implementing the Brier Score in Python Enough theory let’s get our hands dirty with some Python ! This is precisely why it’s such a valuable tool for evaluating probabilistic models. random_state=42) # Train a base classifier base_clf = LogisticRegression(C=1.0) 0.1], # Prediction for sample 1 [0.3, 0.7], # And so on.

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