Machine Learning & Artificial Intelligence | Data Science Free Courses

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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.

Essential Data Science Key Concepts 1. Data: Data is the raw information that is collected and stored. It can be structured (in databases or spreadsheets) or unstructured (text, images, videos). Data can be quantitative (numbers) or qualitative (descriptions). 2. Data Cleaning: Data cleaning involves identifying and correcting errors in the dataset, handling missing values, removing outliers, and ensuring data quality before analysis. 3. Data Exploration: Data exploration involves summarizing the main characteristics of the data, understanding data distributions, identifying patterns, and detecting correlations or relationships within the data. 4. Descriptive Statistics: Descriptive statistics are used to describe and summarize the main features of a dataset. This includes measures like mean, median, mode, standard deviation, and visualization techniques. 5. Data Visualization: Data visualization is the graphical representation of data to help in understanding patterns, trends, and insights. Common visualization tools include bar charts, histograms, scatter plots, and heatmaps. 6. Statistical Inference: Statistical inference involves drawing conclusions from data with uncertainty. It includes hypothesis testing, confidence intervals, and regression analysis to make predictions or draw insights from data. 7. Machine Learning: Machine learning is a subset of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without being explicitly programmed. It includes supervised learning, unsupervised learning, and reinforcement learning. 8. Feature Engineering: Feature engineering is the process of selecting, transforming, and creating features (input variables) to improve model performance in machine learning tasks. 9. Model Evaluation: Model evaluation involves assessing the performance of a machine learning model using metrics like accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrix. 10. Data Preprocessing: Data preprocessing involves preparing the data for analysis or modeling. This includes encoding categorical variables, scaling numerical data, and splitting the data into training and testing sets.

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⚡️ Big ML cheat sheet Here you will find the basic theory of Machine Learning and examples of the implementation of specific ML algorithms - in general, this is just the thing to brush up on your knowledge before the interview. 📎 Crib