Curriculum
- 6 Sections
- 202 Lessons
- 22 Weeks
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- Module 1: Python FundamentalsIntroduction to Python37
- 1.1Setting up development environment (Anaconda, Jupyter, VS Code)Copy
- 1.2Variables and data types (int, float, string, boolean)Copy
- 1.3Basic operations and expressionsCopy
- 1.4Input/output operationsCopy
- 1.5Practice: Simple calculator, temperature converterCopy
- 1.6Control StructuresCopy
- 1.7Conditional statements (if, elif, else)Copy
- 1.8Loops (for, while)Copy
- 1.9Loop control (break, continue)Copy
- 1.10List comprehensionsCopy
- 1.11Practice: Guess the number game, prime number checkerCopy
- 1.12Data StructuresCopy
- 1.13Lists and list operationsCopy
- 1.14Tuples and their immutabilityCopy
- 1.15Dictionaries and dictionary operationsCopy
- 1.16Sets and set operationsCopy
- 1.17Practice: Contact book app, word frequency counterCopy
- 1.18Functions and ModulesCopy
- 1.19Defining and calling functionsCopy
- 1.20Parameters and return valuesCopy
- 1.21Scope and namespacesCopy
- 1.22Lambda functionsCopy
- 1.23Importing and creating modulesCopy
- 1.24Practice: Custom math library, text analyzerCopy
- 1.25Object-Oriented ProgrammingCopy
- 1.26Classes and objectsCopy
- 1.27Attributes and methodsCopy
- 1.28Inheritance and polymorphismCopy
- 1.29Encapsulation and abstractionCopy
- 1.30Practice: Bank account system, simple inventory managementCopy
- 1.31Advanced Python ConceptsCopy
- 1.32Exception handling (try, except, finally)Copy
- 1.33File operations (read, write, append)Copy
- 1.34Regular expressionsCopy
- 1.35Decorators and generatorsCopy
- 1.36Virtual environments and package management (pip, conda)Copy
- 1.37Practice: Log parser, CSV data processorCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopy
- 2.2Relational database fundamentalsCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)Copy
- 2.4Database design principlesCopy
- 2.5Setting up a database (PostgreSQL/SQLite)Copy
- 2.6Practice: Creating a student database schemaCopy
- 2.7Advanced SQL OperationsCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)Copy
- 2.9Filtering and sorting (WHERE, ORDER BY)Copy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)Copy
- 2.11Grouping data (GROUP BY, HAVING)Copy
- 2.12Subqueries and CTEsCopy
- 2.13Indexes and optimizationCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopy
- 2.15Database Integration with PythonCopy
- 2.16Connecting to databases from PythonCopy
- 2.17SQLAlchemy ORMCopy
- 2.18CRUD operations through PythonCopy
- 2.19Transactions and connection poolingCopy
- 2.20Practice: Building a data access layer for an applicationCopy
- 2.21NumPyCopy
- 2.22NumPy FundamentalsCopy
- 2.23Arrays and array creationCopy
- 2.24Array indexing and slicingCopy
- 2.25Array operations and broadcastingCopy
- 2.26Universal functions (ufuncs)Copy
- 2.27Practice: Matrix operations, image processing basicsCopy
- 2.28Advanced NumPyCopy
- 2.29Reshaping and stacking arraysCopy
- 2.30Broadcasting rulesCopy
- 2.31Vectorized operationsCopy
- 2.32Random number generationCopy
- 2.33Linear algebra operationsCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopy
- 2.35PandasCopy
- 2.36Pandas FundamentalsCopy
- 2.37Series and DataFrame objectsCopy
- 2.38Reading/writing data (CSV, Excel, SQL)Copy
- 2.39Indexing and selection (loc, iloc)Copy
- 2.40Handling missing dataCopy
- 2.41Practice: Data cleaning for a messy datasetCopy
- 2.42Data Manipulation with PandasCopy
- 2.43Data transformation (apply, map)Copy
- 2.44Merging, joining, and concatenatingCopy
- 2.45Grouping and aggregationCopy
- 2.46Pivot tables and cross-tabulationCopy
- 2.47Practice: Customer purchase analysisCopy
- 2.48Time Series Analysis with PandasCopy
- 2.49Date/time functionalityCopy
- 2.50Resampling and frequency conversionCopy
- 2.51Rolling window calculationsCopy
- 2.52Time zone handlingCopy
- 2.53Practice: Stock market data analysisCopy
- 2.54Data VisualizationCopy
- 2.55Matplotlib FundamentalsCopy
- 2.56Figure and Axes objectsCopy
- 2.57Line plots, scatter plots, bar chartsCopy
- 2.58Customizing plots (colors, labels, legends)Copy
- 2.59Saving and displaying plotsCopy
- 2.60Practice: Visualizing economic indicatorsCopy
- 2.61Advanced MatplotlibCopy
- 2.62Subplots and layoutsCopy
- 2.633D plottingCopy
- 2.64AnimationsCopy
- 2.65Custom visualizationsCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopy
- 2.67SeabornCopy
- 2.68Statistical visualizationsCopy
- 2.69Distribution plots (histograms, KDE)Copy
- 2.70Categorical plots (box plots, violin plots)Copy
- 2.71Regression plotsCopy
- 2.72Customizing Seaborn plotsCopy
- 2.73Practice: Analyzing and visualizing survey dataCopy
- 2.74PlotlyCopy
- 2.75Interactive visualizationsCopy
- 2.76Plotly Express basicsCopy
- 2.77Advanced Plotly graphsCopy
- 2.78Dashboards with DashCopy
- 2.79Embedding visualizations in web applicationsCopy
- 2.80Practice: Building an interactive stock market dashboardCopy
- Module 3: ML Statistics for BeginnersIntroduction: Role of statistics in ML, descriptive vs. inferential stats. Descriptive Statistics: Mean, median, variance, skewness, kurtosis. Probability Basics: Bayes' theorem, normal, binomial, Poisson distributions. Inferential Statistics: Sampling, hypothesis testing (Z-test, T-test, Chi-square). Correlation & Regression: Pearson correlation, linear regression, R² score. Hands-on in Python: NumPy, Pandas, SciPy, Seaborn, and statsmodels.70
- 3.1Machine Learning FundamentalsCopy
- 3.2Introduction to Machine LearningCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)Copy
- 3.4The ML workflowCopy
- 3.5Training and testing dataCopy
- 3.6Model evaluation basicsCopy
- 3.7Feature engineering overviewCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopy
- 3.9Scikit-learn BasicsCopy
- 3.10Introduction to scikit-learn APICopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)Copy
- 3.12Train-test splitCopy
- 3.13Cross-validationCopy
- 3.14Pipeline constructionCopy
- 3.15Practice: End-to-end ML workflow implementationCopy
- 3.16Supervised LearningCopy
- 3.17Linear ModelsCopy
- 3.18Linear regression (simple and multiple)Copy
- 3.19Regularization techniques (Ridge, Lasso)Copy
- 3.20Logistic regressionCopy
- 3.21Polynomial featuresCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)Copy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)Copy
- 3.24Practice: Credit scoring modelCopy
- 3.25Decision Trees and Ensemble MethodsCopy
- 3.26Decision tree algorithmCopy
- 3.27Entropy and information gainCopy
- 3.28Overfitting and pruningCopy
- 3.29Random forestsCopy
- 3.30Feature importanceCopy
- 3.31Gradient boosting (XGBoost, LightGBM)Copy
- 3.32Model stacking and blendingCopy
- 3.33Practice: Customer churn predictionCopy
- 3.34Support Vector MachinesCopy
- 3.35Linear SVMCopy
- 3.36Kernel trickCopy
- 3.37SVM hyperparametersCopy
- 3.38Multi-class SVMCopy
- 3.39Practice: Handwritten digit recognitionCopy
- 3.40K-Nearest NeighborsCopy
- 3.41Distance metricsCopy
- 3.42KNN for classification and regressionCopy
- 3.43Choosing K valueCopy
- 3.44KNN limitations and optimizationsCopy
- 3.45Practice: Image classification with KNNCopy
- 3.46Naive BayesCopy
- 3.47Bayes theoremCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopy
- 3.49Applications in text classificationCopy
- 3.50Practice: Spam detectionCopy
- 3.51Unsupervised LearningCopy
- 3.52Clustering AlgorithmsCopy
- 3.53K-means clusteringCopy
- 3.54Hierarchical clusteringCopy
- 3.55DBSCANCopy
- 3.56Gaussian mixture modelsCopy
- 3.57Evaluating clustering performanceCopy
- 3.58Practice: Customer segmentationCopy
- 3.59Dimensionality ReductionCopy
- 3.60Principal Component Analysis (PCA)Copy
- 3.61t-SNECopy
- 3.62UMAPCopy
- 3.63Feature selection techniquesCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopy
- 3.65Anomaly DetectionCopy
- 3.66Statistical methodsCopy
- 3.67Isolation ForestCopy
- 3.68One-class SVMCopy
- 3.69Autoencoders for anomaly detectionCopy
- 3.70Practice: Fraud detectionCopy
- Module 5: ML Model Deployment with Flask, FastAPI, and Streamlit6
- Module 6: Final Capstone ProjectDevelop an end-to-end solution that integrates multiple technologies:4
- Tools & Technologies Covered5
- 6.1Languages: PythonCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)Copy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)Copy