What will you in Machine Learning, Data Science and Generative AI with Python Course
- Python Programming: Master Python essentials, including variables, data types, control flow, and functions.
- Data Analysis & Manipulation: Utilize libraries like NumPy and Pandas for data cleaning, transformation, and analysis.
- Data Visualization: Create compelling visualizations using Matplotlib, Seaborn, and Plotly.
- Machine Learning Algorithms: Implement algorithms such as Linear Regression, K-Nearest Neighbors, Decision Trees, Random Forests, and Support Vector Machines using Scikit-Learn.
- Natural Language Processing (NLP): Develop spam filters and text classification models.
- Deep Learning: Explore neural networks and Convolutional Neural Networks (CNNs) for image classification tasks.
Program Overview
Introduction to Python for Data Science
⏳ 1 hour
Setting up the Python environment.
Basic Python syntax and data structures.
Data Analysis with Pandas & NumPy
⏳ 2 hours
Data cleaning and preprocessing.
Exploratory Data Analysis (EDA).
Handling missing data and outliers.
Data Visualization Techniques
⏳ 1.5 hours
Creating static and interactive plots.
Visualizing distributions, correlations, and trends.
Supervised Learning Algorithms
⏳ 3 hours
Implementing and understanding Linear Regression, K-Nearest Neighbors, Decision Trees, and Random Forests.
Evaluating model performance using metrics like accuracy, precision, recall, and F1-score.
Unsupervised Learning Techniques
⏳ 2 hours
Applying K-Means Clustering and Hierarchical Clustering.
Dimensionality reduction using PCA.
Natural Language Processing (NLP)
⏳ 2 hours
Text preprocessing and tokenization.
Building spam filters and text classification models.
Deep Learning with Neural Networks
⏳ 3 hours
Understanding the basics of neural networks.
Implementing CNNs for image classification tasks.
Model Deployment & Best Practices
⏳ 1 hour
Saving and loading models.
Deploying models for real-world applications.
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Job Outlook
High Demand for Data Science Skills: Data Science and Machine Learning are among the most sought-after skills in the tech industry.
Career Opportunities: Proficiency in Python and machine learning opens doors to roles such as Data Scientist, Machine Learning Engineer, and AI Specialist.
Industry Adoption: Companies across various sectors, including finance, healthcare, and e-commerce, leverage data science for decision-making and automation.
Specification: Machine Learning, Data Science and Generative AI with Python
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FAQs
- No prior programming or data science knowledge required.
- Beginner-friendly step-by-step instruction in Python.
- Covers fundamentals of Machine Learning, Data Science, and Generative AI.
- Provides hands-on projects for practical learning.
- Suitable for learners from any background aiming to enter AI/ML fields.
- Python programming essentials including data types, functions, and control flow.
- Data analysis and manipulation using NumPy and Pandas.
- Data visualization with Matplotlib, Seaborn, and Plotly.
- Implement ML algorithms like Linear Regression, Decision Trees, Random Forests, and KNN.
- Build NLP and deep learning models and deploy them for real-world applications.
- Prepares learners for AI, ML, and Data Science careers.
- Provides practical, portfolio-ready projects.
- Demonstrates proficiency in Python and machine learning frameworks.
- Builds foundational knowledge for advanced ML and AI studies.
- Enhances employability in tech, finance, healthcare, and e-commerce sectors.
- Modules include Python, Data Analysis, Data Visualization, Supervised & Unsupervised Learning, NLP, Deep Learning, and Model Deployment.
- Self-paced online learning with lifetime access.
- Hands-on projects to reinforce concepts.
- Clear step-by-step guidance suitable for beginners.
- Combines theory with practical application for comprehensive learning.
- Self-paced with lifetime access to materials.
- Estimated completion: 12–15 hours depending on practice.
- Certificate awarded upon finishing the course.
- Certificate can be added to resumes and LinkedIn profiles.
- Demonstrates practical skills in Python, ML, and AI professionally.
