This Data Science and Machine Learning with Python Masterclass is tailored for students familiar with Python and eager to deepen their expertise in utilizing it for data science and machine learning. With starting salaries for data scientists often exceeding $150,000, we've developed this course to equip students with a highly sought-after skill set in today's competitive job market.
Our curriculum covers the full spectrum of the data science and machine learning tech stack demanded by top-tier companies worldwide. It has propelled students into roles at esteemed firms like McKinsey, Facebook, Amazon, and Google. Drawing from our extensive experience in online and in-person instruction, we've meticulously structured the course to provide a clear and cohesive learning journey. Emphasizing the utilization of data science and machine learning libraries and the underlying principles driving their use, this course balances practical case studies and the mathematical theory underpinning machine learning algorithms.
What sets us apart is our dive into advanced machine learning algorithms often overlooked in other courses, such as sophisticated regularization methods and cutting-edge unsupervised learning techniques like DBSCAN.
Comparable to boot camps commanding thousands of dollars, our comprehensive curriculum encompasses the following:
- Python programming fundamentals
- NumPy for numerical computing in Python
- Extensive exploration of Pandas for comprehensive data analysis
- Proficiency in the Matplotlib programming library for data visualization
- In-depth understanding of seaborn for advanced data visualization
- Machine Learning with SciKit Learn, covering:
- Linear Regression
- Regularization techniques like Lasso, Ridge, and Elastic Net
- K Nearest Neighbors
- K Means Clustering
- Decision Trees and Random Forests
- Natural Language Processing
- Support Vector Machines
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Model deployment strategies
What you'll learn:
- Use data science and machine learning with Python.
- Create data pipeline workflows to analyze, visualize, and gain insights from data.
- Build a portfolio of data science projects with accurate world data.
- Analyze datasets and gain insights through data science.
- Master critical data science skills.
- Understand Machine Learning from top to bottom.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real-world case studies.
- Learn Pandas for data manipulation with Python.
- Create supervised machine learning algorithms to predict classes.
- Learn Matplotlib to create fully customized data visualizations with Python.
- Create regression machine learning algorithms to predict continuous values.
- Learn Seaborn to create beautiful statistical plots with Python.
- Construct a modern portfolio of data science and machine learning resume projects.
- Use Scikit-learn to apply powerful machine learning algorithms.
- Get set up quickly with the Anaconda data science stack environment.
- Learn best practices for real-world datasets.
- Understand the entire product workflow for the machine learning lifecycle.
- Explore how to deploy machine-learning models as interactive APIs.