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)
- 1.2Variables and data types (int, float, string, boolean)
- 1.3Basic operations and expressions
- 1.4Input/output operations
- 1.5Practice: Simple calculator, temperature converter
- 1.6Control Structures
- 1.7Conditional statements (if, elif, else)
- 1.8Loops (for, while)
- 1.9Loop control (break, continue)
- 1.10List comprehensions
- 1.11Practice: Guess the number game, prime number checker
- 1.12Data Structures
- 1.13Lists and list operations
- 1.14Tuples and their immutability
- 1.15Dictionaries and dictionary operations
- 1.16Sets and set operations
- 1.17Practice: Contact book app, word frequency counter
- 1.18Functions and Modules
- 1.19Defining and calling functions
- 1.20Parameters and return values
- 1.21Scope and namespaces
- 1.22Lambda functions
- 1.23Importing and creating modules
- 1.24Practice: Custom math library, text analyzer
- 1.25Object-Oriented Programming
- 1.26Classes and objects
- 1.27Attributes and methods
- 1.28Inheritance and polymorphism
- 1.29Encapsulation and abstraction
- 1.30Practice: Bank account system, simple inventory management
- 1.31Advanced Python Concepts
- 1.32Exception handling (try, except, finally)
- 1.33File operations (read, write, append)
- 1.34Regular expressions
- 1.35Decorators and generators
- 1.36Virtual environments and package management (pip, conda)
- 1.37Practice: Log parser, CSV data processor
- Module 2: SQL and Database FundamentalsIntroduction to Databases80
- 2.1Database concepts and types
- 2.2Relational database fundamentals
- 2.3SQL basics (CREATE, INSERT, SELECT)
- 2.4Database design principles
- 2.5Setting up a database (PostgreSQL/SQLite)
- 2.6Practice: Creating a student database schema
- 2.7Advanced SQL Operations
- 2.8JOIN operations (INNER, LEFT, RIGHT, FULL)
- 2.9Filtering and sorting (WHERE, ORDER BY)
- 2.10Aggregation functions (COUNT, SUM, AVG, MIN, MAX)
- 2.11Grouping data (GROUP BY, HAVING)
- 2.12Subqueries and CTEs
- 2.13Indexes and optimization
- 2.14Practice: Complex queries on an e-commerce database
- 2.15Database Integration with Python
- 2.16Connecting to databases from Python
- 2.17SQLAlchemy ORM
- 2.18CRUD operations through Python
- 2.19Transactions and connection pooling
- 2.20Practice: Building a data access layer for an application
- 2.21NumPy
- 2.22NumPy Fundamentals
- 2.23Arrays and array creation
- 2.24Array indexing and slicing
- 2.25Array operations and broadcasting
- 2.26Universal functions (ufuncs)
- 2.27Practice: Matrix operations, image processing basics
- 2.28Advanced NumPy
- 2.29Reshaping and stacking arrays
- 2.30Broadcasting rules
- 2.31Vectorized operations
- 2.32Random number generation
- 2.33Linear algebra operations
- 2.34Practice: Implementing simple ML algorithms with NumPy
- 2.35Pandas
- 2.36Pandas Fundamentals
- 2.37Series and DataFrame objects
- 2.38Reading/writing data (CSV, Excel, SQL)
- 2.39Indexing and selection (loc, iloc)
- 2.40Handling missing data
- 2.41Practice: Data cleaning for a messy dataset
- 2.42Data Manipulation with Pandas
- 2.43Data transformation (apply, map)
- 2.44Merging, joining, and concatenating
- 2.45Grouping and aggregation
- 2.46Pivot tables and cross-tabulation
- 2.47Practice: Customer purchase analysis
- 2.48Time Series Analysis with Pandas
- 2.49Date/time functionality
- 2.50Resampling and frequency conversion
- 2.51Rolling window calculations
- 2.52Time zone handling
- 2.53Practice: Stock market data analysis
- 2.54Data Visualization
- 2.55Matplotlib Fundamentals
- 2.56Figure and Axes objects
- 2.57Line plots, scatter plots, bar charts
- 2.58Customizing plots (colors, labels, legends)
- 2.59Saving and displaying plots
- 2.60Practice: Visualizing economic indicators
- 2.61Advanced Matplotlib
- 2.62Subplots and layouts
- 2.633D plotting
- 2.64Animations
- 2.65Custom visualizations
- 2.66Practice: Creating a dashboard of COVID-19 data
- 2.67Seaborn
- 2.68Statistical visualizations
- 2.69Distribution plots (histograms, KDE)
- 2.70Categorical plots (box plots, violin plots)
- 2.71Regression plots
- 2.72Customizing Seaborn plots
- 2.73Practice: Analyzing and visualizing survey data
- 2.74Plotly
- 2.75Interactive visualizations
- 2.76Plotly Express basics
- 2.77Advanced Plotly graphs
- 2.78Dashboards with Dash
- 2.79Embedding visualizations in web applications
- 2.80Practice: Building an interactive stock market dashboard
- 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 Fundamentals
- 3.2Introduction to Machine Learning
- 3.3Types of machine learning (supervised, unsupervised, reinforcement)
- 3.4The ML workflow
- 3.5Training and testing data
- 3.6Model evaluation basics
- 3.7Feature engineering overview
- 3.8Practice: Implementing a simple linear regression from scratch
- 3.9Scikit-learn Basics
- 3.10Introduction to scikit-learn API
- 3.11Data preprocessing (StandardScaler, MinMaxScaler)
- 3.12Train-test split
- 3.13Cross-validation
- 3.14Pipeline construction
- 3.15Practice: End-to-end ML workflow implementation
- 3.16Supervised Learning
- 3.17Linear Models
- 3.18Linear regression (simple and multiple)
- 3.19Regularization techniques (Ridge, Lasso)
- 3.20Logistic regression
- 3.21Polynomial features
- 3.22Evaluation metrics for regression (MSE, RMSE, MAE, R²)
- 3.23Evaluation metrics for classification (accuracy, precision, recall, F1)
- 3.24Practice: Credit scoring model
- 3.25Decision Trees and Ensemble Methods
- 3.26Decision tree algorithm
- 3.27Entropy and information gain
- 3.28Overfitting and pruning
- 3.29Random forests
- 3.30Feature importance
- 3.31Gradient boosting (XGBoost, LightGBM)
- 3.32Model stacking and blending
- 3.33Practice: Customer churn prediction
- 3.34Support Vector Machines
- 3.35Linear SVM
- 3.36Kernel trick
- 3.37SVM hyperparameters
- 3.38Multi-class SVM
- 3.39Practice: Handwritten digit recognition
- 3.40K-Nearest Neighbors
- 3.41Distance metrics
- 3.42KNN for classification and regression
- 3.43Choosing K value
- 3.44KNN limitations and optimizations
- 3.45Practice: Image classification with KNN
- 3.46Naive Bayes
- 3.47Bayes theorem
- 3.48Gaussian, Multinomial, and Bernoulli Naive Bayes
- 3.49Applications in text classification
- 3.50Practice: Spam detection
- 3.51Unsupervised Learning
- 3.52Clustering Algorithms
- 3.53K-means clustering
- 3.54Hierarchical clustering
- 3.55DBSCAN
- 3.56Gaussian mixture models
- 3.57Evaluating clustering performance
- 3.58Practice: Customer segmentation
- 3.59Dimensionality Reduction
- 3.60Principal Component Analysis (PCA)
- 3.61t-SNE
- 3.62UMAP
- 3.63Feature selection techniques
- 3.64Practice: Image compression, visualization of high-dimensional data
- 3.65Anomaly Detection
- 3.66Statistical methods
- 3.67Isolation Forest
- 3.68One-class SVM
- 3.69Autoencoders for anomaly detection
- 3.70Practice: Fraud detection
- 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: Python
- 6.2Libraries & Frameworks: NumPy, Pandas, Matplotlib, Seaborn, NLTK, TensorFlow, PyTorch, Scikit-learn, LangChain
- 6.3Databases: SQLite, MySQL, Vector databases (ChromaDB, FAISS, Pinecone), Graph databases (Neo4j)
- 6.4Visualization: Matplotlib, Seaborn,Plotly
- 6.5Deployment: FastAPI, Flask,Streamlit)