Professional Certificate in AI and Machine Learning for Professionals

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