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Advanced Machine Learning and Deep Learning
AdvancedFree LearningMachine Learning

Advanced Machine Learning and Deep Learning

by UptoSkills Team

Missions

14

Quests

82

Games

25

XP

750

Coins

35

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
Certificate

Key Benefits

Earn XP & Level Up

Gain 750 XP to unlock badges and achievements

Collect Coins

Earn 35 coins to redeem rewards

Professional Certificate

QR-verified and recruiter-checkable — ₹499, or ₹199 on Pro

Unlock Achievements

Complete challenges and earn exclusive badges

About This League

This comprehensive league delves into advanced machine learning and deep learning concepts, equipping students with cutting-edge techniques. It covers sophisticated algorithms and practical implementation strategies essential for modern AI applications. The curriculum is designed for individuals aiming to excel in competitive placement and internship opportunities.

What you will learn

Upon completion, you will be able to:

  • Implement advanced regression and classification models using ensemble methods and gradient boosting.
  • Apply unsupervised learning techniques for clustering, dimensionality reduction, and anomaly detection.
  • Build and train complex deep learning models including CNNs, RNNs, and Transformers for diverse tasks.
  • Develop generative models using GANs and explore reinforcement learning for sequential decision-making.
  • Interpret and explain complex ML models using XAI techniques and deploy them into production environments.

What this league covers

ModuleWhat it covers
Module 1: Foundations & Python Optimization for MLOptimizing NumPy, Pandas, and Cython for ML; essential linear algebra refresher and Python profiling.
Module 2: Advanced Regression TechniquesPolynomial regression, Ridge, Lasso, Elastic Net, and advanced evaluation metrics for regression problems.
Module 3: Advanced Classification TechniquesSVMs, kernel methods, ensemble techniques like XGBoost, and strategies for imbalanced datasets.
Module 4: Unsupervised Learning TechniquesAdvanced clustering (DBSCAN), dimensionality reduction (PCA, t-SNE), and anomaly detection (Isolation Forest).
Module 5: Deep Learning FundamentalsNeural network architectures, backpropagation, optimization algorithms, and regularization with TensorFlow/Keras.
Module 6: Convolutional Neural Networks (CNNs)CNN architectures (ResNet), object detection (YOLO), image segmentation (U-Net), and transfer learning.
Module 7: Recurrent Neural Networks (RNNs)LSTMs, GRUs, sequence-to-sequence models, attention mechanisms, and time series analysis with RNNs.
Module 8: Natural Language Processing (NLP)Word embeddings (Word2Vec), Transformers (BERT, GPT), sentiment analysis, and question answering.
Module 9: Generative Adversarial Networks (GANs)GAN theory, DCGANs, WGANs, conditional GANs, and applications in image generation and style transfer.
Module 10: Reinforcement Learning (RL)Markov Decision Processes, Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods.
Module 11: Bayesian Machine LearningBayesian inference, Gaussian Processes, Variational Inference, and Markov Chain Monte Carlo (MCMC) methods.
Module 12: Time Series Analysis and ForecastingARIMA models, State Space Models (Kalman Filters), and deep learning approaches for time series.
Module 13: Model Interpretability and Explainability (XAI)Feature importance, SHAP values, LIME, and partial dependence plots for understanding model decisions.
Module 14: Productionizing Machine Learning ModelsModel deployment with Docker and Kubernetes, MLOps pipelines, and model monitoring strategies.

Students will be prepared for MLOps Fundamentals or further specialization in Machine Learning with Python.

A look inside

Advanced Machine Learning and Deep Learning screen 1Advanced Machine Learning and Deep Learning screen 2Advanced Machine Learning and Deep Learning screen 3Advanced Machine Learning and Deep Learning screen 4

Prerequisites

  • Comfortable writing Python, including lists, dictionaries and functions.
  • Understand supervised learning, training/test splits and overfitting.
  • Comfortable with linear algebra and calculus notation.
  • Have trained at least one model end to end.

Learning Objectives

  • Implement and evaluate advanced deep learning models, including transformers and generative adversarial networks (GANs), using TensorFlow or PyTorch for complex tasks such as natural language processing and image synthesis, achieving state-of-the-art performance metrics.
  • Analyze and compare the theoretical foundations and practical limitations of various advanced machine learning algorithms, such as Bayesian optimization, reinforcement learning, and graph neural networks, justifying the choice of algorithm for specific real-world problems.
  • Create and optimize custom machine learning pipelines using advanced feature engineering techniques, hyperparameter tuning strategies, and model ensembling methods to maximize predictive accuracy and robustness on high-dimensional and noisy datasets.
  • Understand and apply advanced techniques for addressing common challenges in machine learning, including handling imbalanced datasets, mitigating bias and fairness issues, and ensuring model interpretability and explainability.
  • Develop and implement distributed machine learning solutions using cloud-based platforms such as AWS SageMaker or Google Cloud AI Platform, capable of scaling to handle large datasets and complex models efficiently.
  • Evaluate and critique published research papers in advanced machine learning, identifying novel contributions, limitations, and potential areas for future research, and presenting the findings effectively.