Course Curriculum: The $180$ Day AI & ML Scientist

Course Link : https://aieworks.substack.com/p/announcing-our-new-180-day-ai-and

Course Details

Detail	Description
Course Name	$180$ Days to AI/ML Mastery: Code-First Foundations to Deep Learning Architectures
Description	This $180$-day, code-intensive journey transforms students into fluent AI/ML Scientists. We build the core mathematical intuition and practical coding skills necessary to implement, train, and deploy models from foundational statistics to modern deep neural networks. Each day is a hands-on coding session, ensuring immediate, tangible mastery over every algorithm.
Difficulty	Beginner/Intermediate (Starts at a Python beginner level, rapidly accelerates to Intermediate/Expert model development)
Duration	$180$ Lessons (Six months, every day code constraint)
Target Audience	Fresh computer Science & Engineering Grads, Software Engineers/Developers, Data Engineers, Technical Analysts, Product Managers, and anyone serious about transitioning into a Data Scientist or ML Engineer role.

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Why This Course?

Most introductory courses leave you with scattered theoretical knowledge. This course delivers practical model mastery. We don't just explain Gradient Descent; we code it from scratch. We don't just list the features of $\text{Scikit-learn}$; we build and tune end-to-end production-ready $\text{ML}$ pipelines. The focus is on the $99%$ of $\text{ML}$ work: data wrangling, feature engineering, and rigorous model evaluation.

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What You'll Build

The core project journey is a Comprehensive Model Portfolio including:

1. Fundamental $\text{ML}$ Algorithms: Coded from scratch (e.g., Linear Regression, Neural Network Perceptron).
2. An End-to-End $\text{ML}$ Pipeline: Featuring advanced $\text{Scikit-learn}$ techniques (Pipelines, $\text{XGBoost}$).
3. A Computer Vision Classifier: Using $\text{TensorFlow/Keras}$ for image recognition.
4. A Natural Language Processing System: Building a sentiment analyzer with $\text{RNNs/LSTMs}$.

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What Makes This Course Different?

• Hands-on, Every-Day Code Constraint: $180$ individual, build-along coding lessons. Math and theory are immediately followed by code.
• Intuition over Jargon: We demystify complex math (Linear Algebra, Calculus) by showing exactly how it powers the core algorithms, making $\text{ML}$ feel like applied mathematics, not black magic.
• Progressive Mastery: The curriculum starts at the basics and logically builds to the $\text{most}$ advanced $\text{DL}$ concepts—you won't feel lost.

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Prerequisites

• Zero prior $\text{AI}/\text{ML}$ experience is fine.
• Basic familiarity with a computer and comfort with installing software.
• A strong work ethic and commitment to coding every day.

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Course Structure: Logical Progression ($4$ Modules)

The course is structured into four modules, moving from coding fundamentals to building complex Deep Learning models.

Module	Duration	Core Focus	Tangible Outcome/Success Criteria
Module 1	Days $1–36$	Foundational Skills & Data Tools	Proficiency in Python, a working grasp of $\text{ML}$ math ($\text{LA}$, Calculus, Stats), and practical skill with NumPy/Pandas for $\text{EDA}$.
Module 2	Days $37–84$	Introduction to Machine Learning ($\text{Scikit-learn}$)	Ability to implement, train, and rigorously evaluate core Supervised Learning models (Linear, Logistic, $\text{DTs}$, $\text{SVMs}$).
Module 3	Days $85–126$	Unsupervised & Advanced $\text{ML}$	Mastery of Clustering and Dimensionality Reduction ($\text{PCA}$). Experience with $\text{RL}$ basics and $\text{Recommender}$ systems.
Module 4	Days $127–180$	Deep Learning ($\text{DL}$) Architectures	Deep understanding of Neural Networks ($\text{MLP}$, $\text{CNNs}$, $\text{RNNs}$) and practical implementation using $\text{TensorFlow}/\text{PyTorch}$.

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Detailed Curriculum: $180$ Day Lesson Plan

The curriculum below is based directly on the provided $180$-Day plan, structured for rapid skill acquisition.

Module 1: Foundational Skills (Days $1–36$)

Focus: Building the essential Python, Math, and Data Science Toolkit.

Days	Lesson Topics (Hands-on Code)	Key Concept/Insight
$1–7$	Python Crash Course & Game Project	Control Flow, Functions, Data Structures (Lists, Dicts, Sets). The basic building blocks of any $\text{ML}$ script.
$8–15$	Math Essentials: $\text{LA}$ & Calculus	Vectors, Matrix Multiplication (the core of Neural Networks), Derivatives, and Gradient Descent (code this optimization step).
$23–30$	Probability & Statistics	Bayes' Theorem, Distributions, $\text{CLT}$. Understanding data noise, likelihood, and the intuition behind model error.
$31–36$	Python Libraries ($\text{NumPy}/\text{Pandas}$)	Vectorization with $\text{NumPy}$ (why it's $100\text{x}$ faster), DataFrames for data manipulation, cleaning missing data.

Learning Objectives (Module 1):

• Fluency in Python for data manipulation and project setup.
• Code the core mechanics of Linear Algebra and Gradient Descent from scratch.
• Conduct Exploratory Data Analysis ($\text{EDA}$) on a real-world dataset using $\text{Pandas}$.

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Module 2: Introduction to Machine Learning (Days $37–84$)

Focus: Implementing and evaluating core Supervised Learning algorithms using $\text{Scikit-learn}$.

Days	Lesson Topics (Hands-on Code)	Key Concept/Insight
$37–43$	Core $\text{ML}$ Concepts & Workflow	Overfitting/Underfitting, $\text{Train}/\text{Test}/\text{Validation}$ Split (the most critical step), Model Evaluation Metrics.
$44–51$	Regression & Classification Models	Linear Regression ($\text{cost function}$), Logistic Regression (the $\text{sigmoid}$ function and probability), Project: Predict Housing Prices and Spam Detection.
$58–65$	Advanced Classification	Decision Trees ($\text{information gain}$), Random Forests ($\text{Ensemble}$ wisdom), $\text{KNN}$ ($\text{distance}$ metrics), $\text{SVMs}$ (the $\text{kernel}$ $\text{trick}$).
$71–84$	The $\text{Scikit-learn}$ Ecosystem	Pipelines (chaining pre-processing and models), Feature Scaling, Feature Engineering, Model Persistence ($\text{saving}$ and $\text{loading}$).

Learning Objectives (Module 2):

• Implement and interpret the results of all major Supervised Learning algorithms.
• Master the $\text{ML}$ workflow, from data pre-processing to model evaluation.
• Build an End-to-End $\text{ML}$ Pipeline using $\text{Scikit-learn}$ on a public dataset (e.g., Titanic).

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Module 3: Unsupervised & Reinforcement Learning (Days $85–126$)

Focus: Moving beyond labeled data, finding patterns, and learning from interaction.

Days	Lesson Topics (Hands-on Code)	Key Concept/Insight
$85–92$	Unsupervised Learning	K-Means Clustering ($\text{choosing}$ $K$), $\text{Hierarchical}$ $\text{Clustering}$, $\text{PCA}$ for $\text{Dimensionality}$ $\text{Reduction}$.
$99–105$	$\text{RL}$ & $\text{Recommender}$ Systems	Q-Learning ($\text{Exploration}$ $\text{vs.}$ $\text{Exploitation}$), $\text{Agents}$, $\text{Environments}$, $\text{Collaborative}$ $\text{Filtering}$ ($\text{Matrix}$ $\text{Factorization}$ intuition).
$113–126$	Advanced $\text{ML}$ & Tuning	Gradient Boosting ($\text{XGBoost/LightGBM}$)—the powerhouses of structured data. Hyperparameter Tuning ($\text{Grid}$ $\text{Search/Random}$ $\text{Search}$ $\text{theory}$).

Learning Objectives (Module 3):

• Apply Unsupervised techniques for clustering and feature space reduction.
• Grasp the core concepts of $\text{Reinforcement}$ $\text{Learning}$ and implement a simple $\text{RL}$ agent.
• Optimize model performance using advanced techniques like $\text{XGBoost}$ and systematic hyperparameter tuning.

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Module 4: Deep Learning ($\text{DL}$) Architectures (Days $127–180$)

Focus: Building the most powerful modern $\text{AI}$ models using $\text{TensorFlow/PyTorch}$ for Vision and Language.

Days	Lesson Topics (Hands-on Code)	Key Concept/Insight
$127–140$	Neural Networks from Scratch	$\text{Perceptron}$, $\text{Activation}$ $\text{Functions}$, $\text{MLPs}$. Manually coding the Backpropagation algorithm—this is the $\text{DL}$ "aha!" moment.
$141–154$	$\text{DL}$ Frameworks ($\text{TF}/\text{PyTorch}$)	$\text{Keras}$ $\text{Sequential}$ $\text{Models}$, $\text{PyTorch}$ Tensors and Automatic Differentiation ($\text{autograd}$). Building the foundation for all modern $\text{DL}$.
$155–168$	Computer Vision ($\text{CNNs}$)	Convolutional $\text{Neural}$ $\text{Networks}$ ($\text{CNNs}$)—the workhorse of $\text{CV}$. $\text{Pooling}$ $\text{Layers}$, $\text{Transfer}$ $\text{Learning}$ intuition. Project: Image Classifier.
$169–180$	Natural Language Processing ($\text{NLP}$)	$\text{Tokenization}$, Word Embeddings ($\text{Word2Vec}$), $\text{RNNs}$ and $\text{LSTMs}$ (for sequence data). Final Project: Build a Sentiment Analyzer or Chatbot.

Learning Objectives (Module 4):

• Implement the Backpropagation algorithm to train a basic Neural Network.
• Build and train models using both $\text{TensorFlow}/\text{Keras}$ and $\text{PyTorch}$.
• Design and deploy specialized $\text{DL}$ architectures ($\text{CNNs}$ and $\text{LSTMs}$) for Computer Vision and $\text{NLP}$ tasks.

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Final Word: By the end of this $180$-day journey, you won't just have theoretical knowledge; you'll have a portfolio of implemented algorithms and the deep understanding necessary to read any $\text{ML}$ paper, evaluate any model, and confidently step into the role of a capable $\text{AI}/\text{ML}$ Scientist. Go build something great.