Machine Learning & Artificial Intelligence | Data Science Free Courses

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Top 10 essential data science terminologies 1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data. 2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from. 3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis. 4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data. 5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language. 6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data. 7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models. 8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily. 9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. 10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting. Credits: https://t.me/datasciencefree ENJOY LEARNING 👍👍

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Understanding Bias and Variance in Machine Learning Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias). Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance) There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting. Techniques like cross validation can be helpful in these cases. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖

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Popular Python packages for data science: 1. NumPy: For numerical operations and working with arrays. 2. Pandas: For data manipulation and analysis, especially with data frames. 3. Matplotlib and Seaborn: For data visualization. 4. Scikit-learn: For machine learning algorithms and tools. 5. TensorFlow and PyTorch: Deep learning frameworks. 6. SciPy: For scientific and technical computing. 7. Statsmodels: For statistical modeling and hypothesis testing. 8. NLTK and SpaCy: Natural Language Processing libraries. 9. Jupyter Notebooks: Interactive computing and data visualization. 10. Bokeh and Plotly: Additional libraries for interactive visualizations.

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Prompt Engineering in itself does not warrant a separate job. Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT. You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc. The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.