Become a Data Scientist and Machine Learning Engineer in 12 Months for Python Developers

Month 1: Python Refresher and Data Science Librarie

Week 1: Python Refresher

Review Python basics (variables, data types, loops).
Practice writing functions and handling exceptions.
Explore object-oriented programming concepts (classes, inheritance).
Work on mini-projects to apply Python concepts.
Recap and reinforce concepts learned.
Week 2: Python Refresher

Dive deeper into Python data structures (lists, dictionaries, sets).
Study file handling and modules.
Practice advanced Python topics (decorators, generators).
Work on more complex projects that use Python skills.
Recap and reinforce concepts learned.
Week 3: Introduction to Data Science Libraries

Introduction to Pandas (Series, DataFrame, data manipulation).
Data cleaning and handling missing data in Pandas.
Data aggregation and merging datasets.
Practice data analysis and visualization using Pandas.
Recap and reinforce concepts learned.
Week 4: Introduction to Data Science Libraries

Introduction to NumPy (arrays, array operations).
NumPy universal functions and broadcasting.
Practice array slicing and indexing in NumPy.
Perform numerical computations using NumPy.
Recap and reinforce concepts learned.
Month 2: Web Scraping and Data Visualization

Week 1: Web Scraping

Introduction to web scraping, HTML basics.
Using BeautifulSoup to parse HTML and extract data.
Scraping data from a single web page.
Handling pagination and scraping multiple pages.
Recap and reinforce web scraping concepts.
Week 2: Web Scraping

Introduction to web requests using requests library.
Handling different data formats (JSON, XML) during scraping.
Dealing with website restrictions (robots.txt, headers).
Scraping dynamic websites using Selenium.
Recap and reinforce web scraping concepts.
Week 3: Data Visualization

Introduction to Matplotlib for basic plotting.
Customizing plots, adding labels, and annotations.
Creating bar charts, pie charts, and histograms.
Working with subplots and multiple plots.
Recap and reinforce data visualization concepts.
Week 4: Data Visualization

Introduction to Seaborn for statistical data visualization.
Creating categorical plots and distribution plots.
Visualizing relationships with scatter plots and regression plots.
Plotting time series data with Seaborn.
Recap and reinforce data visualization concepts.
Month 3-4: Mathematics for Machine Learning and Introduction to Machine Learning

Week 1: Mathematics for Machine Learning

Introduction to linear algebra (vectors, matrices).
Matrix operations, matrix multiplication, and inverse.
Understanding vector spaces and vector norms.
Eigenvectors, eigenvalues, and applications in machine learning.
Recap and reinforce linear algebra concepts.
Week 2: Mathematics for Machine Learning

Introduction to calculus (differentiation, integration).
Gradient descent and optimization algorithms.
Partial derivatives and the chain rule.
Gradient computation for simple machine learning models.
Recap and reinforce calculus concepts.
Week 3: Introduction to Machine Learning

Supervised learning and linear regression.
Cost functions and gradient descent for linear regression.
Multiple linear regression and polynomial regression.
Model evaluation and regularization techniques.
Recap and reinforce regression concepts.
Week 4: Introduction to Machine Learning

Classification algorithms (Logistic Regression, K-Nearest Neighbors).
Decision Trees and Random Forests.
Naive Bayes and Support Vector Machines.
Model evaluation for classification.
Recap and reinforce classification concepts.
Month 5-6: Deep Learning Fundamentals and TensorFlow Basics

Week 1: Deep Learning Fundamentals

Introduction to neural networks and activation functions.
Gradient descent and backpropagation.
Building a simple neural network from scratch.
Deep learning frameworks and their advantages.
Recap and reinforce deep learning fundamentals.
Week 2: Deep Learning Fundamentals

Convolutional Neural Networks (CNNs) for image data.
Building and training a basic CNN model.
Understanding pooling layers and feature visualization.
Transfer learning and pre-trained models.
Recap and reinforce CNN concepts.
Week 3: TensorFlow Basics

Introduction to TensorFlow and its architecture.
Building computational graphs and using sessions.
Placeholder, Variables, and operations in TensorFlow.
Training a simple neural network using TensorFlow.
Recap and reinforce TensorFlow concepts.
Week 4: TensorFlow Basics

Introduction to TensorBoard for visualization.
Using TensorFlow's Estimators and Keras API.
Saving and restoring TensorFlow models.
Deploying a TensorFlow model in production.
Recap and reinforce TensorFlow concepts.
Month 7-8: PyTorch Basics and Advanced Deep Learning

Week 1: PyTorch Basics

Introduction to PyTorch and its tensors.
Building and training a basic neural network in PyTorch.
Automatic differentiation and gradients in PyTorch.
Customizing PyTorch models and loss functions.
Recap and reinforce PyTorch concepts.
Week 2: PyTorch Basics

Using PyTorch's DataLoader and Dataset classes.
Transfer learning and fine-tuning in PyTorch.
Building and training a CNN using PyTorch.
Using CUDA for GPU acceleration in PyTorch.
Recap and reinforce PyTorch concepts.
Week 3: Advanced Deep Learning

Recurrent Neural Networks (RNNs) for sequence data.
Building and training an RNN model.
Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs).
Sequence-to-Sequence models and attention mechanisms.
Recap and reinforce RNN and LSTM concepts.
Week 4: Advanced Deep Learning

Generative Adversarial Networks (GANs) for image generation.
Building and training a basic GAN model.
Variational Autoencoders (VAEs) for image generation.
Building and training a VAE model.
Recap and reinforce GAN and VAE concepts.
Month 9-10: Model Deployment and AWS, Project and Portfolio Building

Week 1: Model Deployment and AWS

Introduction to model deployment and serving.
Building a Flask web API for model deployment.
Containerization using Docker for deployment.
Deploying a model on AWS Lambda or AWS EC2.
Recap and reinforce model deployment concepts.
Week 2: Model Deployment and AWS

Implementing a simple web interface for model consumption.
Setting up AWS S3 for data storage and retrieval.
Using Amazon API Gateway for serverless API deployment.
Monitoring and scaling deployed models on AWS.
Recap and reinforce AWS deployment concepts.
Week 3: Project and Portfolio Building

Choose a machine learning project of interest.
Plan the project, including data collection and preprocessing.
Implement the project using the skills learned throughout the year.
Test and refine the project for optimal performance.
Create a portfolio showcasing your projects and accomplishments.
Week 4: Project and Portfolio Building

Write detailed documentation for each project, including the problem statement, approach, implementation, and results.
Organize your projects into a professional portfolio with clear explanations and visuals.
Review and finalize your portfolio for presentation.
End of Month 12: Showcase and Networking

Share your portfolio and projects on social media platforms, GitHub, and relevant online communities.
Participate in machine learning forums and discussions to network with otherss