Basics

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• Statistics 101

• Python 101

• Introduction to Data Science with Python

• Exploratory Data Analysis (EDA)

• Data Visualization & Information Analysis

• Data Munging

• Feature Engineering

Machine Learning

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• Introduction to Machine Learning

• Supervised Learning, Unsupervised Learning, Reinforcement Learning

• Linear, Polynomial, Ridge, Lasso Regression, ElasticNet, Extra Tree Regressors

• Intermediate Machine Learning

• Classification and clustering

• Naive Bayes, Bernoulli, Decision Tree Classifiers

• Advanced Machine Learning, Anomaly Detection

• Metrics to evaluate predictions

• Support Vector Machines

• Principal Component Analysis (PCA)

• Train Test Split, K-Folds, Accuracy, Recall, Confusion Matrix

Artificial Intelligence (AI)

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• NLP - Natural Language Processing and sentiment analysis

• Big Data with Hadoop and Spark

• Recommendation Systems

  • Collaborative Filtering

  • Content-Based Filtering

  • Hybrid Recommender Systems

  • Matrix Factorization

  • Deep Learning in Recommender Systems

  • Cold Start Problem

  • Real-World Applications

• Computer Vision with OpenCV - Face Recognition

• Deep Learning, Tensorflow - Time Series

• Artificial Neural Networks (ANN)

• Deep neural networks (DNN)

• Convolutional Neural Networks (CNN)

• Popular CNN Architectures: LeNet-5, AlexNet, VGGNet, GoogLeNet, ResNet

• Recurrent Neural Networks (RNN)

• Generative Adversarial Networks (GAN) for Generative Modeling

• Long Short-Term Memory (LSTM)

• 3D Computer Vision and Depth Estimation

• Social Network Analysis, Page Ranking

• Graph Theory

• Automated Computer Vision

Automated Machine Learning (AutoML)

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• Pycaret: PyCaret simplifies the machine learning workflow and accelerates the model development process by automating several steps

• AutoKeras: It automates the time-consuming process of manual model selection and hyperparameter tuning

• H2O.ai: Offers AutoML capabilities through its H2O AutoML component

• Dask-ML: Integrates with Dask to provide distributed computing capabilities

Reinforcement Learning (RL)

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• Introduction to Reinforcement Learning: Concepts and Algorithms

• Applications and Challenges

• Exploration vs. Exploitation

• Policy Gradient Methods

• Sequential Decision Making

• Multi-agent Reinforcement Learning

Geographic Information Systems (GIS)

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• Introduction to Geographic Information Systems (GIS)

• Spatial Data Analysis

• Geospatial Data Visualization

• Web GIS and Cloud-Based Mapping

• Remote Sensing and GIS Integration

Quantum Machine Learning (QML)

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• Quantum Computing

• QML algorithms

• Qiskit Aqua

• IBM Quantum Experience

Transfer Learning and Transformers

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• Leveraging Pretrained Models for Faster and More Accurate Machine Learning

• Inductive transfer learning

• Transductive transfer learning

• Unsupervised transfer learning

• Common Applications of Transfer Learning

• Best Practices for Transfer Learning

• Transformers and their role in NLP

• Self-attention mechanism

• Transformers vs. Recurrent Neural Networks (RNNs)

Large Language Models (LLMs)

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• A Deep Dive into Large Language Models

• Language Generation at Scale

• Prompt Engineering

• Next-Level Natural Language Processing

• Creative Text Generation with Large Language Models

Machine Learning Operations (MLOps)

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• Bridging the Gap Between Machine Learning and Operations

• Building End-to-End ML Pipelines

• Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning

• Data Governance and Compliance in MLOps

• Version control, Containers, Docker and Kubernates

• Building Intelligent Mobile Apps

• Cross-Platform ML Model Deployment