Explore NumPy basics: what NumPy is and why to use it for arrays and matrices. Learn indexing, slicing, and operations, plus memory efficiency and broadcasting for data science and TensorFlow.

Master NumPy arrays as the core data structure, creating them with np.array, arange, zeros, ones, linspace, and eye, and generating random data with seed for reproducibility, reshaping and inspecting dtype.

Master numpy indexing and selection from single values to slices, broadcast operations, and two-dimensional arrays, then apply conditional selection leveraged in pandas for data cleaning.

Master NumPy operations with element-wise arithmetic, universal array functions, and summary statistics across 1D and 2D arrays, while understanding axis, shape, and handling nan and infinity.

Create pandas series from lists, numpy arrays, or dictionaries with a labeled index, access by label or position, and perform additions that may yield not a number for missing labels.

Learn to handle missing data by three strategies: leave it as missing, drop missing values with dropna, or fill with mean, median, or mode, including per column filling.

Master pandas exercises by loading data with read_csv, inspecting with head, using unique, nunique; then sort, filter, group to analyze crises, inflation, exchange rates for Kenya, Zimbabwe, Algeria.

Learn to create basic plots with Matplotlib in Python, including simple line and scatter plots, labels, titles, axis limits, and basic styling.

Learn Seaborn basics for creating standard statistical plots with one-line calls, using pandas dataframes, and blending with matplotlib for customization on a heart disease dataset.

Summarize how to evaluate classification performance using accuracy, recall, precision, and F1 score with a confusion matrix. Explore precision-recall trade-offs, especially for unbalanced data, and connect metrics to real-world contexts.

Evaluate regression performance using mean absolute error, mean squared error, and root mean squared error to compare continuous predictions with true values, considering outliers and unit interpretation.

Learn Keras syntax basics for TensorFlow with a regression example, preparing data using train_test_split, extracting features and price, and scaling features with MinMaxScaler before building a neural network.

Separate features from the label, perform a train-test split, scale with a MinMax scaler, then build and train a Keras sequential regression model with validation data.

learn to build a binary classifier with Keras, address overfitting using early stopping and dropout, train with validation data, and evaluate performance with classification report and confusion matrices.

Explore image filters and kernels, learn how sliding a 3x3 kernel over images creates blurred and edge-detected outputs, and see how CNNs learn weights in convolutional layers.

Explore pooling layers in convolutional neural networks to reduce large feature maps and parameters, compare max pooling and average pooling, and learn how dropout and architecture choices affect performance.

Create and train a simple CNN for MNIST with Conv2D, MaxPool2D, Flatten, and Dense layers, using a 10-class softmax output, categorical_crossentropy loss, and early stopping.

Learn to train and evaluate a basic recurrent neural network on a sine wave, using a time-series train-test split, scaling, and preparing batches with the time-series generator in Keras.

Explore text data with deep learning by building a character-based recurrent neural network that writes text from the Shakespeare corpus, using embedding, GRU, and temperature-tuned dense outputs in TensorFlow.

Create shuffles and batches for a character-based text generator in TensorFlow, using 120-character sequences and a 128-sample batch, mapping inputs to targets by shifting one step.

train a 3 million-parameter nlp model on a gpu, use Google Colab with TensorFlow 2.x, test random predictions, and prepare to load the saved shakespeare model.