Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine learning types

New developers: whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.) I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.

Machine Learning Algorithm cheat sheet

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🚀 Required Skills for a data scientist 🎯Statistics and Probability 🎯Mathematics 🎯Python, R, SAS and Scala or other. 🎯Data visualisation 🎯Big data 🎯Data inquisitiveness 🎯Business expertise 🎯Critical thinking 🎯Machine learning, deep learning and AI 🎯Communication skills 🎯Teamwork

Data Science vs. Data Analytics

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Top 5 data science concepts 👇 1. Machine Learning: Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data. It involves techniques such as supervised learning, unsupervised learning, and reinforcement learning to analyze and interpret patterns in data. 2. Data Visualization: Data visualization is the graphical representation of data to help users understand complex datasets and identify trends, patterns, and insights. It involves creating visualizations such as charts, graphs, maps, and dashboards to communicate data effectively and facilitate data-driven decision-making. 3. Statistical Analysis: Statistical analysis is the process of collecting, exploring, analyzing, and interpreting data to uncover patterns, relationships, and trends. It involves using statistical methods such as hypothesis testing, regression analysis, and probability theory to draw meaningful conclusions from data and make informed decisions. 4. Data Preprocessing: Data preprocessing is the initial step in the data analysis process that involves cleaning, transforming, and preparing raw data for analysis. It includes tasks such as data cleaning, feature selection, normalization, and handling missing values to ensure the quality and reliability of the data before applying machine learning algorithms. 5. Big Data: Big data refers to large and complex datasets that exceed the processing capabilities of traditional data management tools. It involves storing, processing, and analyzing massive volumes of structured and unstructured data to extract valuable insights and drive informed decision-making. Techniques such as distributed computing, parallel processing, and cloud computing are used to handle big data efficiently. These concepts are fundamental to data science and play a crucial role in extracting meaningful insights from data, building predictive models, and making informed decisions based on data-driven analysis. As a data scientist or data analyst, having a strong understanding of these concepts will help you effectively work with data and derive valuable insights to drive business outcomes.

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🔐"Key Python Libraries for Data Science: Numpy: Core for numerical operations and array handling. SciPy: Complements Numpy with scientific computing features like optimization. Pandas: Crucial for data manipulation, offering powerful DataFrames. Matplotlib: Versatile plotting library for creating various visualizations. Keras: High-level neural networks API for quick deep learning prototyping. TensorFlow: Popular open-source ML framework for building and training models. Scikit-learn: Efficient tools for data mining and statistical modeling. Seaborn: Enhances data visualization with appealing statistical graphics. Statsmodels: Focuses on estimating and testing statistical models. NLTK: Library for working with human language data. These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."

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