Complete MLOps Bootcamp With 10+ End To End ML Projects

Explore choosing the right IDE for Python coding, and learn to use Google Colab for cloud-based notebooks, including creating notebooks, running cells, uploading data, and accessing GPUs.

Explore GitHub codespace, a cloud-based VS Code experience, to code and collaborate without a local setup. Start with templates, run Python or Jupyter notebooks in the cloud.

Install Anaconda and VS Code to set up a versatile data science IDE, create conda environments, manage packages, and run Python and Jupyter notebooks.

Start the Python series by using VS Code to run Python files and Jupyter notebooks, create a conda environment with Python 3.12, activate it, and install ipykernel to run notebooks.

Explore Python basics by distinguishing syntax from semantics, and practice with comments, case sensitivity, indentation, line continuation, and variable type inference.

Explore variables in Python by declaring and assigning values, mastering naming conventions, understanding data types, dynamic typing, and type conversion through practical examples and input-based exercises.

Explore basic data types in Python, including integers, floating point numbers, strings, and booleans, and learn type casting, common type errors, and memory considerations.

Explore Python operators, including arithmetic, comparison, and logical operators, with practical examples and a mini calculator, covering floor division, modulus, exponent, and boolean logic.

Master Python control flow with conditional statements, including if, else, and elif, plus nested conditionals. Work through practical examples like voting eligibility, leap year checks, and even or odd determinations.

Master loops in python, including for and while loops, range basics, nested loops, and break, continue, and pass. Explore traversing strings, summing the first ten natural numbers, and prime numbers.

Explore practical Python list use cases through real-world tasks like to-do lists, grade management, inventory tracking, and user feedback analysis to reinforce core list operations.

Explore Python sets, a built-in data type for storing unique items, with union, intersection, difference, and symmetric difference. Practice membership tests and set methods like add, remove, and clear.

Explore tuples in Python by learning creation, access, immutability, packing and unpacking, and common methods like count and index, plus nesting and conversions with lists.

Master dictionaries in Python: create, access, modify, and iterate; use keys, values, items; work with nested dictionaries, dictionary comprehension, and practical tasks like frequency counting and merging.

Explore Python functions, from defining and calling them to using default parameters, positional and keyword arguments. Learn about return statements with practical examples like even/odd and add.

Learn Python function examples in the complete mlops bootcamp, including temperature conversion, password strength checker, shopping cart total cost, palindrome checks, recursive factorials, file word frequency, and email validation.

Explore lambda functions in Python, tiny anonymous functions defined with the lambda keyword returning a single expression, with examples including addition, even checks, and map usage.

Master the Python map function, applying a function to every item in an iterable to transform data efficiently with map objects and lambda usage.

Explore the Python filter function, using iterables and conditions to select items; implement with functions and lambda expressions to filter lists and dictionaries by criteria.

Explore how to import modules and packages in Python, install and alias packages like numpy, and build custom packages with __init__.py, then call functions such as addition and multiply.

Learn a practical overview of Python's standard library, including arrays, math, random, os, shutil, json, csv, datetime, time, and re, with hands-on file and data serialization examples.

Master Python file operations for text and binary files using with open, reading, writing, appending, line-by-line reads, and seek to reset the cursor.

Explore file path operations in Python using the OS module, including creating directories, listing contents, joining paths, checking existence, and distinguishing absolute and relative paths for files and folders.

Learn to handle Python exceptions gracefully using try, except, else, and finally blocks, catching errors like name errors and zero division errors without stopping program execution.

Define classes and create objects in Python to model real world entities, using constructors, self, and instance methods to build reusable bank account and dog examples.

Explore inheritance in Python with single and multiple inheritance, using car and Tesla examples, learn how to use super and init to initialize attributes and methods.

Explore polymorphism in Python within object oriented programming, using method overriding and abstract base classes to implement objects like dogs, cats, shapes, rectangles, and circles with shared interfaces.

Explore encapsulation in Python by bundling data and methods, and mastering getters and setters to protect private attributes, while previewing abstraction and access modifiers like public, protected, and private.

Explore abstraction in Python by hiding complex implementation and exposing only essential features through abstract base classes and abstract methods, with real-world examples like washing machines.

Explore Python magic (dunder) methods and learn how to override __init__, __str__, and __repr__ to customize object behavior for arithmetic and comparisons through practical class examples.

Employ custom exception handling in Python by defining a base error and a date of birth exception, then use try-except to validate ages 20–30 and display a user-friendly message.

Overload Python operators using magic methods in a vector example, overriding __add__, __sub__, __mul__, __truediv__, __eq__, and __repr__ to customize operations and comparisons.

Explore iterators in Python, showing how they enable efficient looping and memory management through lazy loading, the next function, and handling StopIteration with examples.

Learn how generators in Python create iterators with the yield keyword, enabling lazy value generation and memory-efficient looping, demonstrated by squaring numbers and reading large files.

Explore how decorators in Python modify function behavior without changing code, cover function copy, closures, and creating and applying decorators with practical examples.

Explore NumPy basics for data analysis and machine learning, including creating and reshaping arrays, vectorized operations, universal functions, and normalization with mean and std.

Learn to use pandas for data analysis in Python, mastering series and data frames, creating from dictionaries and lists, and performing data manipulation with read_csv, head, tail, and describe.

Learn data manipulation and analysis with pandas, performing exploratory data analysis, feature engineering, handling missing values, renaming and changing types, grouping and aggregating, and merging data frames.

Learn to read data from JSON, CSV, HTML, and Excel using pandas, convert data frames to JSON, and control output with orient and index options.

Learn to implement Python logging to track events and errors using the built-in logging module, configure levels and formats, and log outputs to files like app.log for end-to-end projects.

Create multiple loggers for different modules using logging.getLogger, set module-specific levels (debug for module one, warnings for module two), and log messages accordingly.

Set up multi-logger logging in a Python app, log arithmetic operations to app one.log using file and stream handlers, and capture division by zero with debug and error logs.

Introduce the Flask framework for building web apps in Python, explain WSGI and Jinja2, and show how forms interact with ML models to create dynamic pages from data sources.

Learn how to build a simple Flask web application from a basic skeleton, create routes, run the app, and explore debugging and template basics.

Explore integrating html with the Flask framework using render_template and templates to build index and about pages, with routes and hosting on 0.0.0.0 for development.

Explore how to implement http get and post in a flask app, using render_template with a templates folder, handling form data via request.form and basic jinja2 templating.

Build dynamic URLs in Flask using get and post forms. Use render_template and the Jinja2 template engine to pass form data to HTML, apply a variable rule, and display results.

Build a Flask end-to-end API by implementing get, post, put, and delete for a to-do list with JSON. Learn routing, request handling, and how to test these endpoints with Postman.

Learn git fundamentals and GitHub workflows through hands-on tutorials on initializing repositories, configuring identity, staging and committing changes, pushing to origin, cloning, and branch management.

Master git workflows by using status, add, commit, and diff to track changes. Create and checkout a branch from main, merge back, push to GitHub, and review history with log.

Learn how to merge branches and resolve conflicts in a collaborative git workflow, including branching, merging to main, and handling push pull conflicts.

Explore mlflow, an end-to-end open-source mlops platform for managing data science and generative ai projects, with tracking, visualization, evaluation, model registry, and serving across sklearn, pytorch, and tensorflow.

Explore how MLflow supports the data science life cycle—from data preparation to monitoring—through experiment tracking, hyperparameter tuning, code structuring, and model deployment.

Set up an MLflow environment, install MLflow via a conda/venv with Python 3.10 and a requirements.txt, and prepare a tracking server for experiments.

Learn to set up the MLflow tracking server, configure the tracking URI, run experiments, and visualize and compare metrics and runs in the MLflow UI.

Explore how MLflow tracks experiments, stores runs in the mlruns directory, and uses start_run to log metrics, params, and artifacts, including remote repository considerations.

Master MLflow by setting up a tracking server URI, exploring MLflow UI for experiments and runs, and scaffolding an end-to-end ml project with dependencies like pandas, numpy, and scikit-learn.

Track an end-to-end ML project with MLflow by logging parameters, metrics, and artifacts from an iris dataset using scikit learn's logistic regression, and visualize results in MLflow UI.

Learn how to infer and validate MLflow artifacts, load a pickle model via mlflow.pyfunc, and perform predictions with serving inputs using iris data for deployment.

Learn MLflow model registry, a centralized store for versioned models with lineage, aliasing, tagging, and annotation, supporting registration and production deployment.

Prepare the California housing data, train a random forest regression model with gridsearchcv, and log hyperparameters and metrics in MLflow.

Train an artificial neural network with normalization and a single hidden layer, tuning learning rate and momentum while tracking experiments and rmse in MLflow during wine quality prediction.

Explore data version control with DVC, learn how to track and version datasets and ML models, integrate with git, and use remote repositories for collaborative ML workflows.

Explore DAGs Hub as a remote MLflow-backed repository to track experiments, share results with a team, and manage end-to-end ML projects within MLOps stacks.

Create your first remote repository on DAGs hub, initialize a git repository, and explore DVC for data versioning, MLflow for experiments, and connect to GitHub or other repos.

Explore versioning and collaboration for data and models using DVC with a dagshub remote repository. Learn to integrate git and DVC, track data changes, configure a remote, and MLflow experiments.