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To start with Machine Learning:    1. Learn Python    2. Practice using Google Colab     Take these free courses: https://t.me/datasciencefun/290 If you need a bit more time before diving deeper, finish the Kaggle tutorials. At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle. If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed. From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit. The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them: https://t.me/datasciencefree/259 Many different books will help you. The attached image will give you an idea of my favorite ones. Finally, keep these three ideas in mind: 1. Start by working on solved problems so you can find help whenever you get stuck. 2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice. 3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others. During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right. Here is the good news: Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space. Focus on finding your path, and Write. More. Code. That's how you win.✌️✌️

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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

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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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1. Can you explain how the memory cell in an LSTM is implemented computationally? The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state. 2. What is CTE in SQL? A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed. 3. List the advantages NumPy Arrays have over Python lists? Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done. 4. What’s the F1 score? How would you use it? The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. 5. Name an example where ensemble techniques might be useful? Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.

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