Data Science

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Data Science

Artificial Intelligence (AI) and Machine Learning (ML) are transformative technologies that are reshaping industries by enabling machines to perform tasks that typically require human intelligence. While AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart,” ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make decisions based on data.

Python has become a key player in these technologies due to its simplicity, rich libraries, and strong community. Beyond AI and ML, Python excels in Analytics and Automation, making it a go-to tool for data scientists and developers.

In Analytics, Python supports data collection, cleaning, and analysis using libraries like pandas, numpy, and matplotlib. Advanced tools like scikit-learn and statsmodels enable predictive and prescriptive analytics, empowering organizations to make data-driven decisions.

In Automation, Python simplifies repetitive tasks such as workflows, web scraping, and database management. Tools like selenium, pyautogui, and apscheduler help businesses boost efficiency and save time.

With its combined strengths in Analytics, Automation, and AI/ML, Python drives innovation and optimizes processes, creating smarter and more efficient systems.

Training Modes:

Online Training
Corporate Training
Video Training

Training Highlights:

  • Unlock AI potential with machine learning, deep learning, and NLP.
  • Master Python libraries: TensorFlow, PyTorch, scikit-learn.
  • Apply AI in computer vision: facial recognition, medical imaging.
  • Explore neural networks: basics to advanced transformers and GNNs.
  • Transform industries: AI tools for healthcare, finance, gaming.
  • Learn reinforcement learning for robotics and autonomous systems.
  • Ensure ethical AI: fairness, transparency, accountability.
  • Dive into generative AI: StyleGAN, DALL-E, neural style transfer.
  • Integrate big data and AI: Hadoop, Spark, PySpark.
  • Build AI apps with AWS, Azure, and Google Cloud.
  • Advance with self-supervised, federated, and quantum AI.
  • Showcase skills with capstone projects like fraud detection and recommendations.

Data Science

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Technical FAQs & Mastery Modules

Module 1 : Python With AI

  • Python data types and structures (lists, tuples, dictionaries, sets)
  • Control structures: Loops and conditionals
  • Functions, modules, and libraries
  • File handling and working with datasets

  • Linear algebra: Vectors, matrices, and matrix operations
  • Probability and statistics: Distributions, Bayes theorem, hypothesis testing
  • Calculus basics: Derivatives, gradients, optimization
  • Graph theory basics for AI

  • Overview of popular libraries: numpy, pandas, matplotlib, scipy
  • Machine learning with scikit-learn
  • Deep learning with TensorFlow, Keras, and PyTorch
  • Specialized libraries for AI: nltk, spaCy, OpenCV

  • Types of machine learning: Supervised, unsupervised, reinforcement
  • Key algorithms: Linear regression, decision trees, k-NN, SVMs
  • Training and testing datasets
  • Evaluation metrics: Accuracy, precision, recall, F1 score, ROC-AUC

  • Introduction to neural networks
  • Activation functions: Sigmoid, ReLU, softmax
  • Optimizers: SGD, Adam, RMSProp
  • Architectures: Feedforward, convolutional (CNN), recurrent (RNN, LSTM, GRU)
  • Transfer learning and fine-tuning pre-trained models

  • Text preprocessing: Tokenization, stemming, lemmatization
  • Word embeddings: Word2Vec, GloVe, FastText
  • Sentiment analysis, named entity recognition, and topic modeling
  • Transformer architectures: BERT, GPT, T5
  • Chatbot development and conversational AI

  • Image preprocessing and augmentation
  • Feature extraction with CNNs
  • Object detection: YOLO, SSD, Faster R-CNN
  • Image segmentation: U-Net, Mask R-CNN
  • Applications: Facial recognition, OCR, medical imaging

  • Basics of reinforcement learning (RL)
  • Markov decision processes (MDPs)
  • Q-learning and Deep Q-Networks (DQNs)
  • Policy gradient methods
  • Applications of RL: Game AI, robotics, resource optimization

  • Generative Adversarial Networks (GANs)
  • Autoencoders and variational autoencoders (VAEs)
  • Attention mechanisms and transformers
  • Self-supervised and semi-supervised learning
  • Explainable AI (XAI)

  • Understanding sequential data
  • Time series forecasting with ARIMA, LSTM, GRU
  • Anomaly detection in time series
  • Real-world applications: Stock market predictions, weather forecasting

  • Integrating AI with big data technologies (Hadoop, Spark)
  • Distributed computing with PySpark
  • Handling large-scale datasets for AI
  • Case studies in big data and AI

  • Using cloud platforms for AI (AWS, Azure, Google Cloud)
  • Training and deploying models on the cloud
  • AutoML and cloud-based AI services
  • Real-time AI with serverless architecture

  • Ethical considerations in AI
  • Detecting and mitigating biases in AI systems
  • Ensuring fairness, transparency, and accountability in AI models
  • Privacy-preserving AI with federated learning

  • Model serialization and deployment using Flask and FastAPI
  • Using Docker and Kubernetes for scalable AI solutions
  • Monitoring model performance post-deployment
  • Handling model drift and retraining

  • Basics of robotics and autonomous systems
  • Integration of AI with robotic systems
  • Path planning and navigation
  • Real-time decision-making with RL

  • Stream processing with Kafka and Spark Streaming
  • Real-time NLP applications (chatbots, real-time translation)
  • Real-time computer vision (surveillance, motion detection)

  • MLOps tools: MLflow, TensorFlow Extended (TFX)
  • AutoML frameworks: H2O, Auto-Sklearn, TPOT
  • Visualization tools for AI workflows
  • AI pipelines with Apache Airflow

  • Healthcare: Disease diagnosis, drug discovery, patient risk prediction
  • Finance: Fraud detection, credit scoring, algorithmic trading
  • Retail: Personalized recommendations, demand forecasting
  • Manufacturing: Predictive maintenance, quality control
  • Gaming: Game AI, procedural content generation

  • Build a personalized recommendation system
  • Develop an AI-powered chatbot
  • Create a computer vision application (e.g., image classifier or object detector)
  • Implement a real-time AI solution for fraud detection or anomaly detection
  • Design a reinforcement learning agent for a game

  • Advanced Q-learning: Double Q-learning, Dueling DQNs
  • Actor-Critic methods: A3C, A2C
  • Proximal Policy Optimization (PPO)
  • Deep Deterministic Policy Gradient (DDPG)
  • Multi-agent reinforcement learning

  • Introduction to federated learning and its applications
  • Privacy-preserving AI techniques
  • Implementing federated learning with frameworks like PySyft
  • Case studies: Healthcare data, financial applications

  • Tools for model interpretability: SHAP, LIME
  • Visualizing feature importance in deep learning models
  • Building interpretable models: Decision trees, rule-based systems
  • Case studies: Transparent decision-making in AI

  • Evolutionary algorithms for neural network optimization
  • Genetic algorithms and particle swarm optimization (PSO)
  • Neuroevolution of augmenting topologies (NEAT)
  • Applications: Game AI, dynamic model generation

  • Capsule Networks (CapsNet)
  • Vision Transformers (ViT)
  • Self-attention in neural networks
  • Graph Neural Networks (GNN) for structured data
  • Energy-based models

  • Understanding zero-shot and few-shot paradigms
  • Pre-trained models for zero-shot learning (e.g., GPT, CLIP)
  • Few-shot learning using meta-learning (MAML, ProtoNets)
  • Applications: Multilingual NLP, rare class prediction

  • Combining text, image, and audio inputs in AI models
  • Multimodal transformers and applications
  • Building models with mixed data sources
  • Applications: Video captioning, emotion recognition

  • Optimizing AI models for edge devices
  • Frameworks: TensorFlow Lite, PyTorch Mobile
  • Deploying AI on microcontrollers and IoT devices
  • Case studies: Real-time AI in wearables, smart home devices

  • Dependency parsing and coreference resolution
  • Large language models (LLMs): Fine-tuning GPT, BERT, LLaMA
  • Summarization techniques: Abstractive vs extractive
  • Advanced text generation with transformers
  • Multilingual NLP and translation models

  • Audio preprocessing techniques
  • Speech-to-text and text-to-speech models
  • Building conversational AI with voice assistants
  • Audio classification and generation (e.g., WaveNet)
  • Applications: Real-time transcription, music generation

  • Techniques for identifying bias in datasets
  • Fairness-aware machine learning algorithms
  • Implementing debiasing techniques in AI models
  • Ethical dilemmas in autonomous systems

  • Advanced GAN architectures: StyleGAN, CycleGAN, BigGAN
  • Text-to-image generation (e.g., DALL-E, Stable Diffusion)
  • Neural style transfer and artistic generation
  • Generative applications in music, design, and gaming

  • Advanced robotic perception and vision
  • AI for motion planning and control
  • Reinforcement learning in robotics
  • Applications: Autonomous vehicles, industrial robots

  • Basics of quantum computing for AI
  • Quantum algorithms for machine learning
  • Implementing AI models on quantum simulators (Qiskit, Cirq)
  • Use cases: Optimization, cryptography

  • Concepts of lifelong learning in AI
  • Techniques for incremental learning without catastrophic forgetting
  • Applications in robotics and real-time systems

  • Deep learning on graph data using GNNs
  • Applications: Social networks, molecular graphs, recommendation systems
  • Tools and frameworks: DGL, PyTorch Geometric

  • Contrastive learning techniques (SimCLR, MoCo)
  • Pretext tasks for self-supervised learning
  • Applications in image, text, and speech data

  • AI-generated art and music (e.g., Magenta, OpenAI Jukebox)
  • Story and content generation using language models
  • Applications in creative industries: Video production, game design

  • Optimization problems in AI: Scheduling, routing
  • Solving problems with evolutionary and heuristic methods
  • AI for financial optimization and resource allocation

  • Continuous integration/continuous deployment (CI/CD) for AI
  • Deploying AI models in Kubernetes clusters
  • API-based AI model deployment with Flask and FastAPI
  • Advanced model monitoring and logging

  • AI for disaster management and crisis response
  • Applications in agriculture: Crop prediction, pest detection
  • Public health analytics with AI
  • AI in environmental sustainability

  • AI applications in genomics and proteomics
  • Predicting protein structures with AI (AlphaFold)
  • Drug discovery and personalized medicine using AI
  • Analyzing biological networks

  • Video segmentation and object tracking
  • Real-time action recognition
  • Applications: Surveillance, sports analytics, autonomous driving

  • Neural architecture search (NAS)
  • Model compression and pruning
  • Knowledge distillation techniques
  • Efficient model inference for real-time applications

  • Building AI for autonomous vehicles and drones
  • Sensor fusion techniques in autonomous systems
  • Decision-making in dynamic environments

  • Decentralized AI models
  • Federated learning and blockchain integration
  • Applications in secure and transparent AI systems

  • Building recommendation systems (content-based, collaborative filtering)
  • Dynamic user profiling using AI
  • Real-time personalization in e-commerce and media

  • AI for fraud detection in financial systems
  • Advanced cybersecurity applications with AI
  • AI for scientific discovery (astronomy, physics, chemistry)
  • Building interactive AI-driven gaming systems

Module 2 : Python With Data Analytics

  • Python data types and structures
  • Control flow: Loops and conditionals
  • Functions and modular programming
  • File handling: Reading from and writing to files

  • DataFrames and Series: Basics and operations
  • Importing datasets: CSV, Excel, SQL, JSON
  • Data cleaning: Handling missing values, duplicates, and outliers
  • Data transformation: Filtering, sorting, and grouping
  • Advanced operations: Merging, joining, and pivot tables

  • Array creation and manipulation
  • Element-wise operations and broadcasting
  • Aggregation, statistics, and linear algebra
  • Handling multi-dimensional arrays

  • Basic plotting with matplotlib: Line plots, bar charts, histograms
  • Statistical plots with seaborn: Pair plots, heatmaps, box plots
  • Interactive visualizations with plotly and bokeh
  • Dashboard creation with dash

  • Understanding data distributions
  • Summary statistics: Mean, median, variance, standard deviation
  • Correlation and pairwise relationships
  • Detecting trends, patterns, and anomalies

  • Handling missing data: Imputation techniques
  • Encoding categorical variables: Label and one-hot encoding
  • Feature scaling: Normalization and standardization
  • Data discretization and transformation

  • Descriptive statistics
  • Probability distributions
  • Hypothesis testing: T-tests, Chi-square tests, ANOVA
  • Confidence intervals and p-values

  • Understanding time series data
  • Time series decomposition: Trend, seasonality, and residuals
  • Moving averages and exponential smoothing
  • Forecasting techniques: ARIMA, SARIMA

  • Grouping and summarizing data
  • Aggregation functions: sum, mean, count, etc.
  • Applying custom functions to groups
  • Pivot tables and cross-tabulations

  • Introduction to supervised and unsupervised learning
  • Regression analysis: Linear and logistic regression
  • Classification techniques: Decision trees, k-Nearest Neighbors
  • Clustering: K-means and hierarchical clustering

  • Creating geographical maps with geopandas and folium
  • Network graphs using networkx
  • Advanced statistical visualizations with statsmodels
  • Word clouds and text-based visualizations

  • Introduction to big data and distributed computing
  • Working with PySpark for large-scale data processing
  • Integration with Hadoop and HDFS
  • Distributed computing with Dask

  • Introduction to cloud platforms: AWS, Azure, Google Cloud
  • Using Python with cloud services (e.g., boto3 for AWS)
  • Data storage and retrieval from cloud databases
  • Deploying data analytics pipelines in the cloud

  • Text preprocessing: Tokenization, stemming, lemmatization
  • Text representation: Bag-of-Words, TF-IDF
  • Sentiment analysis and topic modeling
  • Word embeddings: Word2Vec, GloVe

  • Streaming data processing with Kafka
  • Real-time dashboards with Streamlit and Dash
  • Analyzing live data feeds
  • Use cases: Stock market analysis, IoT data

  • Handling imbalanced datasets
  • Dimensionality reduction: PCA, t-SNE
  • Time series forecasting with Facebook Prophet
  • Automation of analytics workflows

  • SQL and Python integration with sqlite3, psycopg2
  • Querying databases and retrieving data for analysis
  • Working with NoSQL databases like MongoDB
  • Managing and analyzing large datasets with SQL

  • Automating analytics tasks with Python scripts
  • Generating reports in Excel, PDF, and Word formats
  • Email automation for sharing analytics reports
  • Building automated dashboards with Python

  • Using statsmodels for statistical modeling
  • Data pipelines with Apache Airflow
  • Working with Tableau and Python integrations
  • AutoML for automating analytics and ML tasks

  • Healthcare: Patient data analysis, trend prediction
  • Finance: Fraud detection, stock trend analysis
  • Retail: Customer segmentation, sales forecasting
  • Social Media: Sentiment analysis, engagement prediction

  • Data privacy and compliance (GDPR, HIPAA)
  • Avoiding biases in data analysis
  • Ethical considerations in data visualization and reporting

  • Building an interactive sales dashboard
  • Analyzing and forecasting stock market data
  • Social media sentiment analysis for a brand
  • Creating an automated reporting tool

  • Designing ETL pipelines using Python (pandas, pyodbc, sqlalchemy)
  • Automating ETL tasks with airflow and luigi
  • Working with APIs to fetch data (requests, BeautifulSoup, Selenium)
  • Integrating data from multiple sources (databases, web APIs, flat files)

  • Regression analysis: Ridge, Lasso, and ElasticNet
  • Time series stationarity and unit root tests (ADF, KPSS)
  • Multivariate statistical analysis (MANOVA, Canonical Correlation Analysis)
  • Structural Equation Modeling (SEM)

  • Introduction to geospatial data
  • Visualizing geospatial data with folium and geopandas
  • Spatial joins and geoprocessing tasks
  • Heatmaps, choropleths, and point clustering
  • Case studies: Retail store location optimization, traffic analysis

  • Fourier Transform for signal processing
  • Spectral analysis and frequency domain insights
  • Using deep learning models (RNNs, LSTMs) for time series
  • Handling irregular and multivariate time series data

  • Connecting Python with BI tools like Tableau, Power BI
  • Using Python scripts for advanced Tableau analytics
  • Creating data pipelines for BI dashboards
  • Embedding visualizations into business reports

  • Parallel processing with multiprocessing and joblib
  • Distributed computing with Dask for analytics
  • Graph analytics with graph-tool and networkx
  • Working with large datasets in HDF5, Parquet, and Feather formats

  • Web scraping for data extraction (BeautifulSoup, Scrapy, Selenium)
  • Social media analytics with APIs (Twitter, Facebook, LinkedIn)
  • Sentiment analysis on social media platforms
  • Tracking and analyzing trends using real-time feeds

  • Automating file downloads and data processing
  • Scheduling analytics scripts with cron jobs or Task Scheduler
  • Building robust workflows with retry mechanisms and logging
  • Workflow orchestration with Apache Airflow

  • Detecting and handling multicollinearity
  • Outlier detection using statistical and machine learning methods
  • Advanced feature extraction techniques
  • Encoding high-cardinality categorical features

  • Regression models for prediction (Linear, Logistic, and Polynomial)
  • Time series prediction with machine learning models
  • Model evaluation for predictive analytics (R², RMSE, MAE)
  • Ensemble models for better predictions (Bagging, Boosting)

  • Introduction to causal inference in data analytics
  • Establishing causation using experiments and observational data
  • Propensity score matching
  • Applications in marketing and healthcare

  • AutoML tools for data analytics (H2O, Auto-Sklearn)
  • Using scikit-learn for advanced analytics pipelines
  • Ensemble learning methods for robust analytics models
  • Active learning and model retraining on new data

  • Techniques for analyzing customer behavior
  • Lifetime Value (LTV) calculations for businesses
  • Building pricing models using data analytics
  • Identifying upsell and cross-sell opportunities with analytics

  • Understanding data governance principles
  • Data cleaning and ensuring accuracy in analytics pipelines
  • Data encryption and secure data sharing
  • Ensuring compliance with GDPR and other regulations

  • Building real-time analytics dashboards with Streamlit or Dash
  • Processing streaming data with Kafka and Spark Streaming
  • Monitoring IoT data in real-time
  • Applications: Fraud detection, dynamic pricing, live traffic updates

  • Building decision trees and flowcharts
  • Risk analysis and Monte Carlo simulations
  • Scenario modeling and what-if analysis
  • Presenting analytics results for executive decision-making

  • Designing data warehouses for analytics
  • Working with OLAP cubes in Python
  • Building analytics pipelines with cloud-based data warehouses (Snowflake, BigQuery)
  • Using pyathena to query data in Amazon Athena

  • Using PyCaret for automating ML workflows
  • Interactive analytics with Voila
  • Using DuckDB and ClickHouse for fast data queries
  • Exploring TensorFlow Data Validation for data insights

  • Avoiding biases in data preprocessing and analysis
  • Ensuring transparency in model decision-making
  • Ethical challenges in data collection and usage
  • Case studies: Ethical dilemmas in analytics

  • Healthcare: Patient risk profiling, healthcare cost analysis
  • Retail: Customer churn prediction, basket analysis
  • Manufacturing: Predictive maintenance, process optimization
  • Education: Student performance prediction, resource allocation

  • Analyzing and visualizing open government data
  • Predicting customer churn for a subscription service
  • Building a recommendation engine for an e-commerce platform
  • Developing a real-time traffic monitoring system

Module 3 : Python Machine Learning Data Analytics

  • Data types and structures: Lists, Tuples, Dictionaries, Sets
  • Control flow and loops (if-else, for, while)
  • Functions, lambda expressions, and comprehensions
  • File handling and working with external data

  • Loading and saving datasets (CSV, Excel, JSON, SQL)
  • DataFrame operations: Selection, filtering, and transformation
  • Handling missing data and duplicates
  • Aggregation, grouping, and pivot tables

  • Introduction to data visualization
  • Plotting with matplotlib: Line plots, bar charts, scatter plots
  • Advanced visualizations with seaborn: Heatmaps, pairplots, and violin plots
  • Interactive visualizations with plotly and dash

  • Understanding data distributions and outliers
  • Descriptive statistics: Mean, median, variance, standard deviation
  • Correlation analysis and pairwise relationships
  • Dimensionality reduction techniques (PCA, t-SNE)

  • Handling missing and incorrect data
  • Data encoding: Label encoding and one-hot encoding
  • Feature scaling: Standardization and normalization
  • Feature engineering and extraction

  • What is Machine Learning? (Supervised, Unsupervised, Reinforcement Learning)
  • Workflow of a Machine Learning project
  • Model evaluation metrics: Accuracy, precision, recall, F1-score, and ROC-AUC

  • Regression: Linear regression, polynomial regression, Ridge, Lasso
  • Classification: Logistic regression, k-Nearest Neighbors, Decision Trees, Random Forests, SVM
  • Model training, testing, and validation
  • Hyperparameter tuning with GridSearchCV

  • Clustering: K-means, Hierarchical Clustering, DBSCAN
  • Dimensionality reduction: PCA, ICA
  • Anomaly detection methods
  • Applications of unsupervised learning in real-world problems

  • Ensemble learning: Bagging, Boosting (AdaBoost, Gradient Boosting, XGBoost)
  • Neural networks introduction (basic concepts, forward and backpropagation)
  • Deep Learning basics with TensorFlow and PyTorch

  • Understanding time series data
  • Decomposition of time series: Trend, seasonality, and residual
  • Time series forecasting with ARIMA and SARIMA
  • Machine learning models for time series (LSTM, Prophet)

  • Text preprocessing: Tokenization, stemming, and lemmatization
  • Text vectorization techniques: Bag-of-Words, TF-IDF
  • Sentiment analysis and topic modeling
  • Word embeddings (Word2Vec, GloVe) and transformers

  • Introduction to big data and distributed systems
  • Using PySpark for big data analysis
  • Data pipeline creation with dask
  • Integration with Hadoop and HDFS

  • Introduction to deep learning and neural networks
  • Building a neural network with TensorFlow/Keras
  • Convolutional Neural Networks (CNNs) for image classification
  • Recurrent Neural Networks (RNNs) for sequence data

  • Hypothesis testing and statistical inference
  • Data sampling techniques and population analysis
  • A/B testing for decision-making
  • Building dashboards with streamlit

  • Introduction to model deployment
  • Saving and loading ML models (pickle, joblib)
  • Building APIs for models using Flask or FastAPI
  • Deploying models on cloud platforms (AWS, Azure, GCP)

  • Streaming data processing with Kafka
  • Analyzing real-time data with Spark Streaming
  • Building a real-time analytics dashboard

  • Predictive analytics for business decisions
  • Customer segmentation using clustering
  • Sentiment analysis of social media data
  • Building a recommendation system

  • Using statsmodels for advanced statistical modeling
  • Automating workflows with Airflow
  • Using AutoML tools like H2O.ai and Auto-Sklearn
  • Exploring advanced frameworks: Hugging Face for NLP

  • Understanding biases in data and models
  • Model interpretability with SHAP and LIME
  • Fairness and ethics in AI applications

  • Handling high-dimensional datasets
  • Merging, joining, and concatenating complex datasets
  • Working with hierarchical indices and multi-index DataFrames
  • Dealing with real-world messy datasets (inconsistent formats, missing labels)

  • Advanced feature selection techniques (RFE, mutual information, chi-square test)
  • Feature generation: Polynomial features, interaction terms, log transformations
  • Handling imbalanced datasets (SMOTE, ADASYN)
  • Encoding categorical variables: Target encoding, frequency encoding

  • Creating dashboards with plotly and dash
  • Interactive visualizations with bokeh
  • Network graph visualizations (networkx, graph-tool)
  • Geospatial analytics and mapping (folium, geopandas)

  • Collaborative filtering (user-based, item-based)
  • Content-based filtering using NLP techniques
  • Hybrid recommendation systems
  • Case study: Building a movie or product recommendation system

  • Bayesian inference and probability distributions
  • Hidden Markov Models (HMMs) for time series and NLP
  • Gaussian Mixture Models (GMMs)
  • Applications of probabilistic models in real-world problems

  • Sentiment analysis using advanced models like BERT
  • Transformer architectures (BERT, GPT, T5)
  • Building question-answering systems
  • Text summarization and generation

  • Transfer learning with pre-trained models (ResNet, VGG, Inception)
  • Generative Adversarial Networks (GANs)
  • Autoencoders for anomaly detection and feature learning
  • Attention mechanisms in deep learning

  • Image preprocessing and augmentation techniques
  • Object detection with YOLO and Faster R-CNN
  • Semantic segmentation (U-Net, Mask R-CNN)
  • Real-world applications: Face recognition, OCR, medical imaging

  • Seasonal decomposition with advanced methods
  • Long Short-Term Memory (LSTM) for sequential modeling
  • Using attention mechanisms in time series analysis
  • Forecasting with Facebook Prophet and ML-based models

  • Basics of reinforcement learning
  • Understanding Markov Decision Processes (MDP)
  • Q-learning and Deep Q-Networks (DQN)
  • Applications of RL in games, robotics, and resource optimization

  • Introduction to AutoML tools (H2O, Auto-Sklearn, TPOT)
  • Advanced hyperparameter tuning techniques (Bayesian Optimization, Optuna)
  • Using Ray Tune for distributed tuning
  • Automating model selection and training pipelines

  • Understanding black-box models
  • Using SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations)
  • Model interpretability in neural networks (Grad-CAM, integrated gradients)
  • Case studies in XAI applications

  • Advanced PySpark for large-scale data processing
  • Distributed ML with Dask-ML
  • Working with HDFS and Hadoop ecosystems using Python
  • Integration with Spark MLlib for big data analytics

  • ROC-AUC and Precision-Recall tradeoffs
  • Cross-validation techniques: Stratified K-Fold, Leave-One-Out
  • Model performance on imbalanced datasets
  • Cost-sensitive learning and evaluation

  • Real-time ML pipelines with Kafka and Spark Streaming
  • Streaming model inference with TensorFlow Serving
  • Handling live data in ML models
  • Building real-time fraud detection systems

  • Hypothesis testing in depth: ANOVA, Chi-square, T-tests
  • Bayesian statistical models with pymc3
  • Time series stationarity tests (ADF, KPSS)
  • Multi-collinearity and variance inflation factor (VIF)

  • Deployment with Docker and Kubernetes
  • CI/CD pipelines for ML with Jenkins, GitLab CI
  • Monitoring deployed models with Prometheus and Grafana
  • Handling model drift and retraining workflows

  • Ethical considerations in data collection and ML models
  • Bias detection and mitigation in datasets
  • Ensuring fairness in predictions and decision-making
  • Privacy-preserving AI with federated learning

  • Using Snorkel for automated data labeling
  • MLOps with MLflow and TensorFlow Extended (TFX)
  • Federated learning frameworks (PySyft, TensorFlow Federated)
  • Real-time graph analysis with GraphFrames

  • Healthcare: Predicting patient outcomes, medical image analysis
  • Finance: Fraud detection, risk modeling, stock price prediction
  • Retail: Customer segmentation, demand forecasting
  • Manufacturing: Predictive maintenance, quality control

Module 4 : Automation With Python

  • File handling in Python (Reading, Writing, and Appending)
  • Automating file operations (Renaming, Moving, and Copying)
  • Working with directories (Creating, Deleting, and Navigating)
  • Using Python’s os and shutil modules for automation

  • Introduction to web scraping and its applications
  • Working with libraries: BeautifulSoup and lxml
  • Automating web page navigation with Selenium
  • Extracting and storing data (CSV, Excel, JSON)

  • Sending emails using Python (smtplib)
  • Reading and organizing emails with imaplib
  • Automating email attachments and filtering
  • Integrating email automation with other tasks

  • Introduction to APIs and RESTful services
  • Making HTTP requests with requests
  • Automating API calls and data retrieval
  • Parsing JSON and XML data for automation tasks

  • Working with spreadsheets using openpyxl and pandas
  • Automating data analysis and processing tasks
  • Generating automated reports
  • Automating charts and graphs

  • Scheduling tasks with schedule and APScheduler
  • Automating repetitive tasks using Python scripts
  • Running Python scripts as cron jobs or on Windows Task Scheduler

  • Browser automation with Selenium
  • Automating form filling, file uploads, and downloads
  • Handling dynamic web pages and JavaScript-based content
  • Automating social media or other web-based applications

  • Connecting to databases with sqlite3, pyodbc, or SQLAlchemy
  • Automating CRUD operations
  • Integrating database automation with other tasks
  • Generating database reports

  • Working with system commands using subprocess and os
  • Automating system monitoring tasks (CPU, RAM, Disk usage)
  • User and file permission automation
  • Backups and log file management

  • Working with images using Pillow
  • Automating PDF tasks with PyPDF2 and pdfplumber
  • Extracting text, merging, splitting, and creating PDFs
  • Adding watermarks or annotations

  • Automating data cleaning with pandas
  • Automating data visualization with matplotlib and seaborn
  • Automating machine learning workflows using scikit-learn and mlflow

  • Network automation basics
  • Automating SSH connections using paramiko
  • Automating file transfers with ftplib
  • Monitoring and logging network activity

  • Using pyautogui for GUI automation
  • Automating desktop apps with pywinauto or PyQt
  • Workflow automation with Airflow
  • Managing concurrent tasks with multiprocessing and threading

  • Writing testable automation scripts
  • Debugging common automation issues
  • Using unittest and pytest for testing scripts

  • Automating a file organization system
  • Automating a data scraping and analysis pipeline
  • Automating a reporting dashboard generation
  • Automating a personal assistant script

  • Writing efficient and maintainable scripts
  • Handling sensitive data and credentials securely
  • Avoiding anti-scraping measures and respecting web scraping policies
  • Ensuring scalability and reusability of automation scripts

  • Automating AWS, Azure, or GCP services using SDKs (boto3, azure, google-cloud)
  • Infrastructure as code with Terraform and Python
  • Automating CI/CD pipelines with Python (Jenkins, GitHub Actions)
  • Serverless automation with AWS Lambda and Python

  • Controlling IoT devices with Python (paho-mqtt, socket)
  • Automating hardware using pyserial and Raspberry Pi
  • Building IoT automation workflows using Python and cloud integrations
  • Automating sensor data logging and processing

  • Scraping JavaScript-heavy websites using playwright
  • Managing captchas with automation tools like anticaptcha
  • Distributed web scraping with scrapy
  • Avoiding detection with user-agent rotation and proxy management

  • Automating stock market data retrieval (yfinance, alpaca)
  • Automating invoice generation and payment tracking
  • Automating financial data visualization and reporting
  • Building automated trading bots

  • Automating penetration testing with Python (nmap, scapy)
  • Vulnerability scanning and report generation
  • Automating password recovery or brute-force testing (with ethical considerations)
  • Writing automated malware analysis scripts

  • Automating natural language processing tasks (NLTK, spaCy, transformers)
  • Building chatbots with Python (ChatterBot, Rasa)
  • Automating image recognition tasks with TensorFlow and PyTorch
  • Generating automated responses or summaries using AI models

  • Automating document generation with docx and reportlab
  • Converting and processing document formats (PDF to Word, Word to PDF)
  • Automating repetitive office tasks with Python scripts
  • Integration with Google Docs and Microsoft Office APIs

  • Game bot creation with pyautogui and pynput
  • Automating gameplay scenarios and testing
  • Writing AI-based automation for game strategies
  • Integrating automation in multiplayer games (ethical considerations)

  • Automating video editing with Python (moviepy)
  • Automating music playlist management (yt_dlp, spotipy)
  • Automating social media content posting (tweepy, facebook-sdk)
  • Building a YouTube downloader and converter

  • Automating live data streams using WebSockets
  • Processing real-time data with Kafka and Spark Streaming
  • Building real-time dashboards with Python (dash, streamlit)
  • Automating tasks in video conferencing platforms (Zoom, MS Teams)

  • Automating medical data analysis and reporting
  • Automating appointment scheduling systems
  • Processing patient data and generating insights with Python
  • Building an automated alert system for patient monitoring

  • Automating ML pipeline with tools like MLflow and dvc
  • Automating hyperparameter tuning (Optuna, GridSearchCV)
  • Automating model deployment using Flask, FastAPI, or Streamlit
  • Building an automated retraining system for models

  • Automating cryptocurrency trading bots (ccxt)
  • Interacting with blockchain APIs (web3.py)
  • Automating wallet transactions and management
  • Monitoring and analyzing blockchain data

  • Automating e-learning platform management (grading, student tracking)
  • Building interactive learning tools and quizzes with Python
  • Automating research data collection and organization
  • Integration with learning management systems (LMS) APIs

  • Automating task creation and tracking in platforms like Jira and Trello
  • Generating automated project reports and timelines
  • Integrating Python with project management tools using APIs
  • Workflow automation for project updates and team notifications

  • Automating document review and generation (contracts, forms)
  • Monitoring regulatory updates with Python web scraping
  • Automating case law retrieval and analysis
  • Integrating compliance checks in workflows

  • Automating Git operations with Python (GitPython)
  • Writing scripts for branch management, commit checks, and pull requests
  • Automating repository backups and version management
  • Monitoring and analyzing code repositories

  • E-commerce: Automating product price tracking and inventory updates
  • Logistics: Automating shipment tracking and route optimization
  • Banking: Automating report generation and fraud detection
  • Retail: Automating POS (point-of-sale) systems and customer insights

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