Learn Python & SQL for AI, Data Science, and Database Manipulation - Course

Learn To Program in Python with Support

8 Weekly Live Classes 

• Start from no programming knowledge at all to writing in Python
• Leverage the power of Python to solve tasks.
• Use Python for your work problems or personal projects.
• Learn to use Python professionally
• Learn Jupyter Notebook and create .py files
• Learn to create a graphical user interface (GUI)

Data Science Programming you will learn

• Use Python for Data Science and Machine Learning
• Use Spark for Big Data Analysis
• Implement Machine Learning Algorithms
• Learn to use NumPy for Numerical Data
• Learn to use Pandas for Data Analysis
• Learn to use Matplotlib for Python Plotting
• Learn to use Seaborn for statistical plots
• Use Plotly for interactive dynamic visualizations
• Use SciKit-Learn for Machine Learning Tasks
• K-Means Clustering
• Logistic Regression
• Linear Regression
• Random Forest and Decision Trees
• Natural Language Processing and Spam Filters
• Neural Networks
• Support Vector Machines

Disciplines Program Topics
This program helps introduce basic applications of AI to those in business. While participants learn about AI’s current capabilities and potential, they also gain more depth with attention to the reach of automation, machine learning, and robotics.

•  Introduction – AI and Business -Gain a high-level overview of technologies including, capabilities and limitations while understanding AI, its methods, and business applications.
•  The Future of AI in Business - Identify what new problems AI can help solve, how it will continue to transform businesses and how you can prepare for the future of AI.
•  Machine Learning Basics - Learn about supervised and unsupervised learning and the importance of obtaining and managing data for machine learning.
•  Key Applications: Computer Vision & Natural Language Processing - Master how machines see and talk, computer vision, and natural language processing as well as more on recent developments like GANs and RNNs.
•  Neural Networks and Deep Learning - Understand the transition from traditional machine learning to neural networks deep learning and its common applications.