0 to 100: ML Engineer in One Year

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If you're reading this, I shared it with you — directly or indirectly — which suggests you're in the general space of people studying to become ML engineers as quickly as possible.

My earliest observation is people accidentally sabotage themselves by thinking it will be too easy, or too hard.

People who think it'll be easy plan to make this transition in 3 months. Six, for some. I recommend a year of conscientious grinding, especially if — like me — your math is completely nonexistent. 

If I had to streamline the path, I'd say you need to:

1. Pick up a framework (like Tensorflow or Keras) and get really good at it. This is probably more important than math, when you're starting out.

2. Understand neural networks and deep learning. This will, hopefully, naturally push you in the direction of getting to understand the underlying math. 

Anyway, here's my personal 1-year plan. Chipping away at it ever so slowly...

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# ML Engineer in 1 Year
### 1. Core Python & Libraries (1 month)
* **Goal**: Achieve fluency with Python, focusing on code readability and debugging.
* **Focus**: Key libraries — *NumPy, Pandas, Matplotlib, Scikit-Learn*.
* **Time**: Dedicate 1-2 hours daily to code exercises and small projects.
* **Resources**: *Automate the Boring Stuff with Python* (book) + *LeetCode* for algorithm practice.

⠀2. Math for ML (1.5 months)
* **Goal**: Build a solid understanding of the math directly applicable to ML models.
* **Focus**:
  * Linear Algebra (vectors, matrices, eigenvalues)
  * Calculus (derivatives for optimization)
  * Probability & Statistics (Bayes theorem, distributions, basic inference)
* **Time**: 4-5 hours per week, using practical coding exercises to reinforce concepts.
* **Resources**: *3Blue1Brown’s* Linear Algebra series, *Khan Academy* for targeted calculus topics, *StatQuest* videos for probability.

⠀3. Machine Learning Foundations (2 months)
* **Goal**: Develop hands-on knowledge of core ML algorithms and techniques.
* **Focus**:
  * Supervised Learning (Linear Regression, Decision Trees, SVMs)
  * Unsupervised Learning (K-means, PCA)
  * Model Evaluation (train-test split, cross-validation, precision/recall, ROC-AUC)
* **Time**: Aim for completing 2-3 projects during this period.
* **Resources**: *Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow* by Géron.

⠀4. Deep Learning Specialization (2 months)
* **Goal**: Build proficiency in neural networks and understand when to use them.
* **Focus**:
  * Neural network basics: feedforward, backpropagation, activation functions.
  * Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  * Basic NLP concepts for understanding transformers.
* **Time**: 4-5 hours weekly with focus on implementing small projects.
* **Resources**: *Deep Learning Specialization* by Andrew Ng (Coursera) and *fast.ai* for practical work.

⠀5. Model Deployment & MLOps (1.5 months)
* **Goal**: Learn to deploy models and manage ML pipelines in production.
* **Focus**:
  * Docker for containerization
  * Flask/FastAPI for building lightweight APIs
  * Model versioning and deployment on cloud platforms (AWS, GCP, or Azure)
* **Time**: Complete 1-2 end-to-end projects (data ingestion to deployment).
* **Resources**: *Full Stack Deep Learning* course for deployment best practices.

⠀6. Project Portfolio & Specialized Area (2 months)
* **Goal**: Gain expertise in a sub-field of ML and create a compelling portfolio.
* **Focus**:
  * Choose one specialization (e.g., NLP, computer vision, recommendation systems) and work on advanced projects in that area.
  * Publish code on GitHub and write brief case studies for each project.
* **Time**: Dedicate 10-15 hours weekly to deep-dive into the chosen area.
* **Resources**: Research papers, Kaggle competitions, and GitHub repositories in your area of interest.

⠀Ongoing
* **Networking & Learning**: Engage in ML communities (LinkedIn, Kaggle, GitHub).
* **Practice and Review**: Review, debug, and improve past projects to deepen understanding.

⠀This plan is highly streamlined and will require intense focus, especially on practical applications. Stick to small, meaningful projects, and resist spending too much time on non-essential theory.