1. Natural Language Processing(NLP)
1.1. Text Preprocessing
1.1.1. Tokenization
1.1.2. Lemmatization
1.2. Feature Extraction
1.2.1. TF-IDF
1.2.2. Word2Vec
1.2.3. BERT
1.3. Sentiment Analysis
1.4. Named Entity Recognition (NER)
1.5. Chatbots & Conversational AI
2. BIG DATA & Cloud Computing
2.1. Big Data Tools
2.1.1. Hadoop
2.1.2. Spark
2.2. Cloud Services
2.2.1. AWS
2.2.2. Google Cloud
2.2.3. Azure
2.3. No SQL Databases
2.3.1. MongoDB
2.3.2. Cassandra
3. Model Evaluation & Deployment
3.1. Classification Metrics
3.1.1. Accuracy
3.1.2. Precision
3.1.3. Recall
3.1.4. F1 Score
3.2. Regression Metrics
3.2.1. MSE
3.2.2. RMSE
3.2.3. R-Squared
3.3. Cross-Validation & Hyperparameter tunning
3.4. Deployment
3.4.1. Flask
3.4.2. Fast API
3.4.3. Docker
3.4.4. CI/CD Pipelines
4. Applications
4.1. Health Care
4.1.1. Disease prediction
4.1.2. Medical Imaging
4.2. Finance
4.2.1. Fruad Detection
4.2.2. Stock Forecasting
4.3. Transportation
4.3.1. Traffic Prediction
4.3.2. Route Optimization
4.4. E-commerce
4.4.1. Recommendation Systems
4.5. Cyber security
4.5.1. Anomaly
4.5.2. Threat Prevention
5. Data Collection
5.1. Structured Data
5.1.1. Databases
5.1.2. Spreadsheets
5.2. Unstructured Data
5.2.1. Text
5.2.2. Images
5.2.3. Logs
5.3. APIs & WebScraping
5.4. Open Data Sources
5.4.1. Kaggle
5.4.2. Google Datasets
6. Data Preprocessing
6.1. Handling Missing Data
6.1.1. Imputation
6.1.2. Removal
6.2. Feature Engineering
6.2.1. Encoding
6.2.2. Scaling
6.3. Normalization and & Standardization
6.4. Outlier Detection & Treatment
7. Exploratory Data Analysis(EDA)
7.1. Statistical Summaries
7.1.1. Mean
7.1.2. Median
7.1.3. Variance
7.2. Data Visualization
7.2.1. Matplotlib
7.2.2. Seaborn
7.3. Correlation Analysis
7.4. Feature Selection
8. Machine Learning
8.1. Supervised Learning
8.1.1. Regression
8.1.1.1. Linear
8.1.1.2. Logistic
8.1.2. Classification
8.1.2.1. Decision
8.1.2.2. SVM
8.2. Unsupervised Learning
8.2.1. Clustering
8.2.1.1. K-means
8.2.1.2. DBSCAN
8.2.2. Reinforcement Learning
8.2.2.1. Q- Learning
8.2.2.2. Deep Q Networks
9. Deep Learning
9.1. Neural Networks
9.1.1. ANN
9.1.2. CNN
9.1.3. RNN
9.1.4. GANs
9.2. Frameworks
9.2.1. Tensor Flow
9.2.2. PyTorch
9.2.3. Keras
9.3. Activation Function
9.3.1. ReLU
9.3.2. Sigmoid