Roadmap

Tasks 2025

Q1

January

Sprint#0 2024-30-12

Sprint#1 2025-01-06

Sprint#2 2025-01-13

Sprint#3 2025-01-20

Sprint#4 2025-01-27

February

Sprint#5 2025-02-03

Sprint#6 2025-02-10

Sprint#7 2025-02-17

Sprint#8 2025-02-24

March

Sprint#9 2025-03-03

Sprint#10 2025-03-10

Sprint#11 2025-03-17

Sprint#12 2025-03-24

Sprint#13 2025-03-31

April

Sprint#14 2025-04-07

Q2

April

Sprint#0 2025-04-14

Calendar

Calendar
# Date Title Description Tasks
1 2024-01-06 Python Basics 1/4 Introduction to Python programming Variables, data types
1 2024-01-06 ML Foundations 1/4 Basic machine learning concepts Types of ML, learning paradigms
1 2024-01-06 ANN Foundation 1/4 Introduction to neural networks Network architecture basics
2 2024-01-13 Python Basics 2/4 Control structures Loops, conditionals, functions
2 2024-01-13 ML Foundations 2/4 Model evaluation Metrics, validation techniques
2 2024-01-13 ANN Foundation 2/4 Forward propagation Activation functions, layers
3 2024-01-20 Python Basics 3/4 Functions and modules Function creation, importing modules
3 2024-01-20 ML Foundations 3/4 Data preprocessing Cleaning, normalization, feature engineering
3 2024-01-20 ANN Foundation 3/4 Backpropagation Gradient descent basics
4 2024-01-27 Python Basics 4/4 Error handling Try-except, debugging
4 2024-01-27 ML Foundations 4/4 Model selection Cross-validation, hyperparameter tuning
4 2024-01-27 ANN Foundation 4/4 Loss functions Common losses, optimization basics
5 2024-02-03 Python Intermediate 1/4 OOP concepts Classes, objects, inheritance
5 2024-02-03 Supervised Learning 1/4 Linear regression Simple and multiple regression
5 2024-02-03 ANN Training 1/4 Optimization algorithms SGD, Adam, RMSprop
6 2024-02-10 Python Intermediate 2/4 Advanced functions Lambda, decorators, generators
6 2024-02-10 Supervised Learning 2/4 Classification basics Logistic regression, decision trees
6 2024-02-10 ANN Training 2/4 Regularization L1, L2, dropout
7 2024-02-17 Python Intermediate 3/4 File handling Reading/writing files, JSON
7 2024-02-17 Supervised Learning 3/4 Ensemble methods Random forests, boosting
7 2024-02-17 ANN Training 3/4 Batch normalization Implementation and effects
8 2024-02-24 Python Intermediate 4/4 Package management Pip, virtual environments
8 2024-02-24 Supervised Learning 4/4 Model deployment Saving, loading, serving models
8 2024-02-24 ANN Training 4/4 Model evaluation Metrics, validation strategies
9 2024-03-02 Python for DS 1/4 NumPy basics Arrays, operations, indexing
9 2024-03-02 Deep Learning 1/4 DL frameworks PyTorch basics
9 2024-03-02 ANN Advanced 1/4 Advanced architectures ResNet, Inception
10 2024-03-09 Python for DS 2/4 Pandas basics DataFrames, series, indexing
10 2024-03-09 Deep Learning 2/4 Data loading Datasets, dataloaders
10 2024-03-09 ANN Advanced 2/4 Transfer learning Pre-trained models, fine-tuning
11 2024-03-16 Python for DS 3/4 Data visualization Matplotlib, Seaborn
11 2024-03-16 Deep Learning 3/4 Custom layers Building network components
11 2024-03-16 ANN Advanced 3/4 Advanced training Learning rate scheduling
12 2024-03-23 Python for DS 4/4 Data analysis EDA, statistical analysis
12 2024-03-23 Deep Learning 4/4 Model optimization Performance tuning
12 2024-03-23 ANN Advanced 4/4 Deployment Production considerations
13 2024-03-30 Linear Algebra 1/4 Vectors and matrices Basic operations
13 2024-03-30 CNN 1/4 CNN basics Convolution operations
13 2024-03-30 ANN Applications 1/4 Image classification MNIST implementation
14 2024-04-06 Linear Algebra 2/4 Matrix operations Multiplication, inverse
14 2024-04-06 CNN 2/4 Pooling layers Max pooling, average pooling
14 2024-04-06 ANN Applications 2/4 Object detection YOLO implementation
15 2024-04-13 Linear Algebra 3/4 Eigenvalues Decomposition
15 2024-04-13 CNN 3/4 Modern architectures VGG, ResNet
15 2024-04-13 ANN Applications 3/4 Segmentation U-Net implementation
16 2024-04-20 Linear Algebra 4/4 PCA Dimensionality reduction
16 2024-04-20 CNN 4/4 Transfer learning Fine-tuning CNNs
16 2024-04-20 ANN Applications 4/4 Style transfer Neural style transfer
17 2024-04-27 Statistics 1/4 Probability basics Distributions
17 2024-04-27 RNN 1/4 RNN basics Sequential data
17 2024-04-27 RAG Basics 1/4 Vector databases Embedding basics
18 2024-05-04 Statistics 2/4 Hypothesis testing T-tests, chi-square
18 2024-05-04 RNN 2/4 LSTM Long-term dependencies
18 2024-05-04 RAG Basics 2/4 Retrieval methods Vector similarity
19 2024-05-11 Statistics 3/4 Regression analysis Statistical modeling
19 2024-05-11 RNN 3/4 GRU Gated recurrent units
19 2024-05-11 RAG Basics 3/4 Query processing Embedding generation
20 2024-05-18 Statistics 4/4 Bayesian stats Probabilistic modeling
20 2024-05-18 RNN 4/4 Attention mechanism Self-attention basics
20 2024-05-18 RAG Basics 4/4 Response generation Combining retrieved info
21 2024-05-25 Big Data 1/4 Distributed systems Hadoop ecosystem
21 2024-05-25 Lab: Classification 1/4 Project setup Data preparation
21 2024-05-25 RAG Implementation 1/4 System design Architecture planning
22 2024-06-01 Big Data 2/4 MapReduce Programming paradigm
22 2024-06-01 Lab: Classification 2/4 Model development Training pipeline
22 2024-06-01 RAG Implementation 2/4 Integration Connecting components
23 2024-06-08 Big Data 3/4 Spark basics RDD operations
23 2024-06-08 Lab: Classification 3/4 Model evaluation Performance analysis
23 2024-06-08 RAG Implementation 3/4 Optimization Performance tuning
24 2024-06-15 Big Data 4/4 Spark ML ML pipelines
24 2024-06-15 Lab: Classification 4/4 Deployment Production readiness
24 2024-06-15 RAG Implementation 4/4 Deployment System deployment
25 2024-06-22 Lab: NLP 1/4 Text preprocessing Tokenization, cleaning
25 2024-06-22 Lab: CV 1/4 Image preprocessing Augmentation pipeline
25 2024-06-22 Lab: Full Stack 1/4 Frontend design UI/UX planning
26 2024-06-29 Lab: NLP 2/4 Model architecture BERT implementation
26 2024-06-29 Lab: CV 2/4 Model development CNN architecture
26 2024-06-29 Lab: Full Stack 2/4 Backend setup API development
27 2024-07-06 Lab: NLP 3/4 Fine-tuning Transfer learning
27 2024-07-06 Lab: CV 3/4 Training Model optimization
27 2024-07-06 Lab: Full Stack 3/4 Integration API endpoints
28 2024-07-13 Lab: NLP 4/4 Deployment Production system
28 2024-07-13 Lab: CV 4/4 Deployment Model serving
28 2024-07-13 Lab: Full Stack 4/4 Deployment System launch
29 2024-07-20 Final Project 1/4 Project planning Requirements analysis
29 2024-07-20 Final Project 2/4 Implementation Core development
29 2024-07-20 Final Project 3/4 Testing System validation
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