C.7

Python and Data Concepts--> Must haves and a huge time saving

1. Algebra Essentials Exponents & Radicals: Laws of exponents, roots, negative powers, and fractional exponents. Logarithms: Properties of logs (product, quotient, power rules). Absolute Value: Key rules and inequalities. Quadratics: Factorization, completing the square, and quadratic formula. 2. Functions & Graphs Domains: Restrictions from denominators, logs, roots, and trig functions. Parity: Even, odd, or neither. Intercepts: Solve for x- and y-intercepts. Asymptot...

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1. Pandas – Data Structures & Manipulation Series: 1D labeled array (like a column). DataFrame: 2D labeled data (like a spreadsheet/SQL table). Handling Missing Data: dropna(), fillna(). Selection/Filtering: By column, row index, boolean conditions. Sorting: sort_values(), sort_index(). Merging/Joining: concat(), merge(). Transformation: apply(), replace(). Loading Data: From CSV, JSON, Excel, HTML, etc. Cleaning: Handle duplicates, missing values, outliers. 2. Pa...

It is time saving sheet.

1. Integer Rules Addition: Same signs → add & keep sign; different signs → subtract & keep larger number’s sign. Subtraction: Use Keep-Change-Change (KCC rule). Multiplication & Division: Same signs → positive; different signs → negative. 2. Solving Equations Golden Rule: Whatever you do to one side, you must do to the other. Steps: Clear fractions. Apply distributive property (if needed). Combine like terms. Move variables to one side, constants to the ot...

1. Introduction TensorFlow: Open-source ML library from Google Brain for deep learning and general computation. Scalability: Runs on CPUs, GPUs, TPUs, mobile devices. Flexibility: Supports deep learning, traditional ML, and general computations. Visualization: TensorBoard for debugging and performance tracking. Deployment: TensorFlow Serving, TensorFlow Lite, TensorF. 2. Tensor Basics Tensors: Multi-dimensional arrays (scalars, vectors, matrices, higher-rank). Rank: Numbe...

1. Distributions Discrete: Binomial, Geometric, Negative Binomial, Hypergeometric, Poisson. Continuous: Uniform, Normal/Gaussian (Central Limit Theorem, Empirical Rule), Exponential, Gamma. 2. Core Concepts Prediction Error = Bias² + Variance + Irreducible Noise. Bias-Variance Tradeoff: Underfitting vs. Overfitting. Model Types: Parametric vs. Non-Parametric. Cross Validation: k-fold, leave-p-out. 3. Model Evaluation Regression: MSE, SSE, SST, R², Adjusted R². Cl...

Complete biochemistry study bundle covering metabolism, enzyme kinetics, protein structure, mitochondria, lipids, and bioinformatics. Ideal for exam prep with high-yield summaries, clinical links, and molecular insights across core biochemical systems.