I am doing my PhD on Data Science. I followed the following steps to learn the basics of Data Science. If you are interested to learn, can communicate with me or follow the following steps:
🚀 Step 1: Understand the Basics
What to Learn:
What is ML? Types: Supervised, Unsupervised, Reinforcement Learning.
Terminology: Features, labels, models, training/testing, overfitting, etc.
Applications: Image classification, recommendation systems, prediction, etc.
Resources:
Andrew Ng's ML Course (Coursera)
Google’s ML Crash Course
YouTube (StatQuest with Josh Starmer is amazing for intuition)
📊 Step 2: Brush Up on Prerequisites
Math:
Linear Algebra (Vectors, matrices, operations)
Probability & Statistics (Bayes’ theorem, distributions, expected value)
Calculus (Derivatives, gradients – especially for deep learning)
Programming:
Python is the go-to language. Learn libraries: NumPy, Pandas, Matplotlib, Seaborn.
Tip: Don’t get stuck here forever. Learn just enough and move forward.
🧪 Step 3: Learn Core ML Algorithms
Start with Supervised Learning:
Linear/Logistic Regression
Decision Trees, Random Forests
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Naive Bayes
Then explore Unsupervised Learning:
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Finally, Intro to Neural Networks
Practice: Use scikit-learn to build models and test them.
📁 Step 4: Work with Real Data
Kaggle: Join competitions or work on datasets (Titanic, Housing Prices, etc.)
Clean and preprocess data: Handle missing values, encode categorical data, normalize features, etc.
Split your data: Train/Test/Validation
🧠 Step 5: Go Deeper into Special Topics
Model Evaluation: Confusion matrix, precision, recall, F1-score, ROC-AUC
Feature Engineering and Selection
Hyperparameter Tuning: Grid Search, Random Search, Cross-validation
Dimensionality Reduction
Ensemble Methods: Boosting (XGBoost, LightGBM), Bagging
🧱 Step 6: Learn Deep Learning Basics
Neural Networks, Activation Functions, Backpropagation
Frameworks: TensorFlow or PyTorch
CNNs, RNNs, LSTMs (for image and sequential data)
🔬 Step 7: Apply to Projects or Research
Build projects (prediction systems, classification tools, etc.)
Work on domain-specific ML (e.g., health, finance, NLP)
If you’re into research: start reading ML papers (arXiv, Google Scholar)
📚 Bonus: Stay Updated & Network
Follow AI/ML researchers on Twitter or LinkedIn
Join communities: Kaggle, Reddit (r/MachineLearning), GitHub
Subscribe to newsletters (e.g., “The Batch” by Andrew Ng)
