Python Programming & Exploratory Data Analysis Class 1-48 Supervised Machine Learning: Regression & Classification Class 49-96 Unsupervised Machine Learning: Clustering & Modelling Class 97-144 Build AI-ready applications in the field of SpaceTech, BioTech and Automobile. Inferential Statistics Students will make probabilistic estimation Hypothesis Testing Data Density Central Limit Theorem Sampling Distribution Object Oriented Programming Student will learn to create their own Python libraries using OOP Abstraction Inheritance Polymorphism Encapsulation Machine Learning: Regression Student will learn to forecast events using data. XGBoost Regression Linear Regression Support Vector Regression Random Forest Regression Machine Learning: Classification Student will deploy binary classification & multi-class classification models XGBoost Classification Random Forest Classification Logistic Regression Support Vector Classification Heart Disease Estimate the likely factors causing heart disease Stellar Classification Create library to classify stars. Eclipse Prediction Predict the next solar eclipse. Survival From Hepatitis Build a model to measure likeness of survival of hepatitis patients. Conditionals Functions Variables Loops Python Programming Students will learn basic programming constructs and use of libraries. Graphs NumPy arrays Panda Dataframes Plots Data Processing & Visualization Students will learn to process data into a meaningful information. Lists Tuples Strings Data Structures with Python Students will learn how to build Python applications with back-end. Coefficients Normal Distribution Random Variables Conditional Probability Correlation & Probability Students will take a plunge into feature variables and probabilistic estimations. Mind Reader Game An intelligent game which can predict the next number. Search Exoplanets Detect stars that have planets using data. Guess a Word Guess a word based on the jumbled letters and its part of speech. Air Quality Analysis Predict the most likely temperature due to change in air pollution. K-Means Clustering Students will learn to find out the right target variable. Inertia Dunn Index Algorithm Thinking Cluster Tendency Hierarchical Clustering Students will learn to cluster/group data based on features. Linkages Dendograms Means & Centroid. Cluster Analysis Principal Component Analysis Students will learn to identify principal components in the dataset. Matrices Covariance Basis Transformation Scree Plots Model Selection Students will learn to choose the best prediction model based on their accuracy Cross-Validation Resampling Probabilistic Measures Hyperparameter Tuning Cancer Detection Detect the causes of cancers through clusters Human Gene Insights Detect the gene structure Customer Segmentation Predict the most likely visitors in a mall through clusters Titanic Survivors Select the most accurate ML mode 4 12 8 3 11 7 2 10 6 1 9 5 Dictionaries APPLIED TECH CURRICULUM (GRADES 10-12+) AQI PM 2.5 No. of vistiors Time E C L I P S E B A C K G R O U N D