10 days





Course features:


Lab Setup:

Learning Path

  • Overview of Python language features and syntax
  • Data types, variables, and operators
  • Control structures: if statements, loops
  • Introduction to NumPy arrays and basic operations
  • Working with Pandas DataFrames for data manipulation
  • Loading and inspecting datasets with Pandas
  • Introduction to Matplotlib and Seaborn libraries for data visualization
  • Creating line plots, scatter plots, histograms, and bar charts
  • Customizing plots and adding annotations
  • Analyzing and visualizing a real-world dataset using Python and data science libraries
  • Exploratory data analysis (EDA) to gain insights into the data
  • Presenting findings and insights from the analysis
  • Handling missing values and outliers in datasets
  • Data normalization and standardization
  • Feature engineering techniques for creating new features
  • Advanced EDA techniques for exploring relationships between variables
  • Correlation analysis and heatmap visualization
  • Dimensionality reduction techniques: PCA, t-SNE
  • Introduction to statistical tests and hypothesis testing
  • Performing statistical tests using SciPy library
  • Interpreting and analyzing results of statistical tests
  • Performing advanced data analysis tasks on the previously explored dataset
  • Applying statistical techniques to validate findings and hypotheses
  • Documenting and presenting the complete data analysis process
  • Overview of machine learning concepts and terminology
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Machine learning workflow: data preprocessing, model training, evaluation, and deployment
  • Introduction to supervised learning algorithms: regression and classification
  • Linear regression, logistic regression, decision trees, and k-nearest neighbors (KNN)
  • Training and evaluating supervised learning models using Scikit-learn
  • Cross-validation techniques for model evaluation
  • Metrics for evaluating regression and classification models
  • Hyperparameter tuning to optimize model performance
  • Building and training supervised learning models on a real-world dataset
  • Evaluating model performance using appropriate metrics
  • Tuning hyperparameters to improve model accuracy and generalization
  • Introduction to ensemble learning and ensemble methods
  • Bagging, boosting, and stacking algorithms
  • Implementing ensemble learning models with Scikit-learn
  • Introduction to unsupervised learning: clustering and dimensionality reduction
  • K-means clustering, hierarchical clustering, and DBSCAN
  • Principal Component Analysis (PCA) for dimensionality reduction
  • Overview of deep learning concepts and neural networks
  • Building blocks of neural networks: neurons, layers, and activation functions
  • Introduction to deep learning frameworks: TensorFlow and Keras
  • Building and training deep learning models using TensorFlow and Keras
  • Implementing common deep learning architectures: feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs)
  • Evaluating and fine-tuning deep learning models for improved performance
  • Overview of artificial intelligence and its applications
  • History and evolution of AI
  • Types of AI: narrow AI, general AI, and superintelligence
  • Introduction to NLP and its applications
  • Text preprocessing techniques: tokenization, stemming, lemmatization
  • Building NLP models for text classification and sentiment analysis
  • Overview of computer vision and its applications
  • Image preprocessing techniques: resizing, normalization, and augmentation
  • Building computer vision models for image classification and object detection
  • Building AI applications using natural language processing and computer vision techniques
  • Implementing AI models to solve real-world problems
  • Presenting and demonstrating AI projects to the class
  • Reinforcement learning: concepts and algorithms
  • Time series analysis and forecasting techniques
  • Advanced deep learning architectures: generative adversarial networks (GANs), recurrent neural networks (RNNs), and transformers
  • Participants work on advanced projects incorporating concepts learned in the previous sessions
  • Guidance and mentoring provided by instructors for project implementation and troubleshooting
  • Participants collaborate with peers on project development
  • Code reviews, discussions, and knowledge sharing sessions
  • Participants present their projects to the class and instructors
  • Projects are evaluated based on creativity, technical complexity, and practical applicability
  • Feedback provided to participants for further improvement and learning