Description:
This course will provide you with a thorough understanding of Machine Learning concepts, terminology and usage. It would enable you to perform Machine Learning in two ways, namely using Python libraries and Apache Spark.
Long Description:
Master the art of Applied Machine Learning using Python and Apache Spark. This comprehensive course equips you with a deep understanding of Machine Learning concepts and practical usage. You’ll harness the power of Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for data analysis and preparation. Plus, explore Apache Spark’s distributed architecture to scale Machine Learning for large datasets using Spark MLlib. With hands-on projects and Databricks Notebooks, you’ll gain valuable experience and insights into both Python-based and Apache Spark-driven Machine Learning. Elevate your skills and choose the best tools for your data analysis needs with “Applied Machine Learning using Python and Apache Spark” training.
Course Code/Duration:
BDT10 / 3 Days
Learning Objectives:
After this course, you will be able to:
- Have a basic understanding of Machine Learning
- Understand the differences between Supervised and Unsupervised Learning
- Understand how to use Python libraries to explore, clean and prepare data
- Describe the role of Machine Learning and where it fits into Information Technology strategies
- Explain the technical and business drivers that result from using Machine Learning
- Understand techniques like Classification, Clustering and Regression
- Discuss how to identify which kinds of technique to be applied for specific use case
- Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Google Cloud ,Spark mlib, Python and R etc.
- Install and Setup Anaconda.
- Perform hands-on activities using Jupyter Notebooks.
- Understand the popular Machine Learning Algorithms like Linear Regression, Decision Tree, Logistic Regression, K Nearest Neighbor, K-Means clustering etc.
- Perform hands-on activity on Python libraries like NumPy, Pandas, Matplotlib and Scikit-learn
- Understand Apache Spark Processing Framework and distributed architecture
- Compare Machine learning using Python versus Apache Spark
- Perform hands-on activity on Databricks cloud using Apache Spark MLlib
