Professional Certificate in AI and Machine Learning for Professionals

Course details

Overview
AI has revolutionised industries across the board creating a high demand for professionals with artificial intelligence skills. AI provides an exciting career opportunity in data science machine learning engineering artificial intelligence research robotics and many more. Learning AI will equip you with problem-solving techniques to tackle challenging issues in various domains ranging from healthcare and finance to manufacturing and transportation. By gaining knowledge in AI you can analyse data effectively extract valuable patterns and trends and make data-driven decisions that can drive innovation and competitiveness. AI will equip you with the necessary tools to identify opportunities for innovation develop new products or services and stay ahead of competitors in two days fast-paced technology-driven world. The AI and Machine Learning for Professionals course is designed to provide professionals with a comprehensive understanding of artificial intelligence (AI) and machine learning (ML) concepts and applications. The course aims to equip participants with the knowledge and skills necessary to leverage AI and ML technologies in their professional roles and stay ahead in the rapidly evolving digital landscape. Through theoretical lectures hands-on exercises and real-world case studies participants will gain practical insights into the potential of AI and ML and learn how to apply these technologies to solve complex business problems. Artificial intelligence is at the forefront of technological advancement and will continue growing. Suppose you want to future-proof your skills and add up to the evolving job market ensuring that you remain valuable and competitive in a world where technology increasingly relies on AI. In that case this course is the right course for you. This course will enable you to understand the technology driven by AI and give you a market insight into how you can take advantage of this innovation.

Objective
This course aims to develop a solid foundation in AI and machine learning.
Provides insight into the diverse applications of AI and machine learning across industries. You will clearly understand the principles and techniques underlying artificial intelligence and machine learning algorithms.
You will learn about biasesfairnessprivacy and transparency in AI and the importance of incorporating ethical principles in AI development.
You will gain a competitive sight in the job market as artificial intelligence continues transforming industries and creating demand for professionals with expertise in this field.
Our case studies will help you explore how artificial intelligence and machine learning can be leveraged to solve complex business problemsimprove decision making and drive innovation.

Who Should Attend
Professionals like software engineers, data scientists, data analysts or people in technical roles who wish to expand their careers in the new industry trends.
Managers and business leaders can take advantage of this course to make informed decisions and develop bespoke strategies to collaborate within their organisations using AI.
Professionals from various domains who are already using AI in healthcare, finance, marketing, manufacturing, and retail sectors.
Entrepreneurs will gain the necessary knowledge and insights to harness the power of AI for the development of business models to initiate their entrepreneurial ventures.

Course Outlines
Day 1 Introduction to AI and Machine Learning
Overview of artificial intelligence and machine learning
Key concepts terminology and methodologies
Probability for machine learning
Historical developments and current trends in AI and ML
Statistics for machine learning
Ethical Considerations and societal impact of AI
Supervised unsupervised and reinforcement learning.
Nearest neighbour methods
decision tree part
Feature selection and engineering
Evaluation metrics and performance assessment

Day 2 Data Preprocessing and Data Analysis & Algorithms
Data cleaning transformation and normalization
Handling missing data and outliers
Bayesian Optimization
logistic regression
principal component analysis
Exploratory data analysis techniques
Data visualization for insights
Linear regression
Logistic regression
Decision trees and random forests
Support vector machines (SVM)
Naive Bayes classifiers

Day 3 Deep Learning Fundamentals & Unsupervised Learning Algorithms
K-means clustering
Hierarchical clustering
Principal Component Analysis (PCA)
Association rule learning
Anomaly detection
Neural networks and their architecture
Activation functions and backpropagation
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Transfer learning and fine-tuning

Day 4 Natural Language Processing (NLP) & Reinforcement Learning
Text preprocessing and tokenization
Word embeddings and word2vec
Sentiment analysis
Named Entity Recognition (NER)
Text generation and language models
Markov decision processes
Q-learning and policy gradients
Exploration-exploitation trade-off
Deep Q-Networks (DQN)
Applications of reinforcement learning
AI Best Practices

Day 5 Deployment and Ethical Considerations with Real-world Applications & Case Studies
Model deployment and serving
Interpretability and explainability of ML models.
Fairness bias and ethical considerations in AI
Privacy and security concerns
Regulatory Frameworks and compliance
Image recognition and computer vision
Fraud detection and anomaly detection
Recommender systems
Natural language processing applications
Autonomous systems and robotics
Future Trends and Emerging Technologies