- Purpose: Research-focused learning for specific domains to write papers and conduct studies.
- Target Audience: Researchers, Academics, and ML/AI Enthusiasts.
This roadmap is designed for domain-specific research, so you don’t need to learn everything related to Machine Learning or AI. Instead, focus only on the topics relevant to your chosen research domain. This approach ensures a streamlined and efficient learning path tailored to your goals.
- Statistical Machine Learning (Classification & Regression)
- Computer Vision
- NLP & Sentiment Analysis
- Time Series Analysis
- Generative AI with Pretrained Models
You can dive deep into your selected area without worrying about unrelated concepts from other domains. This specificity will save time and enhance your expertise in the chosen field. Best of luck 😊
- AI Engineers need a strong programming foundation for implementing AI models and systems.
- Python Basics:
- Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs.
- Libraries:
- NumPy: Numerical computations.
- Pandas & Polars: Data manipulation and cleaning.
- Matplotlib/Seaborn/Plotly: Data visualization.
- Provides the foundation for understanding and implementing AI algorithms.
- Linear Algebra:
- Matrices, vectors, eigenvalues, eigenvectors.
- Calculus:
- Differentiation and integration for optimization.
- Probability and Statistics:
- Probability distributions, Bayes' theorem, hypothesis testing.
- Optimization:
- Gradient descent, convex and non-convex optimization.
- AI Engineers build on ML techniques to create intelligent systems.
- Statistics
- Correlation, Data Distributions, Hypothesis Testing
- Supervised Learning:
- Linear, Polynomial, Logistic Regression.
- Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost).
- Unsupervised Learning:
- Clustering (K-means, DBSCAN).
- Dimensionality Reduction (PCA, t-SNE).
- Model Optimization:
- Cross-validation, Gradient Descent Variants.
- Machine Learning Playlist
- Machine Learning Module
- Scikit-learn (
sklearn): For statistical machine learning models. - Statsmodels: For statistical analysis.
- SciPy: For statistical analysis.
- Practice using Python's
sklearnand Kaggle competitions.
- A field of AI enabling machines to interpret visual data.
- Includes object detection, image segmentation, and classification.
- Experiment with deep learning models for vision tasks.
- Evaluate performance using standard datasets.
- Publish findings in journals or conferences.
Why Learn Vision Basics?
- Foundational knowledge is essential for image processing tasks.
What to Learn?
- Image Processing: Resizing, filtering, transformations.
- Feature Extraction: HOG, SIFT, SURF.
- Edge Detection and Histograms.
Resources
Why Learn Deep Learning?
- Deep learning powers state-of-the-art vision research.
What to Learn?
- CNN Architectures: ResNet, VGG, MobileNet.
- Specialized Models: YOLO, Faster R-CNN (object detection), U-Net (segmentation).
- GANs for Vision: StyleGAN, CycleGAN.
Resources
What to Focus On?
- Comparing CNN architectures.
- Evaluating object detection algorithms on standard datasets.
- Publishing findings with visualization results.
Resources
- A branch of AI that focuses on understanding and generating human language.
- Study language models for tasks like sentiment analysis and NER.
- Evaluate model performance on NLP benchmarks.
- Publish findings related to language understanding.
Why Learn NLP Basics?
- Foundational concepts help in preprocessing and understanding text data.
What to Learn?
- Tokenization, Stemming, Lemmatization.
- Word Embeddings: Word2Vec, GloVe, FastText.
- Advanced Concepts: TF-IDF, NER.
Resources
Why Learn Deep Learning?
- Powers advanced models for tasks like sentiment analysis and text generation.
What to Learn?
- RNN, LSTM, GRU for Sequence models
- Transformer Architectures: BERT, GPT.
- Sequence-to-Sequence Models: Attention Mechanisms, Seq2Seq.
Resources
What to Focus On?
- Evaluating sentiment analysis models.
- Analyzing pre-trained models for domain-specific tasks.
- Writing papers highlighting advancements in NLP.
Resources
- Focuses on analyzing data indexed in time order for forecasting and pattern detection.
- Experiment with time series forecasting models.
- Evaluate model performance on historical datasets.
- Publish findings in journals or conferences.
What to Learn?
- Concepts: Stationarity, Seasonality, Trends.
- Techniques: ACF, PACF, Moving Averages.
Resources
What to Learn?
- LSTMs and GRUs for sequence modeling.
- ARIMA, SARIMA for classical forecasting.
Resources
- Focuses on generating text, images, and other content using advanced models.
- Fine-tune pre-trained models for creative applications.
- Evaluate performance on generative benchmarks.
What to Learn?
- GANs: StyleGAN, DCGAN.
- Transformers: GPT, Stable Diffusion.
Resources
- Understand domain-specific prerequisites.
- Apply ML/DL techniques to datasets.
- Evaluate models and publish findings.
I am not a professional researcher but have a working knowledge of Machine Learning. While I can guide you on relevant topics and concepts in ML for your chosen domain, I may not be able to assist with the detailed aspects of research methodologies or paper writing. For deeper guidance, consider collaborating with experienced researchers or referring to academic resources. 😊
Note: We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that T-shaped skills are better than i-shaped skill. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of free tutorials that are also great for learning. Best of luck!
Hazrat Ali
- 🌐 LinkedIn Profile
- 🎓 Programmer || Software Engineering