How you will learn
1.
Learning modelA 3 -week experiential learning and hands-on training session
2.
Training MethodologyLearn through real-life business cases and work on live projects
3.
Alumni NetworkJoin an ecosystem of talents and connect with leading employers.
Learn Artificial Intelligence and machine learning best practices Live
AI engineers are responsible for developing new applications and systems that utilize AI to improve performance and efficiency, make better decisions, cut costs and increase profits. Their jobs can focus on applying logic, probability analysis, and machine-learning concepts to problem-solving initiatives.
They also analyze systems to effectively monitor and control development projects. By 2030, AI could contribute up to $15.7 trillion to the global economy, which is more than China and India’s combined output today, according to PricewaterhouseCoopers’ Global Artificial Intelligence Study. This projected growth means organizations are turning to AI to help power their business decisions and increase efficiency.
Salary and Job Outlook
Globally, AI engineering is a specialized field with promising career growth and tends to pay extremely well. According to Glassdoor, the annual median base salary for an AI engineer is $101,991 in the United States. While in Nigeria the annual median base salary for an AI engineer is ₦5m according to Salary Explorer.
AI engineers typically work for tech companies, helping them to improve their products, software, operations, and delivery. More and more, they may also be employed in government and research facilities that work to improve public services. Hiring growth for artificial intelligence specialists, including engineers, has grown 74 percent annually for the past four years, according to LinkedIn’s 2020 Emerging Jobs report.
What you will learn
Module 1
Introduction to data science and statistics
Uncover the power of data science and statistics in this complete and easy to follow step-by-step module. You will learn how to create surveys, prepare and analyze data for analysis as well as how to draw conclusions and have profits from the results of your data analysis. Furthermore, you’ll learn how to predict or explain different behaviors and events, how to collect data, visualize data and how to tell stories through data.
This module is designed to provide you with in-depth knowledge on these:
Introduction to statistics and methods
Data description
Probability
Estimation
Outliers and normal distribution analysis
Outliers and normal distribution analysis
Foundations of Inferential statistics
Mean comparisons
Correlation, regression and non-parametrics
Vectors and intersections
Visualizing relationships in data
Module 2
Data Visualization with Python
Write your first Python program by implementing the concepts of variables, strings, functions, loops, and conditions. Understand the nuances of lists, sets, dictionaries, conditions and branching, objects, and classes. Work with data in Python, including reading and writing files, loading, working, and saving data with Pandas.
This module is designed to provide you with in-depth knowledge on these:
System environment setup
Introduction to python
Python variables and data types
Python conditional statements
Python functions, classes and objects
Python inheritance, iterators and scope
Python dates, math and Json
Python file handling
Module 3
Python Programming for Data Visualization and ML
Data visualization plays an essential role in the representation of both small and large-scale data. In this Data Visualization with Python course, you will learn how to create impressive graphics and charts and customize them to make them more productive and more pleasing to your audience. You will gain expertise in several data visualization libraries in Python, namely Matplotlib and Seaborn to extract information, better understand the data, and make more effective decisions. Learn data visualization and best practices when creating plots and visuals Master basic plotting with Matplotlib. Generate different visualization tools using Matplotlib such as line plots, area plots, histograms, bar charts, box plots, and pie charts Understand Seaborn, a data visualization library in Python, and how to use it to create attractive statistical graphics. Understand Folium and how to use it to create maps and visualize geospatial data.
This module is designed to provide you with in-depth knowledge on these:
Introduction to Visualization Tools
Introduction to Jupiter notebooks and anaconda
Basic Visualization Tools
Specialized Visualization Tools
Advanced Visualization Tools
Creating Maps and Visualizing Geospatial Data
Statistical Computing
Introduction to Numpy python library
Data analysis Pandas python library
Plotting and visualization with Matplotlib python library
Modelling with scikit learn
Deep Learning with Keras
Earn a Nano Degree in Artificial Intelligence
Upon completing [AI] Academy’s AI Incubator Program, you'll receive an industry-recognized, professional certification to share with your network and showcase all that you've learned. AI Academy certificates are formatted for sharing on LinkedIn.
