24
Lessons
10 days
Duration
English
Language
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OBJECTIVEs:
- This bootcamp is designed to provide participants with a comprehensive understanding of Python for various applications in data science, machine learning (ML), artificial intelligence (AI), and deep learning (DL). Participants will learn essential Python programming skills and explore libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and Keras to analyze data, build ML models, and develop AI applications.
- This 10-day course outline provides a structured curriculum covering essential topics in Python for data science, machine learning, artificial intelligence, and deep learning. With a combination of lectures, hands-on exercises, and project work, participants will gain practical skills and experience to apply Python effectively in various domains.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Basic Programming Knowledge: Participants should have a basic understanding of programming concepts such as variables, data types, control structures (e.g., loops, conditionals), and functions. Prior experience with any programming language is beneficial.
- Familiarity with Mathematics: A fundamental understanding of mathematics concepts such as algebra, calculus, and statistics is recommended for understanding ML and AI algorithms.
- Access to a Computer: Participants should have access to a computer with internet connectivity throughout the bootcamp. The computer should meet the minimum requirements for running Python and related software.
Lab Setup:
- Python Environment: Participants should have Python installed on their computers. They can install Python from the official Python website (https://www.python.org/) or using package managers like Anaconda or Miniconda.
- Integrated Development Environment (IDE): Participants can use any Python IDE of their choice for writing and running code. Recommended IDEs include PyCharm, Jupyter Notebook, and Google Colab.
- Data Science Libraries: Participants should have the necessary Python libraries installed for data science, machine learning, and deep learning. These include NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. They can install these libraries using pip or conda.
- Access to Datasets: Sample datasets should be provided for participants to practice data analysis, odel building, and training. Datasets can be obtained from public repositories such as Kaggle, UCI Machine Learning Repository, or built-in datasets in libraries like Scikit-learn.
Learning Path
- Day 1: Introduction to Python for Data Science
- 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
- Day 2: Advanced Data Analysis Techniques
- 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
- Day 3: Introduction to Machine Learning
- 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
- Day 4: Advanced Machine Learning Techniques
- 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
- Day 5: Introduction to Artificial Intelligence
- 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
- Day 6-10: Advanced Topics and Project Work
- 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