Machine Learning & Artificial Intelligence | Data Science Free Courses

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Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌 I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer: Math & Statistics Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics. Here are the probability units you will need to focus on: Basic probability concepts statistics Inferential statistics Regression analysis Experimental design and A/B testing Bayesian statistics Calculus Linear algebra Python: You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning. Variables, data types, and basic operations Control flow statements (e.g., if-else, loops) Functions and modules Error handling and exceptions Basic data structures (e.g., lists, dictionaries, tuples) Object-oriented programming concepts Basic work with APIs Detailed data structures and algorithmic thinking Machine Learning Prerequisites: Exploratory Data Analysis (EDA) with NumPy and Pandas Basic data visualization techniques to visualize the variables and features. Feature extraction Feature engineering Different types of encoding data Machine Learning Fundamentals Using scikit-learn library in combination with other Python libraries for: Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees) Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering) Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients) Solving two types of problems: Regression Classification Neural Networks: Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions. Types of Neural Networks: Feedforward Neural Networks: Simplest form, with straight connections and no loops. Convolutional Neural Networks (CNNs): Great for images, learning visual patterns. Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information. In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems. Deep Learning: Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory Networks (LSTMs) Generative Adversarial Networks (GANs) Autoencoders Deep Belief Networks (DBNs) Transformer Models Machine Learning Project Deployment Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at: Version Control for Data and Models Automated Testing and Continuous Integration (CI) Continuous Delivery and Deployment (CD) Monitoring and Logging Experiment Tracking and Management Feature Stores Data Pipeline and Workflow Orchestration Infrastructure as Code (IaC) Model Serving and APIs 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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Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

Unlock LBank New User Benefits Join LBank and access new user benefits, including a 30 day VIP2 trial, transfer rewards up to 200 USDT, and additional trading bonuses. Sponsored By WaybienAds

✅ Data Scientists in Your 20s – Avoid This Trap 🚫🧠 🎯 The Trap? → Passive Learning  Feels like you’re learning but not truly growing. 🔍 Example: ⦁ Watching endless ML tutorial videos ⦁ Saving notebooks without running or understanding ⦁ Joining courses but not coding models ⦁ Reading research papers without experimenting End result?  ❌ No models built from scratch  ❌ No real data cleaning done  ❌ No insights or reports delivered This is passive learning — absorbing without applying. It builds false confidence and slows progress. 🛠️ How to Fix It:  1️⃣ Learn by doing: Grab real datasets (Kaggle, UCI, public APIs)  2️⃣ Build projects: Classification, regression, clustering tasks  3️⃣ Document findings: Share explanations like you’re presenting to stakeholders  4️⃣ Get feedback: Post code & reports on GitHub, Kaggle, or LinkedIn  5️⃣ Fail fast: Debug models, tune hyperparameters, iterate frequently 📌 In your 20s, build practical data intuition — not just theory or certificates. Stop passive watching.  Start real modeling.  Start storytelling with data. That’s how data scientists grow fast in the real world! 🚀 💬 Tap ❤️ if this resonates with you!

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