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)CopyCopy
- 1.2Variables and data types (int, float, string, boolean)CopyCopy
- 1.3Basic operations and expressionsCopyCopy
- 1.4Input/output operationsCopyCopy
- 1.5Practice: Simple calculator, temperature converterCopyCopy
- 1.6Control StructuresCopyCopy
- 1.7Conditional statements (if, elif, else)CopyCopy
- 1.8Loops (for, while)CopyCopy
- 1.9Loop control (break, continue)CopyCopy
- 1.10List comprehensionsCopyCopy
- 1.11Practice: Guess the number game, prime number checkerCopyCopy
- 1.12Data StructuresCopyCopy
- 1.13Lists and list operationsCopyCopy
- 1.14Tuples and their immutabilityCopyCopy
- 1.15Dictionaries and dictionary operationsCopyCopy
- 1.16Sets and set operationsCopyCopy
- 1.17Practice: Contact book app, word frequency counterCopyCopy
- 1.18Functions and ModulesCopyCopy
- 1.19Defining and calling functionsCopyCopy
- 1.20Parameters and return valuesCopyCopy
- 1.21Scope and namespacesCopyCopy
- 1.22Lambda functionsCopyCopy
- 1.23Importing and creating modulesCopyCopy
- 1.24Practice: Custom math library, text analyzerCopyCopy
- 1.25Object-Oriented ProgrammingCopyCopy
- 1.26Classes and objectsCopyCopy
- 1.27Attributes and methodsCopyCopy
- 1.28Inheritance and polymorphismCopyCopy
- 1.29Encapsulation and abstractionCopyCopy
- 1.30Practice: Bank account system, simple inventory managementCopyCopy
- 1.31Advanced Python ConceptsCopyCopy
- 1.32Exception handling (try, except, finally)CopyCopy
- 1.33File operations (read, write, append)CopyCopy
- 1.34Regular expressionsCopyCopy
- 1.35Decorators and generatorsCopyCopy
- 1.36Virtual environments and package management (pip, conda)CopyCopy
- 1.37Practice: Log parser, CSV data processorCopyCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopyCopy
- 2.2Relational database fundamentalsCopyCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)CopyCopy
- 2.4Database design principlesCopyCopy
- 2.5Setting up a database (PostgreSQL/SQLite)CopyCopy
- 2.6Practice: Creating a student database schemaCopyCopy
- 2.7Advanced SQL OperationsCopyCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)CopyCopy
- 2.9Filtering and sorting (WHERE, ORDER BY)CopyCopy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)CopyCopy
- 2.11Grouping data (GROUP BY, HAVING)CopyCopy
- 2.12Subqueries and CTEsCopyCopy
- 2.13Indexes and optimizationCopyCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopyCopy
- 2.15Database Integration with PythonCopyCopy
- 2.16Connecting to databases from PythonCopyCopy
- 2.17SQLAlchemy ORMCopyCopy
- 2.18CRUD operations through PythonCopyCopy
- 2.19Transactions and connection poolingCopyCopy
- 2.20Practice: Building a data access layer for an applicationCopyCopy
- 2.21NumPyCopyCopy
- 2.22NumPy FundamentalsCopyCopy
- 2.23Arrays and array creationCopyCopy
- 2.24Array indexing and slicingCopyCopy
- 2.25Array operations and broadcastingCopyCopy
- 2.26Universal functions (ufuncs)CopyCopy
- 2.27Practice: Matrix operations, image processing basicsCopyCopy
- 2.28Advanced NumPyCopyCopy
- 2.29Reshaping and stacking arraysCopyCopy
- 2.30Broadcasting rulesCopyCopy
- 2.31Vectorized operationsCopyCopy
- 2.32Random number generationCopyCopy
- 2.33Linear algebra operationsCopyCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopyCopy
- 2.35PandasCopyCopy
- 2.36Pandas FundamentalsCopyCopy
- 2.37Series and DataFrame objectsCopyCopy
- 2.38Reading/writing data (CSV, Excel, SQL)CopyCopy
- 2.39Indexing and selection (loc, iloc)CopyCopy
- 2.40Handling missing dataCopyCopy
- 2.41Practice: Data cleaning for a messy datasetCopyCopy
- 2.42Data Manipulation with PandasCopyCopy
- 2.43Data transformation (apply, map)CopyCopy
- 2.44Merging, joining, and concatenatingCopyCopy
- 2.45Grouping and aggregationCopyCopy
- 2.46Pivot tables and cross-tabulationCopyCopy
- 2.47Practice: Customer purchase analysisCopyCopy
- 2.48Time Series Analysis with PandasCopyCopy
- 2.49Date/time functionalityCopyCopy
- 2.50Resampling and frequency conversionCopyCopy
- 2.51Rolling window calculationsCopyCopy
- 2.52Time zone handlingCopyCopy
- 2.53Practice: Stock market data analysisCopyCopy
- 2.54Data VisualizationCopyCopy
- 2.55Matplotlib FundamentalsCopyCopy
- 2.56Figure and Axes objectsCopyCopy
- 2.57Line plots, scatter plots, bar chartsCopyCopy
- 2.58Customizing plots (colors, labels, legends)CopyCopy
- 2.59Saving and displaying plotsCopyCopy
- 2.60Practice: Visualizing economic indicatorsCopyCopy
- 2.61Advanced MatplotlibCopyCopy
- 2.62Subplots and layoutsCopyCopy
- 2.633D plottingCopyCopy
- 2.64AnimationsCopyCopy
- 2.65Custom visualizationsCopyCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopyCopy
- 2.67SeabornCopyCopy
- 2.68Statistical visualizationsCopyCopy
- 2.69Distribution plots (histograms, KDE)CopyCopy
- 2.70Categorical plots (box plots, violin plots)CopyCopy
- 2.71Regression plotsCopyCopy
- 2.72Customizing Seaborn plotsCopyCopy
- 2.73Practice: Analyzing and visualizing survey dataCopyCopy
- 2.74PlotlyCopyCopy
- 2.75Interactive visualizationsCopyCopy
- 2.76Plotly Express basicsCopyCopy
- 2.77Advanced Plotly graphsCopyCopy
- 2.78Dashboards with DashCopyCopy
- 2.79Embedding visualizations in web applicationsCopyCopy
- 2.80Practice: Building an interactive stock market dashboardCopyCopy
- 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 FundamentalsCopyCopy
- 3.2Introduction to Machine LearningCopyCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)CopyCopy
- 3.4The ML workflowCopyCopy
- 3.5Training and testing dataCopyCopy
- 3.6Model evaluation basicsCopyCopy
- 3.7Feature engineering overviewCopyCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopyCopy
- 3.9Scikit-learn BasicsCopyCopy
- 3.10Introduction to scikit-learn APICopyCopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)CopyCopy
- 3.12Train-test splitCopyCopy
- 3.13Cross-validationCopyCopy
- 3.14Pipeline constructionCopyCopy
- 3.15Practice: End-to-end ML workflow implementationCopyCopy
- 3.16Supervised LearningCopyCopy
- 3.17Linear ModelsCopyCopy
- 3.18Linear regression (simple and multiple)CopyCopy
- 3.19Regularization techniques (Ridge, Lasso)CopyCopy
- 3.20Logistic regressionCopyCopy
- 3.21Polynomial featuresCopyCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)CopyCopy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)CopyCopy
- 3.24Practice: Credit scoring modelCopyCopy
- 3.25Decision Trees and Ensemble MethodsCopyCopy
- 3.26Decision tree algorithmCopyCopy
- 3.27Entropy and information gainCopyCopy
- 3.28Overfitting and pruningCopyCopy
- 3.29Random forestsCopyCopy
- 3.30Feature importanceCopyCopy
- 3.31Gradient boosting (XGBoost, LightGBM)CopyCopy
- 3.32Model stacking and blendingCopyCopy
- 3.33Practice: Customer churn predictionCopyCopy
- 3.34Support Vector MachinesCopyCopy
- 3.35Linear SVMCopyCopy
- 3.36Kernel trickCopyCopy
- 3.37SVM hyperparametersCopyCopy
- 3.38Multi-class SVMCopyCopy
- 3.39Practice: Handwritten digit recognitionCopyCopy
- 3.40K-Nearest NeighborsCopyCopy
- 3.41Distance metricsCopyCopy
- 3.42KNN for classification and regressionCopyCopy
- 3.43Choosing K valueCopyCopy
- 3.44KNN limitations and optimizationsCopyCopy
- 3.45Practice: Image classification with KNNCopyCopy
- 3.46Naive BayesCopyCopy
- 3.47Bayes theoremCopyCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopyCopy
- 3.49Applications in text classificationCopyCopy
- 3.50Practice: Spam detectionCopyCopy
- 3.51Unsupervised LearningCopyCopy
- 3.52Clustering AlgorithmsCopyCopy
- 3.53K-means clusteringCopyCopy
- 3.54Hierarchical clusteringCopyCopy
- 3.55DBSCANCopyCopy
- 3.56Gaussian mixture modelsCopyCopy
- 3.57Evaluating clustering performanceCopyCopy
- 3.58Practice: Customer segmentationCopyCopy
- 3.59Dimensionality ReductionCopyCopy
- 3.60Principal Component Analysis (PCA)CopyCopy
- 3.61t-SNECopyCopy
- 3.62UMAPCopyCopy
- 3.63Feature selection techniquesCopyCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopyCopy
- 3.65Anomaly DetectionCopyCopy
- 3.66Statistical methodsCopyCopy
- 3.67Isolation ForestCopyCopy
- 3.68One-class SVMCopyCopy
- 3.69Autoencoders for anomaly detectionCopyCopy
- 3.70Practice: Fraud detectionCopyCopy
- 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: PythonCopyCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopyCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)CopyCopy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)CopyCopy