Artificial Intelligence Course Content
Artificial Intelligence Online Course Module:
The Artificial Intelligence training course curriculum has been intended to provide you with the most up-to-date knowledge and abilities. By taking advantage of the opportunity to learn hands-on coding with supervision and feedback from our mentors, you can become an Artificial Intelligence expert. Because all of our trainers and mentors are accomplished professionals, you will be learning from the finest in the area.. You can find the complete course details in below-mentioned modules:
Course Curriculum
Module 1:Python and linux basics
- Understanding the linux os
- Basics of python and linux libraries and functions
Module 2:Data Wrangling with SQL
- Introduction to SQL
- Database normalization and entity-relationship model
- SQL operators
- Working with SQL: Join, tables, and variables
- Deep dive into SQL
- functions
- Working with Subqueries
- SQL views, functions, and stored procedures
Module 3:GIT
- What is Version Control?
- Types of Version Control System
- Introduction to SVN
- Introduction to Git
- Git Lifecycle
- Common Git commands
- Working with branches in Git
- Merging branches
- Resolving merge conflicts
- Git workflow
Module 4:Advanced statistics
- Central tendency
- Variability
- Hypothesis testing
- Anova
- Correlation
- Regression
- Probability definitions and notation
- Joint probabilities
- The sum rule, conditional probability, and the product rule
- Bayes theorem
Module 5:Python with data science
- Introduction to Data Science using Python
- Python basic constructs
- Math for DS-Statistics & Probability
- OOPs in Python
- NumPy for mathematical computing
- SciPy for scientific computing
- Data manipulation
- Data visualization with Matplotlib
Module 6:Machine learning with python
- Machine Learning using Python
- Supervised learning
- Unsupervised learning
- Dimensionality reduction
- Time-series forecasting
Module 7:AI and deep learning
- Introduction to Deep Learning and Neural Networks
- Multi-layered Neural Networks
- Artificial Neural Networks and various methods
- Deep Learning libraries
Module 8:Deploying machine learning models on cloud
- Why and when we need MLOps\AI pipelines
- Training, tuning, and serving on AI platform
- Kubeflow pipelines on AI platform
- CI/CD for Kube Flow pipelines
Module 9:Data visualization with power BI
- Introduction to Power BI
- Data Extraction
- Data Transformation – Shaping & Combining Data
- Data Modelling & DAX
- Data Visualisation with analytics
- Power BI Service (Cloud), Q & A, and Data Insights
- Power BI Settings, Administration & Direct Connectivity
- Embedded Power BI with API & Power BI
- Power BI Advance & Power BI Premium
Module 10:Data science at Scale with pySpark
- Introduction to Big Data and Apache Spark
- Apache Spark framework and RDDs
- PySpark SQL and Data Frames
- Introduction to Hive
Module 11:Data analysis with MS Excel
- Entering data
- Referencing in formulas
- Name range
- Understanding logical functions & conditional formatting
- Important formulas in Excel
- Working with Dynamic table
- Data transformation for analysis
- Working with charts for data visualization
- Pivot tables in Excel
- Working with Macros in Excel and working with VBA
Module 12: Conclusion
Summarize all the points discussed.
