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)CopyCopyCopy
- 1.2Variables and data types (int, float, string, boolean)CopyCopyCopy
- 1.3Basic operations and expressionsCopyCopyCopy
- 1.4Input/output operationsCopyCopyCopy
- 1.5Practice: Simple calculator, temperature converterCopyCopyCopy
- 1.6Control StructuresCopyCopyCopy
- 1.7Conditional statements (if, elif, else)CopyCopyCopy
- 1.8Loops (for, while)CopyCopyCopy
- 1.9Loop control (break, continue)CopyCopyCopy
- 1.10List comprehensionsCopyCopyCopy
- 1.11Practice: Guess the number game, prime number checkerCopyCopyCopy
- 1.12Data StructuresCopyCopyCopy
- 1.13Lists and list operationsCopyCopyCopy
- 1.14Tuples and their immutabilityCopyCopyCopy
- 1.15Dictionaries and dictionary operationsCopyCopyCopy
- 1.16Sets and set operationsCopyCopyCopy
- 1.17Practice: Contact book app, word frequency counterCopyCopyCopy
- 1.18Functions and ModulesCopyCopyCopy
- 1.19Defining and calling functionsCopyCopyCopy
- 1.20Parameters and return valuesCopyCopyCopy
- 1.21Scope and namespacesCopyCopyCopy
- 1.22Lambda functionsCopyCopyCopy
- 1.23Importing and creating modulesCopyCopyCopy
- 1.24Practice: Custom math library, text analyzerCopyCopyCopy
- 1.25Object-Oriented ProgrammingCopyCopyCopy
- 1.26Classes and objectsCopyCopyCopy
- 1.27Attributes and methodsCopyCopyCopy
- 1.28Inheritance and polymorphismCopyCopyCopy
- 1.29Encapsulation and abstractionCopyCopyCopy
- 1.30Practice: Bank account system, simple inventory managementCopyCopyCopy
- 1.31Advanced Python ConceptsCopyCopyCopy
- 1.32Exception handling (try, except, finally)CopyCopyCopy
- 1.33File operations (read, write, append)CopyCopyCopy
- 1.34Regular expressionsCopyCopyCopy
- 1.35Decorators and generatorsCopyCopyCopy
- 1.36Virtual environments and package management (pip, conda)CopyCopyCopy
- 1.37Practice: Log parser, CSV data processorCopyCopyCopy
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and typesCopyCopyCopy
- 2.2Relational database fundamentalsCopyCopyCopy
- 2.3SQL basics (CREATE, INSERT, SELECT)CopyCopyCopy
- 2.4Database design principlesCopyCopyCopy
- 2.5Setting up a database (PostgreSQL/SQLite)CopyCopyCopy
- 2.6Practice: Creating a student database schemaCopyCopyCopy
- 2.7Advanced SQL OperationsCopyCopyCopy
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)CopyCopyCopy
- 2.9Filtering and sorting (WHERE, ORDER BY)CopyCopyCopy
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)CopyCopyCopy
- 2.11Grouping data (GROUP BY, HAVING)CopyCopyCopy
- 2.12Subqueries and CTEsCopyCopyCopy
- 2.13Indexes and optimizationCopyCopyCopy
- 2.14Practice: Complex queries on an e-commerce databaseCopyCopyCopy
- 2.15Database Integration with PythonCopyCopyCopy
- 2.16Connecting to databases from PythonCopyCopyCopy
- 2.17SQLAlchemy ORMCopyCopyCopy
- 2.18CRUD operations through PythonCopyCopyCopy
- 2.19Transactions and connection poolingCopyCopyCopy
- 2.20Practice: Building a data access layer for an applicationCopyCopyCopy
- 2.21NumPyCopyCopyCopy
- 2.22NumPy FundamentalsCopyCopyCopy
- 2.23Arrays and array creationCopyCopyCopy
- 2.24Array indexing and slicingCopyCopyCopy
- 2.25Array operations and broadcastingCopyCopyCopy
- 2.26Universal functions (ufuncs)CopyCopyCopy
- 2.27Practice: Matrix operations, image processing basicsCopyCopyCopy
- 2.28Advanced NumPyCopyCopyCopy
- 2.29Reshaping and stacking arraysCopyCopyCopy
- 2.30Broadcasting rulesCopyCopyCopy
- 2.31Vectorized operationsCopyCopyCopy
- 2.32Random number generationCopyCopyCopy
- 2.33Linear algebra operationsCopyCopyCopy
- 2.34Practice: Implementing simple ML algorithms with NumPyCopyCopyCopy
- 2.35PandasCopyCopyCopy
- 2.36Pandas FundamentalsCopyCopyCopy
- 2.37Series and DataFrame objectsCopyCopyCopy
- 2.38Reading/writing data (CSV, Excel, SQL)CopyCopyCopy
- 2.39Indexing and selection (loc, iloc)CopyCopyCopy
- 2.40Handling missing dataCopyCopyCopy
- 2.41Practice: Data cleaning for a messy datasetCopyCopyCopy
- 2.42Data Manipulation with PandasCopyCopyCopy
- 2.43Data transformation (apply, map)CopyCopyCopy
- 2.44Merging, joining, and concatenatingCopyCopyCopy
- 2.45Grouping and aggregationCopyCopyCopy
- 2.46Pivot tables and cross-tabulationCopyCopyCopy
- 2.47Practice: Customer purchase analysisCopyCopyCopy
- 2.48Time Series Analysis with PandasCopyCopyCopy
- 2.49Date/time functionalityCopyCopyCopy
- 2.50Resampling and frequency conversionCopyCopyCopy
- 2.51Rolling window calculationsCopyCopyCopy
- 2.52Time zone handlingCopyCopyCopy
- 2.53Practice: Stock market data analysisCopyCopyCopy
- 2.54Data VisualizationCopyCopyCopy
- 2.55Matplotlib FundamentalsCopyCopyCopy
- 2.56Figure and Axes objectsCopyCopyCopy
- 2.57Line plots, scatter plots, bar chartsCopyCopyCopy
- 2.58Customizing plots (colors, labels, legends)CopyCopyCopy
- 2.59Saving and displaying plotsCopyCopyCopy
- 2.60Practice: Visualizing economic indicatorsCopyCopyCopy
- 2.61Advanced MatplotlibCopyCopyCopy
- 2.62Subplots and layoutsCopyCopyCopy
- 2.633D plottingCopyCopyCopy
- 2.64AnimationsCopyCopyCopy
- 2.65Custom visualizationsCopyCopyCopy
- 2.66Practice: Creating a dashboard of COVID-19 dataCopyCopyCopy
- 2.67SeabornCopyCopyCopy
- 2.68Statistical visualizationsCopyCopyCopy
- 2.69Distribution plots (histograms, KDE)CopyCopyCopy
- 2.70Categorical plots (box plots, violin plots)CopyCopyCopy
- 2.71Regression plotsCopyCopyCopy
- 2.72Customizing Seaborn plotsCopyCopyCopy
- 2.73Practice: Analyzing and visualizing survey dataCopyCopyCopy
- 2.74PlotlyCopyCopyCopy
- 2.75Interactive visualizationsCopyCopyCopy
- 2.76Plotly Express basicsCopyCopyCopy
- 2.77Advanced Plotly graphsCopyCopyCopy
- 2.78Dashboards with DashCopyCopyCopy
- 2.79Embedding visualizations in web applicationsCopyCopyCopy
- 2.80Practice: Building an interactive stock market dashboardCopyCopyCopy
- 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 FundamentalsCopyCopyCopy
- 3.2Introduction to Machine LearningCopyCopyCopy
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)CopyCopyCopy
- 3.4The ML workflowCopyCopyCopy
- 3.5Training and testing dataCopyCopyCopy
- 3.6Model evaluation basicsCopyCopyCopy
- 3.7Feature engineering overviewCopyCopyCopy
- 3.8Practice: Implementing a simple linear regression from scratchCopyCopyCopy
- 3.9Scikit-learn BasicsCopyCopyCopy
- 3.10Introduction to scikit-learn APICopyCopyCopy
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)CopyCopyCopy
- 3.12Train-test splitCopyCopyCopy
- 3.13Cross-validationCopyCopyCopy
- 3.14Pipeline constructionCopyCopyCopy
- 3.15Practice: End-to-end ML workflow implementationCopyCopyCopy
- 3.16Supervised LearningCopyCopyCopy
- 3.17Linear ModelsCopyCopyCopy
- 3.18Linear regression (simple and multiple)CopyCopyCopy
- 3.19Regularization techniques (Ridge, Lasso)CopyCopyCopy
- 3.20Logistic regressionCopyCopyCopy
- 3.21Polynomial featuresCopyCopyCopy
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)CopyCopyCopy
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)CopyCopyCopy
- 3.24Practice: Credit scoring modelCopyCopyCopy
- 3.25Decision Trees and Ensemble MethodsCopyCopyCopy
- 3.26Decision tree algorithmCopyCopyCopy
- 3.27Entropy and information gainCopyCopyCopy
- 3.28Overfitting and pruningCopyCopyCopy
- 3.29Random forestsCopyCopyCopy
- 3.30Feature importanceCopyCopyCopy
- 3.31Gradient boosting (XGBoost, LightGBM)CopyCopyCopy
- 3.32Model stacking and blendingCopyCopyCopy
- 3.33Practice: Customer churn predictionCopyCopyCopy
- 3.34Support Vector MachinesCopyCopyCopy
- 3.35Linear SVMCopyCopyCopy
- 3.36Kernel trickCopyCopyCopy
- 3.37SVM hyperparametersCopyCopyCopy
- 3.38Multi-class SVMCopyCopyCopy
- 3.39Practice: Handwritten digit recognitionCopyCopyCopy
- 3.40K-Nearest NeighborsCopyCopyCopy
- 3.41Distance metricsCopyCopyCopy
- 3.42KNN for classification and regressionCopyCopyCopy
- 3.43Choosing K valueCopyCopyCopy
- 3.44KNN limitations and optimizationsCopyCopyCopy
- 3.45Practice: Image classification with KNNCopyCopyCopy
- 3.46Naive BayesCopyCopyCopy
- 3.47Bayes theoremCopyCopyCopy
- 3.48Gaussian, Multinomial, and Bernoulli Naive BayesCopyCopyCopy
- 3.49Applications in text classificationCopyCopyCopy
- 3.50Practice: Spam detectionCopyCopyCopy
- 3.51Unsupervised LearningCopyCopyCopy
- 3.52Clustering AlgorithmsCopyCopyCopy
- 3.53K-means clusteringCopyCopyCopy
- 3.54Hierarchical clusteringCopyCopyCopy
- 3.55DBSCANCopyCopyCopy
- 3.56Gaussian mixture modelsCopyCopyCopy
- 3.57Evaluating clustering performanceCopyCopyCopy
- 3.58Practice: Customer segmentationCopyCopyCopy
- 3.59Dimensionality ReductionCopyCopyCopy
- 3.60Principal Component Analysis (PCA)CopyCopyCopy
- 3.61t-SNECopyCopyCopy
- 3.62UMAPCopyCopyCopy
- 3.63Feature selection techniquesCopyCopyCopy
- 3.64Practice: Image compression, visualization of high-dimensional dataCopyCopyCopy
- 3.65Anomaly DetectionCopyCopyCopy
- 3.66Statistical methodsCopyCopyCopy
- 3.67Isolation ForestCopyCopyCopy
- 3.68One-class SVMCopyCopyCopy
- 3.69Autoencoders for anomaly detectionCopyCopyCopy
- 3.70Practice: Fraud detectionCopyCopyCopy
- 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: PythonCopyCopyCopy
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChainCopyCopyCopy
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)CopyCopyCopy
- 6.4Visualization: Matplotlib, Seaborn,PlotlyCopyCopyCopy
- 6.5Deployment: FastAPI, Flask,Streamlit)CopyCopyCopy