Artificial Intelligence & ChatGPT Prompts

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Data Scientist Roadmap | |-- 1. Basic Foundations | |-- a. Mathematics | | |-- i. Linear Algebra | | |-- ii. Calculus | | |-- iii. Probability | | `-- iv. Statistics | | | |-- b. Programming | | |-- i. Python | | | |-- 1. Syntax and Basic Concepts | | | |-- 2. Data Structures | | | |-- 3. Control Structures | | | |-- 4. Functions | | | `-- 5. Object-Oriented Programming | | | | | `-- ii. R (optional, based on preference) | | | |-- c. Data Manipulation | | |-- i. Numpy (Python) | | |-- ii. Pandas (Python) | | `-- iii. Dplyr (R) | | | `-- d. Data Visualization | |-- i. Matplotlib (Python) | |-- ii. Seaborn (Python) | `-- iii. ggplot2 (R) | |-- 2. Data Exploration and Preprocessing | |-- a. Exploratory Data Analysis (EDA) | |-- b. Feature Engineering | |-- c. Data Cleaning | |-- d. Handling Missing Data | `-- e. Data Scaling and Normalization | |-- 3. Machine Learning | |-- a. Supervised Learning | | |-- i. Regression | | | |-- 1. Linear Regression | | | `-- 2. Polynomial Regression | | | | | `-- ii. Classification | | |-- 1. Logistic Regression | | |-- 2. k-Nearest Neighbors | | |-- 3. Support Vector Machines | | |-- 4. Decision Trees | | `-- 5. Random Forest | | | |-- b. Unsupervised Learning | | |-- i. Clustering | | | |-- 1. K-means | | | |-- 2. DBSCAN | | | `-- 3. Hierarchical Clustering | | | | | `-- ii. Dimensionality Reduction | | |-- 1. Principal Component Analysis (PCA) | | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE) | | `-- 3. Linear Discriminant Analysis (LDA) | | | |-- c. Reinforcement Learning | |-- d. Model Evaluation and Validation | | |-- i. Cross-validation | | |-- ii. Hyperparameter Tuning | | `-- iii. Model Selection | | | `-- e. ML Libraries and Frameworks | |-- i. Scikit-learn (Python) | |-- ii. TensorFlow (Python) | |-- iii. Keras (Python) | `-- iv. PyTorch (Python) | |-- 4. Deep Learning | |-- a. Neural Networks | | |-- i. Perceptron | | `-- ii. Multi-Layer Perceptron | | | |-- b. Convolutional Neural Networks (CNNs) | | |-- i. Image Classification | | |-- ii. Object Detection | | `-- iii. Image Segmentation | | | |-- c. Recurrent Neural Networks (RNNs) | | |-- i. Sequence-to-Sequence Models | | |-- ii. Text Classification | | `-- iii. Sentiment Analysis | | | |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) | | |-- i. Time Series Forecasting | | `-- ii. Language Modeling | | | `-- e. Generative Adversarial Networks (GANs) | |-- i. Image Synthesis | |-- ii. Style Transfer | `-- iii. Data Augmentation | |-- 5. Big Data Technologies | |-- a. Hadoop | | |-- i. HDFS | | `-- ii. MapReduce | | | |-- b. Spark | | |-- i. RDDs | | |-- ii. DataFrames | | `-- iii. MLlib | | | `-- c. NoSQL Databases | |-- i. MongoDB | |-- ii. Cassandra | |-- iii. HBase | `-- iv. Couchbase | |-- 6. Data Visualization and Reporting | |-- a. Dashboarding Tools | | |-- i. Tableau | | |-- ii. Power BI | | |-- iii. Dash (Python) | | `-- iv. Shiny (R) | | | |-- b. Storytelling with Data | `-- c. Effective Communication | |-- 7. Domain Knowledge and Soft Skills | |-- a. Industry-specific Knowledge | |-- b. Problem-solving | |-- c. Communication Skills | |-- d. Time Management | `-- e. Teamwork | `-- 8. Staying Updated and Continuous Learning |-- a. Online Courses |-- b. Books and Research Papers |-- c. Blogs and Podcasts |-- d. Conferences and Workshops `-- e. Networking and Community Engagement

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How to use AI to learn Anything Faster 👆

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AI & LLM Project Ideas 👆

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Paid vs Free AI Tools 👆

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