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AI/ML Roadmap

Beginner Level:

• Learn Python programming language
  • Variables, data types, and operators
  • Control flow and loops
  • Functions and modules
  • File handling and data input/output
• Introduction to Data Science
  • Data types and data structures
  • Exploratory data analysis (EDA)
  • Data visualization
  • Statistical concepts and summary statistics
• Fundamentals of Machine Learning
  • Supervised learning: regression and classification
  • Unsupervised learning: clustering and dimensionality reduction
  • Evaluation metrics and model validation
  • Feature engineering and selection
• Python Libraries for AI/ML
  • NumPy for numerical computing
  • Pandas for data manipulation and analysis
  • Scikit-learn for machine learning algorithms
  • Matplotlib and Seaborn for data visualization
• Hands-on Projects
  • Implementing linear regression
  • Building a decision tree classifier
  • Analyzing a dataset using Pandas
  • Creating basic data visualizations

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Intermediate Level:

• Advanced Machine Learning Algorithms
  • Ensemble methods: random forests, gradient boosting
  • Support vector machines (SVM)
  • Neural networks and deep learning
  • Reinforcement learning basics
• Deep Learning
  • Artificial neural networks (ANN)
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Transfer learning and fine-tuning
• Natural Language Processing (NLP)
  • Text preprocessing and tokenization
  • Sentiment analysis
  • Named entity recognition (NER)
  • Language modeling and generation
• Unsupervised Learning
  • Clustering algorithms: k-means, hierarchical clustering
  • Dimensionality reduction techniques: PCA, t-SNE
  • Association rule mining
  • Anomaly detection
• Big Data and Distributed Computing
  • Introduction to Apache Hadoop and MapReduce
  • Apache Spark fundamentals
  • Processing large datasets with Spark
  • Distributed machine learning with Spark MLlib

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Expert Level:

• Advanced Deep Learning
  • Advanced architectures: GANs, autoencoders
  • Sequence-to-sequence models
  • Reinforcement learning algorithms: Q-learning, policy gradients
  • Advanced optimization techniques
• Model Optimization and Hyperparameter Tuning
  • Hyperparameter tuning methods: grid search, random search
  • Bayesian optimization
  • Regularization techniques: L1, L2, dropout
  • Model performance evaluation and interpretation
  • Avoiding overfitting and underfitting
• Advanced Topics in NLP
  • Text summarization
  • Machine translation
  • Question answering systems
  • Transformer models: BERT, GPT
• Time Series Analysis
  • Time series data preprocessing
  • Time series forecasting models: ARIMA, SARIMA, Prophet
  • Long Short-Term Memory (LSTM) networks for time series
  • Handling seasonality and trends in time series data
• Real-world Applications
  • Deploying machine learning models in production
  • Scalability and performance considerations
  • Ethical and responsible AI
  • Stay updated with research papers and latest advancements