Data Science - Data Science and Machine Learning with Python
About the Course
Course Objectives:
Learn how to use Python for data analysis and machine learning
Master popular Python libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow
Understand data preprocessing techniques to clean and prepare data for analysis
Apply machine learning algorithms to solve real-world problems and build predictive models
Visualize data and present insights using tools like Matplotlib and Seaborn
Learn advanced topics in AI, including neural networks and deep learning
Who Should Enroll: This course is ideal for aspiring data scientists, software engineers, analysts, and anyone interested in leveraging Python for data-driven decision-making. Whether you are a beginner in programming or have some experience in Python, this course will provide you with the skills to excel in the rapidly growing field of data science and machine learning.
Course Outline:
Introduction to Data Science and Python:
Understanding the role of data science in business and technology
Introduction to Python programming for data analysis
Setting up Python environment with Jupyter notebooks, Anaconda, and IDEs
Data Preprocessing and Cleaning:
Importing, cleaning, and transforming data with Pandas
Handling missing data, outliers, and categorical variables
Data normalization and scaling techniques
Exploratory Data Analysis (EDA):
Statistical analysis and hypothesis testing
Visualizing data distributions and relationships using Matplotlib and Seaborn
Identifying trends and patterns through data exploration
Data Visualization Techniques:
Creating data visualizations with Matplotlib and Seaborn
Plotting time series, histograms, bar charts, and heatmaps
Using advanced visualization tools like Plotly and Tableau for interactive data analysis
Introduction to Machine Learning:
Understanding the fundamentals of machine learning
Difference between supervised, unsupervised, and reinforcement learning
Building and evaluating machine learning models with Scikit-learn
Supervised Learning Algorithms:
Implementing regression models: Linear Regression, Logistic Regression
Understanding decision trees, random forests, and gradient boosting
Model evaluation techniques: Cross-validation, confusion matrix, and ROC curves
Unsupervised Learning Algorithms:
Clustering techniques: K-Means, Hierarchical Clustering, DBSCAN
Dimensionality reduction using PCA (Principal Component Analysis)
Association rule learning and anomaly detection
Deep Learning and Neural Networks:
Introduction to deep learning and artificial neural networks (ANN)
Building neural networks with TensorFlow and Keras
Understanding backpropagation and training deep learning models
Natural Language Processing (NLP):
Introduction to NLP and text processing techniques
Tokenization, stemming, lemmatization, and vectorization
Building text classification and sentiment analysis models
Model Deployment and Integration:
Exporting machine learning models for production
Creating APIs to deploy models using Flask or FastAPI
Cloud deployment of machine learning models on platforms like AWS, Azure, or Google Cloud
Capstone Project – Real-World Data Science Application:
Building a complete data science solution from data collection to model deployment
Applying machine learning to solve a real-world problem (e.g., sales prediction, recommendation systems, etc.)
Presenting the results and findings through data visualizations and reports
Benefits of the Course:
Gain a strong understanding of data science principles and machine learning techniques
Learn Python libraries and tools used by industry professionals
Work on real-world data science projects and develop a portfolio to showcase your skills
Certification upon successful completion to enhance your career opportunities
Access to continuous support and guidance from expert instructors