Machine Learning Data Science And Deep Learning With Python
 
In today's competitive job market, the demand for machine learning and data science expertise is at an all-time high. According to reports from Glassdoor and Indeed, data scientists are among the top-paid technology professionals, with an average salary of $120,000. Python, renowned for its user-friendly nature, has emerged as the language of choice for many data scientists. Its widespread adoption drives revolutionary advancements in the field, including deep learning, making it indispensable for data mining, artificial intelligence, and various analytical techniques highly prized by businesses and employers.

The course "Machine Learning, Data Science, and Deep Learning with Python" is meticulously crafted to impart the techniques employed by actual data scientists and machine learning practitioners in the tech industry. Led by expert instructor Frank Kane, who draws from his nine-year tenure at Amazon and IMDb, it equips you with the essential skills needed to excel in this rapidly evolving career path. The course curriculum is derived from analyzing real-world requirements from data scientist job listings at major tech companies.

Covering a comprehensive range of skills sought after by employers, the course encompasses topics such as data visualization in Python using Matplotlib and Seaborn, transfer learning, sentiment analysis, experimental design, and A/B testing. Each concept is presented in simple, accessible language and reinforced through practical demonstrations using Python code that you can experiment with and extend. For those new to Python, the course commences with crash courses designed to facilitate a smooth transition into the language.

What you will learn:

- Construct artificial neural networks utilizing Tensorflow and Keras.
- Perform predictions using linear regression, polynomial regression, and multivariate regression techniques.
- Implement machine learning on a large scale using Apache Spark's MLLib.
- Develop a movie recommender system employing item-based and user-based collaborative filtering.
- Classify images, data, and sentiments using deep learning methodologies.