Machine Learning & Artificial Intelligence | Data Science Free Courses

7 Baby steps to start with Machine Learning: 1. Start with Python 2. Learn to use Google Colab 3. Take a Pandas tutorial 4. Then a Seaborn tutorial 5. Decision Trees are a good first algorithm 6. Finish Kaggle's "Intro to Machine Learning" 7. Solve the Titanic challenge

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How do you start AI and ML ? Where do you go to learn these skills? What courses are the best? There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos. What’s more important than how you start is why you start. Start with why. Why do you want to learn these skills? Do you want to make money? Do you want to build things? Do you want to make a difference? Again, no right reason. All are valid in their own way. Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started. Got a why? Good. Time for some hard skills. I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course You can completed courses from (in order): Treehouse / youtube( free) - Introduction to Python Udacity - Deep Learning & AI Nanodegree fast.ai - Part 1and Part 2 They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that. If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI. Join for more: https://t.me/machinelearning_deeplearning 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

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10 commonly asked data science interview questions along with their answers 1️⃣ What is the difference between supervised and unsupervised learning? Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data. 2️⃣ Explain the bias-variance tradeoff in machine learning. The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance. 3️⃣ What is the Central Limit Theorem and why is it important in statistics? The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes. 4️⃣ Describe the process of feature selection and why it is important in machine learning. Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy. 5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them? Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data. 6️⃣ What is regularization and why is it used in machine learning? Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features. 7️⃣ How do you handle missing data in a dataset? Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly. 8️⃣ What is the difference between classification and regression in machine learning? Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome. 9️⃣ Explain the concept of cross-validation and why it is used. Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting. 🔟 What evaluation metrics would you use to evaluate a binary classification model? Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem. 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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An important collection of the 15 best machine learning cheat sheets. 1- Supervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf 2- Unsupervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf 3- Deep Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf 4- Machine Learning Tips and Tricks https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf 5- Probabilities and Statistics https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf 6- Comprehensive Stanford Master Cheat Sheet https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf 7- Linear Algebra and Calculus https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf 8- Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf 9- Keras Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 10- Deep Learning with Keras Cheat Sheet https://github.com/rstudio/cheatsheets/raw/master/keras.pdf 11- Visual Guide to Neural Network Infrastructures http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png 12- Skicit-Learn Python Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf 13- Scikit-learn Cheat Sheet: Choosing the Right Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/ 14- Tensorflow Cheat Sheet https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf 15- Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ ENJOY LEARNING 👍👍

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Types of Databases

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Data Science Roles