Nex-G Innovations

The objective of the AI/ML Training & Certification program is to equip participants with a strong foundation in artificial intelligence and machine learning concepts, tools, and techniques.

The training focuses on practical implementation using industry-standard platforms such as Python, TensorFlow, and scikit-learn. Participants will learn how to preprocess data, build and evaluate machine learning models, and apply deep learning algorithms to solve real-world problems across various domains.

By the end of the program, learners will be able to confidently design, develop, and deploy AI-driven solutions, positioning themselves for roles in data science, AI engineering, and intelligent automation.

Code: NES_SK_22569

Duration: 120 Hrs / 3 months / Customized

Mode: Corporate (Instructor Led Online / Onsite), Individual (Instructor Led Online)

+91-8826900551

• 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