PYTHON DATA SCIENCE ML AI

Course description

Embark on a comprehensive journey into Python Data Science, Machine Learning, and Artificial Intelligence. This immersive course equips you with the skills needed to analyze data, build predictive models, and develop intelligent applications from scratch. Begin with Python fundamentals and quickly progress to mastering essential data science libraries like NumPy, Pandas, and Matplotlib, where you'll learn to process, visualize, and derive insights from data. Advance to machine learning techniques with scikit-learn, exploring supervised and unsupervised learning models such as regression, classification, clustering, and decision trees. Gain hands-on experience in real-world applications, from predicting trends to building recommendation systems. Dive deeper into neural networks and AI development with frameworks like TensorFlow and PyTorch, where you'll learn to create deep learning models, including convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for time-series data..

What you'll learn

Python Essentials for Data Science

• Mastering Python Basics: Data Types, Loops, Functions, and Classes
• Core Libraries:
• NumPy for Numerical Computing
• Pandas for Data Manipulation and Analysis
• Matplotlib and Seaborn for Data Visualization
• Advanced Python Concepts: List Comprehensions, Generators, and Decorators

Data Wrangling and Preprocessing

• Handling Missing Data and Outliers
• Feature Engineering and Scaling Techniques
• Dimensionality Reduction with PCA and t-SNE
• Data Cleaning and Transformation Using Pandas
• Working with Time Series Data

Exploratory Data Analysis (EDA)

• Creating Advanced Visualizations Using Matplotlib and Seaborn
• Statistical Analysis with Python: Correlation, Hypothesis Testing, and ANOVA
• Interactive Data Dashboards Using Plotly

Machine Learning Fundamentals

• Supervised Learning Techniques:
• Regression Models (Linear, Ridge, Lasso)
• Classification Models (Logistic Regression, SVM, Decision Trees)
• Unsupervised Learning Techniques:
• Clustering Algorithms (K-Means, DBSCAN)
• Dimensionality Reduction with PCA
• Model Evaluation and Hyperparameter Tuning:
• Cross-Validation and Grid Search

Advanced Machine Learning

• Ensemble Methods: Random Forest, Gradient Boosting (XGBoost, LightGBM)
• Feature Selection and Engineering for High-Performance Models
• Working with Imbalanced Datasets: SMOTE and Undersampling
• Building Pipelines for End-to-End ML Workflows

Deep Learning and AI

• Neural Network Basics: Perceptron, Backpropagation
• Building Deep Learning Models with TensorFlow and Keras
• Convolutional Neural Networks (CNNs) for Image Processing
• Recurrent Neural Networks (RNNs) for Time Series and Sequential Data
• Transfer Learning with Pre-trained Models

Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, Stop Words, Lemmatization
• Word Embeddings: Word2Vec, GloVe, FastText
• Sentiment Analysis and Topic Modeling
• Building Chatbots Using Transformer Models (BERT, GPT)
• Advanced NLP with Hugging Face

Computer Vision

• Image Preprocessing and Augmentation
• Object Detection with YOLO and SSD
• Semantic Segmentation and Image Style Transfer
• Building Real-Time Applications (e.g., Facial Recognition, OCR)

Data Pipelines and Big Data

• Data Pipelines with Apache Airflow
• Distributed Computing with Dask
• Handling Large Datasets with PySpark

Deployment and Model Serving

• Building RESTful APIs for ML Models with Flask/Django
• Model Serialization Using Pickle and ONNX
• Real-Time Model Deployment with FastAPI
• Deploying Models on Cloud Platforms:
• AWS Sagemaker, Google Cloud AI Platform
• Containerization with Docker

Performance Optimization and Monitoring

• Model Optimization with TensorFlow Lite
• Query Optimization in Big Data
• Monitoring Models with MLFlow
• Real-Time Logging and Debugging with ELK Stack

Task Automation and Scheduling

• Automating Workflows with Python Scripts
• Scheduling Tasks with Celery and Celery Beat
• Asynchronous Processing for Scalable Applications

Testing and Quality Assurance

• Writing Unit Tests for Data Pipelines and ML Models
• Test Automation for ML Pipelines Using PyTest
• A/B Testing for Model Performance Validation

Capstone Project: End-to-End Data Science Application

• Problem Definition and Dataset Selection
• Data Preprocessing and Exploratory Analysis
• Building and Tuning Predictive Models
• Integrating Models into a Web Application
• Deploying the Solution to Production
• Final Presentation and Review

Program details