Python, Machine Learning and IOT Bootcamp

Beginner python essentials:

• Flowcharts, Data Types, Operators

• Conditional Statements & Loops

• Functions & Recursion

• Strings

• In-built Data Structures - List, Tuple, Dictionary, Set

Intermediate python essentials:

• Lambda Functions, List Comprehension, Functional Programming, Decorator, Args, Kwargs

• Object Oriented Programming

• Exception Handling, Modules, Package, Library, Built-in Modules in Python

• Basic DSA & Problem Solving

  • Time complexity, List, 2D List, Bit Manipulation, Strings, Searching, Sorting

Python for Data science

• • • • • Numpy and Pandas

        • Data visualisation using matplotlib and seaborn

Math for Data science

• • • • • Linear algebra, Derivatives and Partial Derivatives

        • Probability and statistics

Supervised Learning

• • • • • Linear Regression, Gradient Descent, Multicollinearity, VIF, R-square, Heteroscedasticity, Sklearn, Polynomial Regression, Bias-Variance trade-off, Regularisation

        • Logistic Regression, Squashing function, AUC. ROC, Precision-Recall Curve, Confusion matrix, Specificity

        • KNN, Decision Trees, Ensemble learning, Bagging, Boosting

        • Support Vector Machine

        • Bayesian Machine Learning

Unsupervised Learning

• KMeans, Customer Segmentation, Hierarchical, DBSCAN, Anomaly Detection, Local Outlier Factor, Isolation Forest, Dimensionality Reduction, PCA, t-SNE, GMM, Information Theory, Expectation Maximisation

Recommender Systems

• Collaborative/Content filtering

Neural Networks

• Neural Networks - MLP, Backpropagation, Hyperparameter Tuning, Practical Aspects of DL

• Keras, Tensorflow, Pytorch

Deployment

• How to package and deploy a machine learning model as a web application?

  • Serving machine learning models using FastAPI and Python on Heroku

  • Containerizing machine learning based applications using Docker