AI & ML
The AI/ML Training Program is designed to provide participants with a comprehensive understanding of Artificial
Intelligence (AI) and Machine Learning (ML) concepts, tools, and applications. This program combines theoretical
knowledge with practical skills, preparing individuals to tackle real-world challenges using AI and ML
techniques.
Code:
NES_SK_22569
Duration: 240 Hrs / 6 months / Customized
Mode: Corporate (Instructor Led Online / Onsite), Individual (Instructor Led Online)
Module 1- Python Programming Fundamentals
- Starting Python Using the interpreter Running a Python script
- Python scripts on UNIX/Windows Editors and IDEs
- Using variables Built-in functions Strings
- About flow control White space Conditional expressions While loops Alternate loop exits
- File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw
(binary)
data
Module 2 - Data Analysis with Python
- Data Science Fundamentals
- Data Operations with Numpy
- Data Manipulation with Pandas
Module 3 - Data Visualization
Techniques
- Introduction to Data Visualization
- Data Visualization using Matplotlib
- Hands-on Pandas for Rapid Visualization
- Seaborn for Data Visualization
Module 4 - EDA & Data Storytelling
- Introduction to Exploratory Data Analysis
- EDA Framework Deep Dive Scientific
- Exploration of Industry Data
Module 5 - Machine Learning
Foundation
- Why is Machine learning important?
- Statistical learning vs. Machine learning
- Common Machine Learning Applications
- How Do You Create a Machine Learning Algorithm?
- Iteration and evaluation
- Introduction to Machine Learning
- Linear Regression
- Logistic Regression
- Model Evaluation Techniques
Module 6 - Machine Learning
Intermediate
- Decision Trees
- Random Forests
- Dimentionality Reduction using PCA
- Naive Bayes Classifier
Module 7 - Machine Learning with
Python
- Why use python for machine learning?
- The building of environment on python for machine learning
- Choice of libraries
- Scientific Python Packages Overview
- Introduction to notebooks in python (Ipython, jupyter)
Module 8 - Introduction to Applied
Machine Learning
- Fundamentals of Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-supervised Machine Learning
- Evaluation and revalidation
- Applications and beyond
Module 9 - Supervised Machine Learning
Models
- How supervised learning algorithms work
- Why and where to use supervised learning
- Concepts of learning models
- Bias-variance tradeoff
- How supervised learning algorithms work
- Algorithms to be used for supervised learning
- Support Vector Machines o linear regression
- logistic regression
- naive Bayes
- decision trees
- k-nearest neighbor algorithm
- Challenges of learning
- Exercises
Module 10 - Un-Supervised Machine
Learning Models
- How un-supervised learning algorithms work
- Why and where to use unsupervised learning
- Concepts of learning models
- How un-supervised learning algorithms work
- Algorithms to be used for unsupervised learning
- Clustering
- k-Means clustering
- Gaussian mixture models
- Association
- Challenges of learning
- Exercises
Module 11 - Resampling Machine Learning
Models
- Cross-validation and the Bootstrap
- Training Error versus Test error
- Training- versus Test-Set Performance
- More on prediction-error estimates
- Validation-set approach
- K-fold Cross-validation
- Exercises
Module 12 - Basics of AI, Tensorflow &
Keras
- Introduction to Artificial Intelligence
- Applications of Artificial Intelligence
- Introduction to TensorFlow and Keras
- Math behind AI: Simplified
Module 13 - Deep Learning Foundation
- Introduction to Deep Learning
- Activation Function and Learning
- Rate Role of Optimizers in Deep Learning Models
- Deep Learning Model Practical
Module 14 - Computer Vision
- Introduction to Convolution Neural Networks
- Decoding Image Components
- Identifying MNIST using CNN
- Preprocessing Image Data to apply CNN
Module 15 - Natural Language
Processing
- Introduction to NLP & Word Vectors
- Decoding a Textual Data
- NLP using Recurrent Neutral Networks (RNN)
- NLP using Memory Alterations
Module 16 - Essentials of Generative Al,
Prompt Engineering & ChatGPT
- Generative AI and its Landscape
- Explainable AI
- Conversational AI
- Prompt Engineering
- Designing and Generating Effective Prompts
- Large Language Models
- ChatGPT and its Applications
- Ethical Considerations in Generative AI Models
- Responsible Data Usage and Privacy
- The Future of Generative AI
- AI Technologies for Innovation