Description

What is Artificial Intelligence (AI)

Artificial Intelligence (AI) is the principle of simulating human intelligence with computers programmed to emulate human thought patterns and mimic their actions. Technology in this field grows day by day, steering use into the future by predicting situations and helping to solve complex problems. AI technology enables software, apps, and machines to learn, think, and correct themselves the same way humans do. Humans must first set up the system and develop the set of rules to be followed (algorithms), then computer programs use algorithms to analyze data, find patterns, and act on what they discover. AI programs become more accurate as they receive and process more data. A large part of AI involves machine learning, which allows computers to understand and copy human behavior by finding data patterns. In short, AI technology helps us do our jobs better and easier.

We offer Artificial Intelligence course that walk you through combining Deep Learning, Machine Learning, Natural Language Processing (NLP) and other AI technologies to improve business operations. By attending Artificial Intelligence master course, you learn to work with, or even build, your Artificial Intelligence framework.

Artificial Intelligence (AI) Consulting Market Size

The global Artificial Intelligence (AI) Consulting market size was valued at USD 93472.06 million in 2022 and is expected to expand at a CAGR of 37.46% during the forecast period, reaching USD 630611.25 million by 2028.- Linkedin

How much does an AI consultant make?

The estimated total pay for a AI consultant is $142,348 per year in the United States, with an average salary of $113,279 per year. Glassdoor.

The following topics will be covered as part of Artificial Intelligence Master Course.

Artificial Intelligence Master Course Syllabus

Introduction to the AI
• Application of AI
• AI Terminologies
• AI Project Life Cycle stages
• Datasets
• Mathematic Foundation for AI and ML
• Research Field Associated with AI
• Statistics Foundation
• Natural Language Processing (NLP) Foundation
• Computer Vision Foundation
• Hands on with Advance Python Libraries
• Hands on with NumPy and Pandas
• Exploratory Data Analysis (EDA)
• Hands on with Matplotlib and Seaborn
• Hands on with NLTK for NLP
• Hands on with Sapcy for NLP
• Types of Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Data Pre-processing
• Regression Algorithms
• Regression Models Performance Metrics
• Classification Algorithms
• Classification Models Performance Metrics
• Clustering Algorithms
• Clustering Models Performance Metrics
• Dimensionality Reduction
• PCA
• LDB
• Kernel PCA
• Foundation of Neural Network and Deep Learning
• Hands on with TensorFlow and Keras
• Artificial Neural Network
• Convolution Neural Network
• Recurrent Neural Network
• Deep Neural Network
• What Is Transfer Learning? How to Use Pre-Trained Models
• How to Use Pre-Trained Models
• VGG (e.g. VGG16 or VGG19).
• GoogLeNet (e.g. InceptionV3).
• Residual Network (e.g. ResNet50).
• Transformer models
• NLP with Deep Learning
• Computer Vision: GANs (Generative Adversarial Networks), LLM Model: BERT and GPT
• Project hands on for NLP based Projects
• Project hands on for Computer Vision based Projects
• Time Series Forecasting
• Foundation of Generative AI
• Foundation of ChatGPT