Embark on a comprehensive 10-hour tutorial series covering various Machine Learning techniques for beginners using Python, delivered in Hindi. Learn essential ML algorithms including linear regression, support vector machines, and decision trees. Gain insights into the intricacies of linear regression, understanding how it works and evaluates loss for accurate predictions using loss functions. Explore Python code implementations for linear regression and gradient descent. Dive into topics such as data collection, supervised and unsupervised learning, training and test data, multiple regression models, loss functions, mini-batch and stochastic gradient descent, classification, K-Nearest Neighbors, overfitting and underfitting, logistic regression, and more. Apply your knowledge through hands-on projects, including an end-to-end Python ML project and handwritten digit recognition using the MNIST dataset. Master essential evaluation metrics like precision, recall, confusion matrix, and F1-score to assess classifier performance effectively. Read more