🤖 AI & Machine Learning
Python for Data Science, AI & Development
Build real-world expertise in Python across data science, AI, and modern software development. This hands-on course move...
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Sections
1. Python Foundations for Data Work7 views
Master core Python syntax and tooling for data tasks, from environments and notebooks to clean, reliable scripts.
15 topics (15 versions)
1.1Installing Python and Tooling
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1.2Working in Jupyter and VS Code
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1.3Running Scripts and Notebooks
1.4Variables, Types, and Casting
1.5Strings and f-strings
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1.6Numbers and Arithmetic
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1.7Booleans and Logic
1.8Conditionals and Control Flow
1.9Functions and Docstrings
1.10Modules and Imports
1.11Virtual Environments
1.12Errors and Exceptions
1.13File I/O Essentials
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1.14Coding Style and PEP 8
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1.15Using the REPL and Help
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2. Data Structures and Iteration14 views
Use Python collections and iteration patterns to write expressive, efficient, and readable data-oriented code.
15 topics (15 versions)
2.1Lists and List Comprehensions
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2.2Tuples and Immutability
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2.3Dictionaries and Dict Comprehensions
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2.4Sets and Set Operations
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2.5Slicing and Views
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2.6Iterables and Iterators
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2.7Generators and yield
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2.8Enumerate and Zip
2.9Sorting and Custom Keys
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2.10Lambda Functions
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2.11Map, Filter, Reduce
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2.12*args and **kwargs
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2.13Recursion vs Iteration
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2.14Time Complexity Basics
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2.15Type Hints and dataclasses
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3. Numerical Computing with NumPy15 views
Leverage NumPy for fast array programming, broadcasting, vectorization, and linear algebra operations.
15 topics (15 versions)
3.1ndarray Creation
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3.2Dtypes and Casting
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3.3Indexing and Slicing
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3.4Boolean Masking
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3.5Broadcasting Rules
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3.6Vectorization Techniques
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3.7Universal Functions (ufuncs)
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3.8Aggregations and Reductions
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3.9Reshaping and Transpose
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3.10Stacking and Splitting
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3.11Random Number Generation
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3.12Linear Algebra Routines
3.13Memory Layout and Strides
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3.14Performance Tips and NumExpr
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3.15Saving and Loading Arrays
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4. Data Analysis with pandas11 views
Manipulate and analyze tabular data using pandas for indexing, joins, time series, and robust I/O.
15 topics (15 versions)
4.1Series and DataFrame Basics
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4.2Reading CSV and Excel
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4.3Indexing and Selection
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4.4Filtering and query
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4.5Handling Missing Values
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4.6Type Conversion and Categories
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4.7Sorting and Ranking
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4.8GroupBy and Aggregations
4.9Apply and Vectorized Ops
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4.10Merge, Join, and Concat
4.11Pivot Tables and Crosstabs
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4.12Time Series with pandas
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4.13Window and Rolling Ops
4.14String Methods and Regex
4.15Database I/O with SQLAlchemy
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5. Data Cleaning and Feature Engineering15 views
Prepare high-quality datasets with robust transformations and informative features while avoiding leakage.
15 topics (15 versions)
5.1Detecting and Handling Outliers
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5.2Imputation Strategies
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5.3Scaling and Normalization
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5.4Encoding Categorical Variables
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5.5Feature Binning and Discretization
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5.6Feature Interactions and Polynomials
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5.7Text Cleaning Basics
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5.8Datetime Parsing and Features
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5.9Addressing Class Imbalance
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5.10Target Leakage Avoidance
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5.11Train–Validation Splits
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5.12Pipeline-Friendly Transforms
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5.13Feature Selection Methods
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5.14Dimensionality Reduction
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5.15Multicollinearity and Correlation
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6. Data Visualization and Storytelling0 views
Explore and communicate insights with clear, accessible visuals using Matplotlib, Seaborn, and Plotly.
15 topics (15 versions)
6.1Visualization Principles
6.2Matplotlib Essentials
6.3Seaborn for Statistical Plots
6.4Plotly for Interactive Charts
6.5Histograms and Density Plots
6.6Scatterplots and Pair Plots
6.7Bar Charts and Categorical Plots
6.8Time Series Visualizations
6.9Heatmaps and Correlations
6.10Faceting and Small Multiples
6.11Annotations and Highlights
6.12Color, Themes, and Accessibility
6.13Dashboard Basics
6.14Exporting and Sharing Figures
6.15Communicating Uncertainty
7. Statistics and Probability for Data Science12 views
Develop statistical intuition for inference, experimentation, and uncertainty-aware decisions.
15 topics (15 versions)
7.1Descriptive Statistics
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7.2Probability Distributions
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7.3Sampling and CLT
7.4Hypothesis Testing
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7.5Confidence Intervals
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7.6t-tests and ANOVA
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7.7Nonparametric Tests
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7.8Correlation and Covariance
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7.9Regression Fundamentals
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7.10Bias–Variance Tradeoff
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7.11Cross-Validation Concepts
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7.12Bayesian Thinking Basics
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7.13A/B Testing Design
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7.14Power and Sample Size
7.15Causality and Confounding
8. Machine Learning with scikit-learn16 views
Build, tune, and evaluate models using scikit-learn pipelines with reproducible ML workflows.
15 topics (15 versions)
8.1ML Workflow and Pipelines
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8.2Data Splits and CV Strategies
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8.3Classification Metrics
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8.4Regression Metrics
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8.5Linear and Logistic Regression
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8.6Decision Trees and Forests
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8.7Gradient Boosting Methods
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8.8kNN and SVM
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8.9Naive Bayes Models
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8.10Clustering with k-means
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8.11Dimensionality Reduction with PCA
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8.12Hyperparameter Tuning
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8.13Model Interpretation
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8.14Handling Class Imbalance
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8.15Saving and Loading Models
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9. Deep Learning Foundations12 views
Understand neural networks and train models with PyTorch, from CNNs to transformers and deployment.
15 topics (15 versions)
9.1Neural Network Basics
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9.2Activation Functions
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9.3Backpropagation Intuition
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9.4PyTorch Tensors
9.5Building Models in PyTorch
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9.6Training Loops and Optimizers
9.7Regularization and Dropout
9.8Convolutional Neural Networks
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9.9Recurrent Networks and LSTM
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9.10Transformers Foundations
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9.11Transfer Learning
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9.12Embeddings and Representations
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9.13Data Augmentation
9.14GPU Acceleration
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9.15Serving Deep Models
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10. Data Sources, Engineering, and Deployment11 views
Acquire data from files, web, and databases; then test, package, version, and deploy reliable services.
15 topics (15 versions)
10.1Working with Files and Formats
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10.2JSON and XML Parsing
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10.3Web Scraping Basics
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10.4REST APIs and requests
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10.5Authentication and Tokens
10.6SQL Fundamentals
10.7pandas with SQLAlchemy
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10.8Git and GitHub Workflows
10.9Spark for Large Datasets
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10.10Data Versioning with DVC
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10.11Packaging with Poetry or pip
10.12Testing with pytest
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10.13Logging and Configuration
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10.14Building REST APIs with FastAPI
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10.15Containers and Deployment
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