Data science and machine learning
Do you want to learn the number #1 programming language that powers the internet? Are you tired…
- 0
- 30h 20m
- 10
Step into the world of data with our comprehensive Data Science and Machine Learning course, designed to take you from fundamentals to advanced AI-driven solutions. Whether you’re a beginner or aiming to advance your skills, this program blends theoretical knowledge with real-world application.
You’ll explore core topics like data analysis, statistics, data visualization, and predictive modeling. Then, deepen your understanding with hands-on experience in machine learning algorithms, Python programming, and essential tools like Pandas, scikit-learn, TensorFlow, and Matplotlib.
Throughout the course, you’ll work with real-life datasets across sectors like healthcare, finance, and marketing, building strong practical skills and decision-making confidence. Our step-by-step guidance, live coding sessions, and capstone projects ensure you graduate with not only knowledge but also a portfolio that proves your expertise.
By the end, you’ll be equipped to build intelligent models, draw insights from data, and pursue careers in data science, AI, or analytics with confidence.
What Will You Learn?
- Data analysis, cleaning, and visualization
- Supervised & unsupervised machine learning algorithms
- Model evaluation & hyperparameter tuning
- Deep learning fundamentals with TensorFlow
- Real-life project work using Python and Pandas
Course Curriculum
Learn the Concepts
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Get introduced to Data Science & ML fundamentals, types of data, lifecycle, and Python essentials for analytics.
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Perform EDA, handle missing values & outliers, and explore tools like WEKA and MATLAB for preprocessing.
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Visualize data using Matplotlib & Seaborn; dive into supervised learning and ML math foundations.
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Understand probabilistic models, optimization, hyperparameter tuning, and cross-validation techniques.
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Explore unsupervised learning methods like clustering and dimensionality reduction, plus reinforcement learning basics.
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Work on predictive analytics, NLP, text processing, and time series forecasting techniques.
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Learn image/video processing with OpenCV, neural networks, CNNs, RNNs, and IoT-driven analytics.
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Master databases, SQL, and deploy ML models using Flask, FastAPI, Streamlit, and cloud platforms (AWS, Azure).
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Build your Data Science portfolio with projects on GitHub and Kaggle to showcase your skills.
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Tackle real-world ML problems through Kaggle and HackerRank competitions.
Practice What You Learn
Quizzes & Assessments
Real-World Projects
Internship / Capstone Project
Final Exam & Report Submission
Certificate of Completion
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