Machine Learning & Artificial Intelligence | Data Science Free Courses

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10 great Python packages for Data Science not known to many: 1️⃣ CleanLab Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. 2️⃣ LazyPredict A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code. 3️⃣ Lux A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data. 4️⃣ PyForest A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code. 5️⃣ PivotTableJS PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥 6️⃣ Drawdata Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. 7️⃣ black The Uncompromising Code Formatter 8️⃣ PyCaret An open-source, low-code machine learning library in Python that automates the machine learning workflow. 9️⃣ PyTorch-Lightning by LightningAI Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation. 🔟 Streamlit A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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https://topmate.io/analyst/1024129 If you're a job seeker, these well structured document resources will help you to know and learn all the real time Data Science & Machine Learning Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide! Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

Fastest way to excel at Data Interviews: Take as many Interviews as possible. Don't be too picky with the roles you apply for as a beginner. Cast a wide net and apply for every data-related position you can find. What's the worst that could happen? You might get rejected. So what? Remember: ☑ Each interview is a learning opportunity ☑ You'll refine your coding skills with every technical round ☑ Your data visualization explanations will get clearer each time ☑ You'll get more comfortable discussing your projects and impact. There are 2 types of data enthusiasts out there: Those who ace data analyst interviews and those who don't apply enough. 💡 Pro Tip: Keep an "interview journal" to note what worked, what didn't, and areas for improvement. Your future self will thank you! I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

How to enter into Data Science 👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation. 👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it. 👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills. 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 😊

Let's see the step-by-step process of Machine Learning! Step 1: Define the Problem Start by identifying the problem you wish to solve. Set clear goals and establish criteria for measuring success. Understanding the problem thoroughly is pivotal for the project's success. Step 2: Acquire and Explore Data Collect relevant data pertinent to the identified problem. Delve into the data to comprehend its characteristics, quality, and interrelationships. This preliminary analysis lays the groundwork for subsequent model development. Step 3: Prepare the Data Cleanse the data, address missing values, and engineer new features as necessary. This preprocessing phase ensures that the data is primed for training machine learning models. Step 4: Select and Train Models Choose suitable machine learning algorithms and train multiple models. Evaluate their performance using diverse techniques to identify the most effective approach. Step 5: Evaluate Models and Enhance Performance Assess the performance of trained models using various evaluation metrics. Fine-tune model parameters to optimize performance and iteratively enhance results. Step 6: Deployment Prepare the trained model for deployment into production. Collaborate closely with relevant teams to ensure seamless integration and performance monitoring. Step 7: Monitoring and Maintenance Continuously monitor the deployed model's performance in real-world scenarios. Regularly update and retrain the model with new data to maintain accuracy and relevance. Step 8: Documentation and Reporting Document the entire project, including methodologies, findings, and insights. Comprehensive documentation ensures transparency and facilitates the reproducibility of the project. I have curated the best resources to learn Data Science & Machine Learning 👇👇 https://topmate.io/coding/914624 All the best 👍👍

7 things you should know before becoming a Data Scientist: 7/ Higher complexity solutions =/= higher impact solutions. 6/ The best Data Scientists do much more than Data Science. They lead product teams, they talk to customers, they build pipelines etc. 5/ You won’t get along with every business partner. But you have to learn how to work with them. 4/ A lot of Data Science work is tedious and boring and repetitive. 3/ You will spend so much more time on communication than you expect. 2/ Data quality is often more important than fancy algorithms. 1/ You’ll make mistakes, a lot of it. What matters more is how you recover and grow from them. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

How to get started with data science Many people who get interested in learning data science don't really know what it's all about. They start coding just for the sake of it and on first challenge or problem they can't solve, they quit. Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude. If you're among people who want to get started with data science but don't know how - I have something amazing for you! I created Best Data Science & Machine Learning Resources that will help you organize your career in data, from first learning day to a job in tech. Share this channel link with someone who wants to get into data science and AI but is confused. 👇👇 https://t.me/datasciencefun Happy learning 😄😄

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Data Science Roadmap | |-- Fundamentals |   |-- Mathematics |   |   |-- Linear Algebra |   |   |-- Calculus |   |   |-- Probability and Statistics |   | |   |-- Programming |   |   |-- Python |   |   |-- R |   |   |-- SQL | |-- Data Collection and Cleaning |   |-- Data Sources |   |   |-- APIs |   |   |-- Web Scraping |   |   |-- Databases |   | |   |-- Data Cleaning |   |   |-- Missing Values |   |   |-- Data Transformation |   |   |-- Data Normalization | |-- Data Analysis |   |-- Exploratory Data Analysis (EDA) |   |   |-- Descriptive Statistics |   |   |-- Data Visualization |   |   |-- Hypothesis Testing |   | |   |-- Data Wrangling |   |   |-- Pandas |   |   |-- NumPy |   |   |-- dplyr (R) | |-- Machine Learning |   |-- Supervised Learning |   |   |-- Regression |   |   |-- Classification |   | |   |-- Unsupervised Learning |   |   |-- Clustering |   |   |-- Dimensionality Reduction |   | |   |-- Reinforcement Learning |   |   |-- Q-Learning |   |   |-- Policy Gradient Methods |   | |   |-- Model Evaluation |   |   |-- Cross-Validation |   |   |-- Performance Metrics |   |   |-- Hyperparameter Tuning | |-- Deep Learning |   |-- Neural Networks |   |   |-- Feedforward Networks |   |   |-- Backpropagation |   | |   |-- Advanced Architectures |   |   |-- Convolutional Neural Networks (CNN) |   |   |-- Recurrent Neural Networks (RNN) |   |   |-- Transformers |   | |   |-- Tools and Frameworks |   |   |-- TensorFlow |   |   |-- PyTorch | |-- Natural Language Processing (NLP) |   |-- Text Preprocessing |   |   |-- Tokenization |   |   |-- Stop Words Removal |   |   |-- Stemming and Lemmatization |   | |   |-- NLP Techniques |   |   |-- Word Embeddings |   |   |-- Sentiment Analysis |   |   |-- Named Entity Recognition (NER) | |-- Data Visualization |   |-- Basic Plotting |   |   |-- Matplotlib |   |   |-- Seaborn |   |   |-- ggplot2 (R) |   | |   |-- Interactive Visualization |   |   |-- Plotly |   |   |-- Bokeh |   |   |-- Dash | |-- Big Data |   |-- Tools and Frameworks |   |   |-- Hadoop |   |   |-- Spark |   | |   |-- NoSQL Databases |       |-- MongoDB |       |-- Cassandra | |-- Cloud Computing |   |-- Cloud Platforms |   |   |-- AWS |   |   |-- Google Cloud |   |   |-- Azure |   | |   |-- Data Services |       |-- Data Storage (S3, Google Cloud Storage) |       |-- Data Pipelines (Dataflow, AWS Data Pipeline) | |-- Model Deployment |   |-- Serving Models |   |   |-- Flask/Django |   |   |-- FastAPI |   | |   |-- Model Monitoring |       |-- Performance Tracking |       |-- A/B Testing | |-- Domain Knowledge |   |-- Industry-Specific Applications |   |   |-- Finance |   |   |-- Healthcare |   |   |-- Retail | |-- Ethical and Responsible AI |   |-- Bias and Fairness |   |-- Privacy and Security |   |-- Interpretability and Explainability | |-- Communication and Storytelling |   |-- Reporting |   |-- Dashboarding |   |-- Presentation Skills | |-- Advanced Topics |   |-- Time Series Analysis |   |-- Anomaly Detection |   |-- Graph Analytics └-- Comments     |-- # Single-line comment (Python)     └-- /* Multi-line comment (Python/R) */ I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Most Important Mathematical Equations in Data Science! 1️⃣ Gradient Descent: Optimization algorithm minimizing the cost function. 2️⃣ Normal Distribution: Distribution characterized by mean μ\muμ and variance σ2\sigma^2σ2. 3️⃣ Sigmoid Function: Activation function mapping real values to 0-1 range. 4️⃣ Linear Regression: Predictive model of linear input-output relationships. 5️⃣ Cosine Similarity: Metric for vector similarity based on angle cosine. 6️⃣ Naive Bayes: Classifier using Bayes’ Theorem and feature independence. 7️⃣ K-Means: Clustering minimizing distances to cluster centroids. 8️⃣ Log Loss: Performance measure for probability output models. 9️⃣ Mean Squared Error (MSE): Average of squared prediction errors. 🔟 MSE (Bias-Variance Decomposition): Explains MSE through bias and variance. 1️⃣1️⃣ MSE + L2 Regularization: Adds penalty to prevent overfitting. 1️⃣2️⃣ Entropy: Uncertainty measure used in decision trees. 1️⃣3️⃣ Softmax: Converts logits to probabilities for classification. 1️⃣4️⃣ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals. 1️⃣5️⃣ Correlation: Measures linear relationships between variables. 1️⃣6️⃣ Z-score: Standardizes value based on standard deviations from mean. 1️⃣7️⃣ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood. 1️⃣8️⃣ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices. 1️⃣9️⃣ R-squared (R²): Proportion of variance explained by regression. 2️⃣0️⃣ F1 Score: Harmonic mean of precision and recall. 2️⃣1️⃣ Expected Value: Weighted average of all possible values. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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Use this checklist to see if you’re truly JOB-READY. The more items you complete, the closer you are to landing your dream data science job! 😎 Check Your Skills with This Checklist! Python:- Master Python fundamentals Understand Pandas for data manipulation Learn data visualization with Matplotlib and Seaborn Practice error handling and debugging Statistics:- Grasp probability theory Know descriptive and inferential statistics Learn statistical machine learning concepts Exploratory Data Analysis (EDA):- Perform data summarization Work on data cleaning and transformation Visualize data effectively SQL:- Understand the BIG 6 SQL statements Practice joins and common table expressions (CTEs) Use window functions Learn to write stored procedures Machine Learning:- Master feature engineering Understand regression and classification techniques Learn clustering methods Model Evaluation:- Work with confusion matrices Understand precision, recall, and F1-score Practice cross-validation Learn about overfitting and underfitting Deep Learning:- Get familiar with Convolutional Neural Networks (CNNs) Understand transformers Learn PyTorch or TensorFlow basics Practice model training and optimization Resume:- Ensure your resume is ATS-friendly Customize for the job description Use the STAR method to highlight achievements Include a link to your portfolio AI-Enabled Mindset:- Develop Googling skills Use AI tools like ChatGPT or Bard for learning Commit to continuous learning Hone problem-solving abilities Communication:- Practice presenting insights clearly Write professional emails Manage stakeholder communication Utilize project management tools LinkedIn:- Have a good profile picture and banner Get 10+ endorsed skills Collect at least 3 recommendations Link your portfolio in your profile Portfolio:- Include 4+ business-related projects Showcase one project per tool you know Create an insights desk Prepare a video presentation I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Supervised vs. Unsupervised Learning - Overfitting and Underfitting - Cross-validation - Bias-Variance Tradeoff - Accuracy vs Interpretability - Accuracy vs Latency 𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 - Logistic Regression - Decision Trees - Random Forest - Support Vector Machines - K-Nearest Neighbors - Naive Bayes - Linear Regression - Ridge and Lasso Regression - K-Means Clustering - Hierarchical Clustering - PCA 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀 - EDA - Data Cleaning (e.g. missing value imputation) - Data Preprocessing (e.g. scaling) - Feature Engineering (e.g. aggregation) - Feature Selection (e.g. variable importance) - Model Training (e.g. gradient descent) - Model Evaluation (e.g. AUC vs Accuracy) - Model Productionization 𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 - Grid Search - Random Search - Bayesian Optimization 𝗠𝗟 𝗖𝗮𝘀𝗲𝘀 - [Capital One] Detect credit card fraudsters - [Amazon] Forecast monthly sales - [Airbnb] Estimate lifetime value of a guest I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Many data scientists don't know how to push ML models to production. Here's the recipe 👇 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time 🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model 🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github 🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API 🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker 🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs 🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling. 𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility. 𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment 𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker. 𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍