7 Days of Hands-On AI Development Bootcamp and Certification

Learn Python fundamentals for AI development, writing simple programs, using variables, data types, operators, loops, and control flow, and get started with NumPy and pandas for data manipulation.

Introduce Python for AI basics, covering installation of Python 3.13.0 and Jupyter notebooks, and demonstrate a Hello World program using print. Verify the Python version in the terminal.

Learn Python basics: variables and data types, operators, control flow, and functions, using a hands-on notebook approach with Markdown, printing, and loops.

Learn numpy basics for numerical computation in Python, including arrays and matrices. Create arrays with np.array, print them, and perform array operations and matrix multiplication using np.dot.

Learn how pandas enables data manipulation and analysis with data frames, loading CSV data, inspecting results, filtering rows, selecting columns, and computing descriptive statistics for AI projects.

Read a text file of numbers, square each number, and write the results to output.txt using a Python script, demonstrating basic data manipulation and file handling.

Master exploratory data analysis by loading datasets, cleaning missing values and outliers, and visualizing relationships with pandas, matplotlib, and seaborn; learn basic descriptive statistics and data preparation for modeling.

Explore how to load a csv dataset with pandas and inspect its structure, shape, columns, and data types, using a hands-on example with mba.csv from Kaggle.

Identify and handle missing data in a real-world dataset by loading mba.csv with pandas, inspecting null values, and applying drop or fill techniques such as mean imputation.

Explore data transformation and feature engineering by applying min-max scaling and one-hot encoding to prepare numerical and categorical features for machine learning models.

visualize data with matplotlib and seaborn by plotting age versus income, depicting gender distribution with a bar chart, and using seaborn for pair plots and correlation heat maps.

Learn basic descriptive statistics, including mean, median, mode, and standard deviation, and use data.describe to reveal distribution, central tendency, spread, min, max, and percentiles.

Perform exploratory data analysis on the Titanic dataset using pandas to clean missing values, compute survival rates by gender and class, and visualize results with seaborn and matplotlib.

Explore basic machine learning concepts, differentiate supervised from unsupervised learning, and build a simple linear regression model with scikit-learn using a train-test split to evaluate mean squared error and R-squared.

Discover what machine learning is, how systems learn from data, and explore supervised and unsupervised types, including linear regression for predicting continuous targets.

Learn supervised learning and dataset preparation by loading a house prices dataset, selecting features such as rooms, size, and age, and performing a train-test split with 80/20.

Build a linear regression model to predict house price from input features, weights, and a bias term. Use scikit-learn's linear regression, train on X_train and y_train, then predict X_test.

Evaluate regression models by computing mean squared error and r squared with scikit-learn, and interpret the results to assess model performance and variance explained.

Learn why feature scaling matters for gradient-based models and how to apply scikit-learn's standard scaler to standardize data, then explore ridge and lasso regularization to curb overfitting.

Predict house prices with linear regression using the California housing data. Load, explore, prepare features, train-test split, and evaluate with MSE and R2, then interpret model coefficients.

Learn classification in supervised learning by building a logistic regression model to detect spam. Measure accuracy, recall, f1 score, and confusion matrix, visualize decision boundary, and apply to real-world data.

Understand classification by predicting a category from features, contrasting it with regression. Explore binary and multi-class problems such as email spam and image labeling.

Explore logistic regression, a supervised binary classifier using the sigmoid function with z = w x + b to predict class probability, via a spam detection dataset hands-on exercise.

Load and prepare the data set, split into training and testing sets, train a logistic regression model with scikit-learn, and make predictions to classify test emails as spam.

Evaluate the classification model using confusion matrix, accuracy, precision, recall, and F1 score with a spam example, detailing true positives, true negatives, false positives, and false negatives.

Visualizing the decision boundary with a 2D feature set, using a mesh grid and contour plot to show model predictions and test points.

Train a logistic regression classifier to detect spam emails using word frequency and email length, then evaluate with accuracy, precision, recall, and F1 score, and visualize confusion matrix with Seaborn.

Explore neural networks and deep learning. Build a simple feedforward neural network with activation functions and layers, train via backpropagation to classify handwritten digits on the Minst dataset using TensorFlow.

Explore how a neural network mimics human brain through input, hidden, output layers, with weights, biases, and activation functions like ReLU, sigmoid, and softmax, using back propagation to reduce loss.

Explore deep learning frameworks with TensorFlow and PyTorch, highlighting TensorFlow's beginner-friendly, high-level design for production, and PyTorch's dynamic, pythonic flexibility for research.

Explore the mNIST dataset: 28 by 28 pixel grayscale images of handwritten digits 0–9, with 60,000 training and 10,000 test images for deep learning and image classification.

Preprocess mnist data by reshaping to 28x28x1 and normalizing, build a 128-neuron feedforward network with a flattened input and softmax output, then train five epochs with batch size 128.

Evaluate the neural network using test loss and test accuracy, then generate predictions on x_test, visualize the first image with its predicted label, and plot the results.

Explore activation functions like ReLU and softmax in a hands-on ai development bootcamp, showing how ReLU introduces non-linearity and how softmax converts logits to probabilities for multi-class classification.

Build and train a neural network to classify handwritten digits from the MNIST dataset, using a hidden ReLU layer and a softmax output, with preprocessing, training, evaluation, and prediction visualization.

Learn NLP and text classification by building a sentiment analysis model with a pre-trained Hugging Face model, exploring transfer learning. Apply tokenization, stemming, vectorization, and evaluate with accuracy and F1.

Explore how natural language processing enables machines to understand and manipulate human language, powering chatbots, sentiment analysis, machine translation, speech recognition, and tasks like text classification and named entity recognition.

Learn to build a sentiment analysis model that classifies movie reviews as positive or negative using a pre-trained model, with hands-on practice applying it to customer feedback and social media.

Explore text preprocessing through tokenization, stemming and lemmatization, and vectorization using tf-idf, with practical Python examples using NLTK and sklearn to convert text into numeric features.

Explore pre-trained NLP models from Hugging Face, install transformers, run sentiment analysis with a Distilbert pipeline, and fine-tune models using training arguments, a trainer, and custom datasets.

Load the imdb reviews dataset and preprocess with tokenization and padding, then build and train a simple lstm sentiment model in TensorFlow Keras.

Assess model performance using accuracy, precision, recall, and F1, then visualize results with a confusion matrix and a seaborn heatmap for clear interpretation.

Explore sentiment analysis with pre-trained NLP models using Hugging Face and TensorFlow to tokenize and pad IMDb reviews, train models, and evaluate accuracy, precision, recall, F1, and confusion matrices.

Deploy a machine learning model as a Flask web service, building an end-to-end app that returns predictions and exposes a RESTful API on Heroku.

Install flask with pip and build a basic Python web app to deploy machine learning models as a web service, define routes, and run the server on localhost.

Create a web interface for your model by building a prediction function, using logistic regression on IMDb data, and deploying a Flask form with a /predict route.

Deploy a Flask app to Heroku by setting up the Heroku CLI, creating a procfile and requirements.txt, and pushing your code to test the live deployment.

Test the web service API and edge cases using form data, Postman, curl, and live deployments to verify sentiment predictions and handle errors.

Build a basic calculator in Python that handles add, subtract, multiply, and divide operations via a command-line interface, with functions, input prompts, and a division-by-zero check.

Build an image classifier for handwritten digits using Keras and TensorFlow with the mNIST dataset. Normalize and reshape to 28x28x1, one-hot encode labels, construct a CNN, train, evaluate, and predict.

Build a rule-based Python chatbot that replies with predefined answers based on keyword patterns, using a responses dictionary, regex matching with re, and a loop that exits on bye.

Build a spam email detector with scikit-learn using text preprocessing and logistic regression, using Kaggle data, and evaluate with accuracy, precision, recall, F1, and visualize the confusion matrix.

Load the HAR dataset from UCI, train a random forest on smartphone accelerometer and gyroscope features to classify activities, and evaluate with accuracy, precision, recall, and F1.

Learn sentiment analysis on text data with NLTK, preprocess with stopwords, build features, train a Naive Bayes classifier on movie reviews, evaluate accuracy, and test new inputs.

Build a movie recommendation system using cosine similarity and a tf-idf vectorizer to compare genre vectors, using pandas to manage movie data and generate ranked suggestions.

Train a linear regression model on the California housing dataset to predict median house value. Evaluate performance with mean squared error and R2, and inspect model coefficients and intercept.

Build a weather forecasting model from historical data with linear regression, using temperature, humidity, wind speed, and precipitation to predict the next day's temperature, and evaluate with MSE and R2.

Build a basic neural network from scratch in Python using numpy, with a sigmoid activation and mean squared error loss, including forward and backward passes and xor training.

Use linear regression to predict stock prices from historical data. Leverage pandas, sklearn, and matplotlib to train, evaluate with mean squared error and R2, and visualize actual versus predicted prices.

Train a logistic regression model on the Pima Indians diabetes dataset to predict diabetes from features like glucose, blood pressure, BMI, and evaluate with accuracy, confusion matrix, and classification report.

Build a dog versus cat classifier using a convolutional neural network and data augmentation on the Kaggle Dogs vs Cats dataset, training and evaluating with CNN layers and image generators.

Develop a tic-tac-toe AI with the minimax algorithm in Python, implementing a 1D nine-slot board, winner and draw checks, and a recursive routine to select optimal moves.

Build a credit card fraud detection model with scikit-learn using a Kaggle dataset; train/test split, standardization, and a random forest classifier, then evaluate accuracy, precision, and the confusion matrix.

Train a decision tree on the iris dataset to classify iris species using sepal and petal measurements, then evaluate with accuracy, confusion matrix, and classification report, and visualize the tree.

Build a simple personal assistant in Python that listens to voice commands, recognizes speech with the speech recognition library, and speaks responses using pyttsx3, performing web searches and time queries.

Master frequency-based extractive text summarization with NLTK by filtering stopwords, tokenizing sentences and words, scoring sentences by word frequency, and selecting top sentences to form a summary.

Develop a fake product review detection model with NLP, using TF-IDF vectorization and logistic regression to classify genuine versus fake reviews, evaluated with accuracy and a classification report.

Learn to detect emotion in text with the NLTK Vader sentiment analyzer, using polarity and compound scores to label text as positive, negative, neutral, or joy, sadness, or anger.

Develop a book recommendation system using item-based collaborative filtering and cosine similarity on a user-book matrix built from a sample dataset with pandas.

Learn to build a car price prediction model with a random forest regressor, using features like make, model, year, and mileage, and evaluate with mean squared error and R2 score.

Build a fake news detection model using Naive Bayes and a Kaggle dataset, applying tf-idf vectorization, train-test split, and evaluating with accuracy and a classification report.

Create a resume scanner that uses TF-IDF keyword extraction and cosine similarity to match candidates to job requirements, using sample resumes and a job description.

Develop a logistic regression churn-prediction model from a customer dataset with tenure, usage, and demographics, then apply train-test split and evaluate with accuracy and a classification report.

Learn to perform named entity recognition with spaCy by extracting and visualizing entities such as people, organizations, locations, dates, and monetary values, then save results to a csv for analysis.

Predict employee attrition using XGBoost on the Kaggle IBM HR analytics dataset, preprocess data, encode categoricals, and evaluate with accuracy, confusion matrix, and classification report.

Predict heart disease risk from patient health data using supervised learning with logistic regression and random forest, including data loading, preprocessing, train-test split, evaluation, and visualization.

Build a movie rating predictor with collaborative filtering and matrix factorization, specifically singular value decomposition, using the Movielens dataset. Train test split and RMSE evaluation measure model performance.

Develop an automatic essay grading system using bert to score essays from the asap dataset, leveraging kaggle data, with preprocessing in pandas and torch and evaluation via mse and r2.