The document outlines a comprehensive 8-month curriculum for learning computer science fundamentals and advanced Python, covering topics such as data structures, algorithms, SQL, statistics for AI, machine learning, deep learning, and practical applications with APIs and cloud platforms. Each month focuses on specific areas, gradually progressing from basic programming to complex data analysis and machine learning techniques. The curriculum also includes hands-on projects and tracks for natural language processing and computer vision.

2. CS Fundamentals & Python
Data Representation
Bits, bytes, and binary system
Computer Networks
IP addresses, internet routing,
protocols
Python Fundamentals
Variables, lists, dictionaries,
functions, modules
Programming Basics
Variables, conditionals, loops,
algorithms
Month - 1

3. Data Structures & Adv Python
Month - 2
01 02 03
Data Structures
Arrays, linked lists,
hash tables, stacks,
queues, trees,
graphs
Algorithms
Binary search,
bubble sort, quick
sort, merge sort,
recursion
Advanced Python
Inheritance,
generators,
iterators, list
comprehensions,
decorators

4. Version Control, SQL, & Data
Manipulation
Month - 3
SQL Basics
Relational databases, SELECT, WHERE, GROUP BY, ORDER BY
Data Manipulation
Numpy, Pandas, data visualizatio
Version Control
Git, GitHub, add, commit, push, branching

5. Math & Statistics for AI
Month - 4
01
02
03
04
05
06
07
08
09
10
Descriptive Statistics
Inferential Statistics
Continuous Data
Discrete Data
Mean, Median, Mode
Variance, Standard
Deviation
Probability, Distributions
Hypothesis Testing
Linear Algebra
Calculus

6. Data Analysis & Machine
Learning
Month - 5
Clustering, dimensionality reduction
Metrics for regression and classification
Regression, classification, linear
models, nonlinear models
Handling NA values,
outliers, normalization,
feature engineering
Preprocessing
Model Building
Model
Evaluation
Unsupervised
Learning

7. ML Ops & Machine Learning
Projects
Month - 6
APIs
FastAPI, server development
DevOps
CI/CD pipelines, containerization, Docker,
Kubernetes
Cloud Platforms
AWS, Azure
Machine Learning Projects
Apply learned skills to real-world
problems

8. Deep Learning
Month - 7
Neural Networks
Forward and
backward
propagation,
multilayer
perceptrons
Convolutional Neural
Networks
CNNs for image
data Sequence Models
RNNs, LSTMs for
sequential data

9. NLP or Computer Vision
Month - 8
NLP track
Regex, text representation
methods, classification, libraries
like Spacy and NLTK
CV track
Basic image processing
techniques, libraries like OpenCV,
CNNs, data preprocessing