Machine Learning, AI & Neural Networks: A Complete Course

Detect and clean data with Python using pandas and NumPy, performing missing value handling, mean and mode imputation, and replacement of inconsistencies for robust analysis.

Apply min-max, robust, and standardization scaling in python to normalize height and weight, reduce outliers, and prepare data for ML models like kNN and SVM.

Explore feature selection and dimensionality reduction in Python, using variance threshold and k-best methods (ANOVA F-test), then apply principal component analysis to iris data and visualize with plots.

Explore simple and multiple linear regression in Python using sklearn, numpy, and matplotlib to fit models, predict outcomes, visualize fits, and interpret coefficients and intercepts on Iris and seaborn datasets.

Master polynomial regression as an extension of linear regression by adding polynomial features of degree two to capture non-linear relationships, fit with linear regression, and visualize a better curved fit.

Explore how support vector machines classify data by finding the optimal hyperplane with linear and non-linear kernels. See python examples with iris data and a classification report.

Explore logistic regression and decision trees for classification in Python, using scikit-learn with binary and iris datasets, train-test splits, and accuracy evaluation.

Explore ensemble learning with random forest and gradient boosting, including XGBoost and LightGBM, through practical iris and breast cancer datasets to boost accuracy and speed.

Explore unsupervised clustering methods in Python, including k-means, hierarchical clustering, and dbscan. See practical demonstrations that uncover hidden patterns in data.

Build neural networks with TensorFlow and Keras, constructing dense and convolutional models for multi-class classification, and train on MNIST and CIFAR-10 datasets.

Explore dropout and batch normalization to reduce overfitting and speed training in neural networks, with practical TensorFlow sequential model examples and concrete layer configurations.

Explore object detection and image segmentation in Python with OpenCV and Mask R-CNN, drawing bounding boxes and pixel-level segments for autonomous driving and medical imaging analysis.

Learn to work with sequential time series data in Python by plotting daily data with pandas and matplotlib, identify trends and seasonal patterns, and perform seasonal decomposition.

Explore natural language processing with Python, focusing on tokenization using NLTK and word embeddings. Learn stop words removal and word vectors for semantic meaning.

Explore sentiment analysis and chatbot development in natural language processing by building Python examples with TextBlob and NLTK, analyzing polarity and subjectivity, and creating a simple chatbot.

Explore variational autoencoders (VAE), a probabilistic generative model that learns a latent space distribution to generate new realistic data, with encoder, sampling layer, and decoder in TensorFlow and Keras.

Deploy machine learning models using Flask and FastAPI to build simple API endpoints for model serving, including post methods, JSON input, and output results.

Explore model optimization for production with python, applying model compression using joblib, interface and pipeline optimization, and a random forest workflow on iris data for scalable deployment.

Explore ai ethics and bias in machine learning by showing how imbalanced data reinforces bias, and how to detect and mitigate it using code, fairness metrics, and fairlearn.