IBM - Generative AI & ML Professional Program Online Course

IBM - Generative AI & ML Professional Program

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English

Course Description

Welcome to our cutting-edge Generative Artificial Intelligence (AI) and Machine Learning (ML) program designed for aspiring data scientists, engineers, and tech enthusiasts, this program offers an in-depth exploration of the technologies driving the future of innovation. Whether you're a beginner eager to break into the field or a professional looking to expand your expertise, our comprehensive curriculum will equip you with the skills needed to excel in this dynamic and fast-growing industry. This IBM - Generative Artificial Intelligence (AI) and Machine Learning (ML) covers self-paced e-learning materials including course content, Lab/Assignment, hands on experience and Certification of IBM after completing the exam by IBM. -- Eligibility Criteria -- - No prior AI or ML knowledge required. - Open to all interested in AI and ML. - Basic understanding of programming recommended. - Access to a computer and internet. - English proficiency for comprehension of course materials.

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What you’ll learn

Python Training Module : Get introduced to programming with Python, one of the most popular and versatile languages. Develop applications ranging from web development to data analysis, and write clean, efficient code.
Machine Learning with Python : Explore machine learning using Python. Implement algorithms, preprocess data, and build predictive models. Cover supervised and unsupervised learning, model evaluation, and real-world applications.
Machine Learning with R : Learn to implement machine learning algorithms using R. Preprocess data, build and evaluate models, and apply techniques to solve realworld problems. Cover key topics like supervised and unsupervised learning.
Game-playing AI with Swift for TensorFlow (S4TF) : Develop AI models to play and master games using Swift and TensorFlow. Learn reinforcement learning and neural networks, covering basics of S4TF and creating intelligent gaming agents.
Deep Learning Fundamentals : Understand the basics of deep learning, including neural networks, CNNs, and RNNs. Explore applications in image recognition and NLP, and gain hands-on experience with popular frameworks.
Build Your Own Chatbot : Learn to design and develop chatbots from scratch. Explore natural language processing, dialogue management, and integration with messaging platforms. Gain hands-on experience in building intelligent, interactive chatbots.
Deep Learning with TensorFlow : Learn to design and develop chatbots from scratch. Explore natural language processing, dialogue management, and integration with messaging platforms. Gain hands-on experience in building intelligent, interactive chatbots.
How To Build Watson A1 And Swift APIS And Make Money : Explore the fundamentals of creating Watson A1 and Swift APIs. Develop powerful AI interfaces with Watson A1 and utilize Swift's capabilities to build scalable APIs that can generate revenue. Perfect for developers and entrepreneurs aiming to leverage AI for profitable API development.

Covering Topics

Learning Mode : Online Self-Paced (Learn Anywhere & Anytime).

Program Duration : 6 Months.

Learning Hours : 60+ (Includes E-Courseware, Videos, Hands-on-Labs, Assignments, Quiz etc by IBM).

Support : Weekly Doubt Clearance Session.

Certification : Official IBM certifications that directly engage with your LinkedIn Profile.

Curriculum

  Course 1 : Python

Module 1: Introduction to PYTHON 
Module 2: Deep dive into PYTHON
Module 3: Python Libraries
Module 4: Error Handling
Module 5: Other Topics
Module 6: Regression (Use case study)
Module 7: Other Regression related topics
Module 8: Advance

  Course 2 : Machine Learning with Python 

Module 1: Supervised vs Unsupervised Learning
Module 2: Supervised Learning I
Module 3: Supervised Learning II
Module 4: Unsupervised Learning
Module 5: Dimensionality Reduction & Collaborative Filtering

  Course 3 : Machine Learning With R 

Module 1: Machine Learning vs Statistical Modeling & Supervised VS Unsupervised Learning
Module 2: Supervised Learning I
Module 3: Supervised Learning II
Module 4: Unsupervised Learning
Module 5: Dimensionality Reduction & Collaborative Filtering

  Course 4 : Game-playing AI with Swift for Tensor Flow (S4TF)

Module 1: Set up your environment
Module 2: Understanding Swift's basics
Module 3: Tic Tac Toe (Minimax)
Module 4: Cartpole (Reinforcement Learning)
Module 5: 2048 (Monte Carlo Tree Search)

  Course 5 : Deep Learning Fundamentals
 
Module 1: Deep Learning Concept
Module 2: Deep Learning Concepts Continued
Module 3: Platforms for Deep Learning
Module 4: Deep Learning Software Libraries

  Course 6 : Build Your Own Chatbot 

Module 1: Introduction to Chatbots
Module 2: Working with Intents
Module 3: Working with Entities
Module 4: Defining the Dialog
Module 5: Deploying your Chatbot
Module 6: Advanced concepts — Part 1
Module 7: Advanced concepts — Part 2

  Course 7 : Deep Learning with TensorFlow 

Module 1: Introduction to TensorFlow
Module 2: Convolutional Neural Networks (CNN)
Module 3: Recurrent Neural Networks (RNN)
Module 4: Unsupervised Learning
Module 5: Autoencoders

  Course 8 : How to Build Watson AI and Swift APIs and Make Money

Module 1: Run server-side Swift locally and on the cloud, Incomplete section
Module 2: Build an iOS app to consume server-side Swift based on IBM Kitura, Incomplete section
Module 3: Monetize your app with IBM API Connect, Incomplete section