This training course aims to provide a comprehensive understanding of the core concepts, principles, and applications of AI and ML. This training is designed for participants who have a basic understanding of programming and statistics but want to dive deeper into AI and machine learning concepts and applications.

Duration: 10 Days

Objectives

• Understanding the Fundamentals of AI and ML 
• Exploring Core Concepts and Algorithms
• Mastering Data Preprocessing and Feature Engineering Techniques
• Evaluating and Optimizing Models
• Applying AI and ML in Real-World Scenarios
• Addressing Ethical Concerns in AI
• Exploring Future Trends and Innovations
• Identifying Challenges and Opportunities

Day 1: Introduction to AI and ML

• Overview of AI and ML: Definitions, history, and applications
• Types of AI: Narrow AI, General AI, and Superintelligent AI
• Types of ML: Supervised, Unsupervised, and Reinforcement Learning
• Mathematics for ML: Linear algebra, calculus, probability, and statistics basics
• Tools and Environments: Introduction to Python, Jupyter Notebooks, and libraries like NumPy and Pandas

Day 2: Data Preprocessing and Exploration

• Data Collection: Sources and methods
• Data Cleaning: Handling missing data, outliers, and normalization
• Data Visualization: Using Matplotlib and Seaborn for exploratory data analysis
• Feature Engineering: Creating meaningful features from raw data
• Dimensionality Reduction: PCA, t-SNE, and other techniques

Day 3: Supervised Learning - Regression

• Introduction to Supervised Learning: Concepts and algorithms
• Linear Regression: Simple and multiple linear regression
• Evaluation Metrics: MSE, RMSE, and R²
• Polynomial Regression: When and how to use it
• Hands-on Exercise: Building and evaluating regression models

Day 4: Supervised Learning - Classification

• Introduction to Classification: Binary and multiclass classification
• Logistic Regression: Understanding and applying it
• Decision Trees: Concepts and implementation
• Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC-AUC
• Hands-on Exercise: Building and evaluating classification models

Day 5: Unsupervised Learning - Clustering

• Introduction to Unsupervised Learning: Concepts and importance
• K-Means Clustering: Algorithm, distance metrics, and optimization
• Hierarchical Clustering: Concepts and dendrograms
• DBSCAN: Density-based clustering
• Hands-on Exercise: Applying clustering techniques to datasets

Day 6: Unsupervised Learning - Association and Anomaly Detection

• Association Rule Mining: Apriori algorithm and market basket analysis
• Anomaly Detection: Techniques and applications in fraud detection
• Principal Component Analysis (PCA): Dimensionality reduction for unsupervised learning
• Hands-on Exercise: Implementing association rules and anomaly detection

Day 7: Neural Networks and Deep Learning - Basics

• Introduction to Neural Networks: Perceptron and multilayer perceptron
• Activation Functions: Sigmoid, ReLU, Tanh, etc.
• Backpropagation: Understanding and implementing
• Introduction to TensorFlow/Keras: Setting up and basic operations
• Hands-on Exercise: Building a simple neural network

Day 8: Deep Learning - Advanced Topics

• Convolutional Neural Networks (CNNs): Architecture and applications in image processing
• Recurrent Neural Networks (RNNs): Understanding sequences and time series data
• Transfer Learning: Using pre-trained models
• Hands-on Exercise: Building and training CNNs and RNNs

Day 9: Reinforcement Learning

• Introduction to Reinforcement Learning: Key concepts and terminology
• Markov Decision Processes (MDPs): Framework for RL
• Q-Learning and Deep Q-Networks (DQNs): Algorithms and applications
• Hands-on Exercise: Implementing a simple reinforcement learning algorithm

Day 10: AI/ML Applications and Future Trends

• Natural Language Processing (NLP): Basics and applications like sentiment analysis
• AI Ethics and Fairness: Understanding bias and ethical considerations in AI
• AI in Industry: Case studies from healthcare, finance, autonomous systems, etc.
• Capstone Project: Apply the learned concepts to a real-world problem

Training Methodology

The training approach comprises many methods to facilitate deep understanding and practical proficiency in artificial intelligence (AI) and machine learning (ML). Participants will experience complete immersion in a dynamic learning environment that includes interactive lectures, hands-on coding laboratories, real-world case studies, and group discussions. During the training program, participants have the opportunity to demonstrate their skills and receive customized feedback through practical projects and assessments. This prepares them to effectively apply the principles of Artificial Intelligence (AI) and Machine Learning (ML) in real-world scenarios.

Key Notes

i. The participant must be conversant with English.

ii. Upon completion of training the participant will be issued with an Authorized Training Certificate

iii. Course duration is flexible and the contents can be modified to fit any number of days.

iv. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

v. One-year post-training support Consultation and Coaching provided after the course.

vi. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you

Course Schedule