What you'll learn

• Introduction Class
• Introduction to Python
• Programming Elements ( Keywords & Variables, Data types, Numbers & Strings, Operators in python, Hands-on implementation.
• Conditions & Loops ( indentation & scopes, if, else & Elif blocks, introduction to loops, For & While loops, Break & Continue statements)
• Data Structures ( Lists, Tuples, Sets Dictionaries, CRUD operations on data structures, Building Rock Paper Scissor with console )
• Functions & Exception Handling ( introduction to functions, Positional & keyword arguments, Return statements, Try, catch & finally block )
• Add-on topics ( File handling - i/o, Numpy arrays, Case studies )
• Introduction(Understanding prediction system, Machine Learning v/s Deep Learning, Forms of ML, Supervised & Unsupervised, Regression & Classification )
• Linear regression ( Sklearn Library, Prediction pipeline, Architecture of Linear Regression, Best fit line & prediction )
• Logistic Regression (Regression v/s Classification, Logistic Regression's architecture, Sigmoid functions, Binary v/s Multiclass data )
• Evaluation metrics (Loss functions - RMSE, MSE, MAE, Accuracy - pros & cons,Confusion metrics, Precision, Recall & Fl Score )
• KNN & SVM ( Hyperplanes & Support Vectors, Architecture of SVM Classifiers, K-Nearest Neighbors classifiers, Accuracy comparisons )
• Decision tree & Ensemble Learning ( Tree-based models, Bagging & Boosting, Concept of Ensemble models, Random Forest Classifier )
• Problem Statements ( Wine Quality Prediction, Diabetes prediction, House price prediction, Titanic dataset )
• Unsupervised Learning – 1 ( Need of unsupervised learning, K-means clustering, Training K-means )
• Unsupervised Learning-II ( Mean shift clustering, K-means v/s Mean Shift clustering, Industrial use cases of unsupervised learning)
• Add-on topics ( Hyperparameter tuning, Grid Search CV & Randomized Search CV, Best Estimators & Best params )