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| Item Details | Price | ||
|---|---|---|---|
| Instructor: UpSkill Campus |
Language: English |
In today’s data-driven world, organizations rely on data insights to make informed decisions and drive innovation. The Data Science and Machine Learning Training Program equips learners with essential skills in data analysis, statistics, and programming to build predictive models and solve real-world problems.
The program covers Python for data analysis, data preprocessing, visualization, and exploratory data analysis (EDA) using NumPy, Pandas, Matplotlib, and Seaborn. Learners then explore supervised and unsupervised machine learning techniques such as regression, decision trees, clustering, SVMs, and neural networks.
Advanced topics include deep learning, NLP, and model deployment using tools like Scikit-learn, TensorFlow, and Keras. Through hands-on projects and real-world case studies, participants gain practical experience in building, evaluating, and deploying machine learning models across various domains.
Get life long access to all the courses with unlimited learning.
Our curriculum is designed by experts to make sure you get the best learning experience.
Interact and network with like-minded folks from various backgrounds in exclusive chat groups.
Stuck on something? Discuss it with your peers and the instructors in the inbuilt chat groups.
With the quizzes and live tests practice what you learned, and track your class performance.
Flaunt your skills with course certificates. You can showcase the certificates on LinkedIn with a click.
Get familiar with tools like Python, Jupyter Notebook, and basic data handling.
Understand common algorithms like Linear Regression and Classification.
Analyze real-world Data Science and Machine Learning projects. Prepare and submit a report.
Study the provided eBook covering key concepts in Data Science and Machine Learning.
Understand basic statistics concepts such as mean, median, variance, and probability
Build and submit a final project on GitHub demonstrating your learning.