Training Course on Artificial Intelligence and Machine Learning Applications for AI Developers

Course Outline

Module 1: Introduction to AI and Machine Learning Concepts

ü  What is AI? Different types of AI (narrow, general, superintelligence)

ü  What is Machine Learning? Supervised, Unsupervised, Reinforcement Learning

ü  Key applications of AI/ML in various industries

ü  The AI/ML development lifecycle

ü  Introduction to Python for AI/ML and essential libraries (NumPy, Pandas)

ü  Practical Session: Setting up a Python environment and performing basic data manipulation with Pandas.

Module 2: Data Preprocessing and Feature Engineering for ML

ü  Importance of data quality and preparation

ü  Handling missing values: imputation, removal

ü  Data encoding: one-hot encoding, label encoding

ü  Feature scaling: normalization, standardization

ü  Feature engineering techniques: creating new features from existing ones

ü  Practical Session: Cleaning and transforming a messy dataset to prepare it for machine learning using Pandas and scikit-learn.

Module 3: Supervised Learning: Regression Models

ü  Introduction to supervised learning: predicting continuous values

ü  Linear Regression: Simple and Multiple Linear Regression

ü  Polynomial Regression and Regularization (Lasso, Ridge)

ü  Evaluating regression models: MAE, MSE, R-squared

ü  Overfitting and underfitting concepts

ü  Practical Session: Building and evaluating a linear regression model on a dataset to predict a continuous variable.

Module 4: Supervised Learning: Classification Models

ü  Introduction to classification: predicting categorical labels

ü  Logistic Regression for binary and multi-class classification

ü  Decision Trees and Random Forests

ü  Support Vector Machines (SVMs)

ü  Evaluating classification models: accuracy, precision, recall, F1-score, confusion matrix

ü  Practical Session: Building and evaluating a classification model (e.g., Logistic Regression or Decision Tree) on a dataset.

Module 5: Unsupervised Learning: Clustering and Dimensionality Reduction

ü  Introduction to unsupervised learning: finding patterns in unlabeled data

ü  K-Means Clustering: algorithm, choosing K, evaluation

ü  Hierarchical Clustering

ü  Dimensionality Reduction: Principal Component Analysis (PCA)

ü  Use cases for clustering and dimensionality reduction

ü  Practical Session: Applying K-Means clustering to a dataset and visualizing the clusters.

Module 6: Introduction to Neural Networks and Deep Learning

ü  The Perceptron model: building blocks of neural networks

ü  Multilayer Perceptrons (MLPs) and activation functions

ü  Backpropagation algorithm for training neural networks

ü  Introduction to Deep Learning frameworks (TensorFlow/Keras or PyTorch)

ü  Convolutional Neural Networks (CNNs) overview (for image data)

ü  Practical session: Building a simple feedforward neural network using TensorFlow/Keras or PyTorch to solve a classification problem.

Module 7: Natural Language Processing (NLP) Fundamentals

ü  Introduction to NLP: processing and understanding human language

ü  Text preprocessing: tokenization, stop words, stemming, lemmatization

ü  Text representation: Bag-of-Words, TF-IDF, Word Embeddings

ü  Basic NLP tasks: sentiment analysis, text classification

ü  Introduction to NLP libraries (e.g., NLTK, spaCy)

ü  Practical Session: Performing basic text preprocessing and sentiment analysis on a sample text dataset.

Module 8: Computer Vision Fundamentals

ü  Introduction to Computer Vision: enabling computers to "see"

ü  Image representation: pixels, channels

ü  Basic image processing operations: resizing, filtering

ü  Feature extraction for images

ü  Overview of image classification tasks

ü  Practical Session: Loading, manipulating, and displaying images using libraries like OpenCV or Pillow.

Module 9: Model Evaluation, Selection, and Ethical AI

ü  Cross-validation techniques for robust model evaluation

ü  Hyperparameter tuning and model optimization

ü  Model selection criteria: bias-variance trade-off

ü  Ethical considerations in AI: bias, fairness, transparency, accountability

ü  Interpretable AI (XAI) concepts

ü  Practical Session: Implementing cross-validation and hyperparameter tuning for a previously built model.

Module 10: Model Deployment and MLOps Concepts

ü  Saving and loading machine learning models

ü  Introduction to model deployment strategies: REST APIs, batch predictions

ü  MLOps (Machine Learning Operations) concepts: automation, monitoring, versioning

ü  Serving models with frameworks like Flask/FastAPI

ü  Introduction to cloud ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)

ü  Practical Session: Building a simple Flask/FastAPI application to serve a trained machine learning model via a REST API.

About Our Trainers

The training will be delivered by a team of highly experienced AI/ML Engineers, Data Scientists, and Machine Learning Architects with extensive backgrounds in designing, developing, and deploying real-world AI and ML solutions across various industries. Our trainers bring a wealth of practical strategies, hands-on coding expertise with leading AI/ML frameworks, and deep insights into the entire AI project lifecycle. Their expertise ensures that participants gain cutting-edge knowledge and actionable skills to excel in the field of AI and Machine Learning development.

Quality Statement

We are committed to delivering high-quality, practical, and relevant training programs that empower professionals to excel in their roles and contribute significantly to the success of their organizations. Our courses are continually updated to reflect the latest industry trends, regulatory changes, and best practices.

Admission Criteria

ü  Participants should be reasonably proficient in English. 

ü  Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254737296202

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254720272325 / +254737296202 or Email training@phoenixtrainingcenter.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737296202