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Artificial intelligence can change your career by 180 degrees! 📌 Here's how you can start with AI engineering with zero experience! The simplest definition of artificial intelligence| Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence. AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more. Step 1: Master the prerequisites Basics of programming Probability and statistics essentials Data structures Data analysis essentials Step 2: Get into machine learning and deep learning Basics of data science, an intersection field Feature engineering and machine learning Neural networks and deep learning Scikit-learn for machine learning along with Numpy, Pandas and matplotlib TensorFlow, Keras and PyTorch for deep learning Step 3: Exploring Generative Adversarial Networks (GANs) Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data. Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer. Step 4: Get into Transformers architecture Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure. Implementations: Use libraries like Hugging Face’s Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks. Step 5: Working with Pre-trained Large Language Models Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis. Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance. Step 6: Introduction to LangChain Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities. Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks. Step 7: Leveraging Vector Databases Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models. Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings. Practical application: Integrate vector databases into your projects for enhanced search functionalities Step 8: Exploration of Retrieval-Augmented Generation (RAG) Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs. Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications. Step 9: Deployment of AI Projects Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models. Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging. Step 10: Keep building Implement Projects and Gain Practical Experience Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition. Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others. Hope this helps you ☺️

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1. What is the difference between the RANK() and DENSE_RANK() functions? The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5. 2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset? One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0. 3. What is the shortcut to add a filter to a table in EXCEL? The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L. 4. What is DAX in Power BI? DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have. 5. Define shelves and sets in Tableau? Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data. Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.

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Top 10 machine Learning algorithms 1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output. 2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class. 3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure. 4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees. 5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes. 6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set. 7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label. 8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training. 9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors. 10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data. Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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