Python for AI and Machine Learning Training Course

Module 1: Python Essentials for Data Science

• Mastery of Python data structures: Lists, Dictionaries, Sets, and Tuples for data storage
• Implementing list comprehensions and lambda functions for concise code
• Error handling and exception management in data processing scripts
• Managing environments and packages using Conda and Pip
• Understanding the functional programming paradigm in Python
• Practical session: Building a custom data pre-processing script to clean raw text files

Module 2: Numerical Computing with NumPy

• Creating and manipulating multi-dimensional arrays (ND-Arrays) for mathematical operations
• Understanding Vectorization and Broadcasting for high-performance computing
• Mathematical functions: Linear algebra, statistics, and random number generation
• Indexing, slicing, and advanced filtering of large numerical datasets
• Memory management and performance optimization with NumPy arrays
• Practical session: Implementing a matrix multiplication and linear system solver without built-in loops

Module 3: Data Manipulation and Analysis with Pandas

• Loading diverse data formats: CSV, Excel, SQL databases, and JSON into DataFrames
• Advanced data cleaning techniques: Handling missing values, duplicates, and outliers
• Grouping, pivoting, and aggregating data for summary statistics
• Merging, joining, and concatenating multiple complex datasets
• Time-series data handling: Resampling, shifting, and window functions
• Practical session: Performing a comprehensive demographic analysis on a multi-million-row dataset

Module 4: Data Visualization and Exploratory Data Analysis

• Creating static visualizations with Matplotlib: Line plots, histograms, and scatter plots
• Advanced statistical plotting using Seaborn for correlation matrices and heatmaps
• Interactive data exploration techniques to identify underlying patterns
• Customizing plot aesthetics: Themes, labels, and multi-panel figures
• Visualizing high-dimensional data distributions and density
• Practical session: Generating a visual dashboard to identify trends in a retail sales dataset

Module 5: Foundations of Supervised Learning

• Introduction to the Scikit-Learn API and the "fit-transform-predict" workflow
• Simple and Multiple Linear Regression for predicting continuous values
• Understanding the Bias-Variance tradeoff and model generalization
• Implementing K-Nearest Neighbors (KNN) for basic classification tasks
• Measuring performance: Mean Squared Error (MSE) and R-Squared values
• Practical session: Building and evaluating a housing price prediction model

Module 6: Advanced Classification and Regression Techniques

• Logic of Logistic Regression for binary and multi-class classification
• Implementing Decision Trees and understanding entropy and Gini impurity
• The power of Ensemble Learning: Random Forests and Gradient Boosting machines
• Support Vector Machines (SVM) for complex boundary classification
• Evaluating classifiers: Confusion matrices, Precision, Recall, and F1-Score
• Practical session: Developing a credit scoring model to predict loan defaults

Module 7: Unsupervised Learning and Dimensionality Reduction

• Clustering techniques: K-Means, Hierarchical Clustering, and DBSCAN
• Principal Component Analysis (PCA) for feature reduction and visualization
• Market Basket Analysis using the Apriori algorithm for association rules
• Anomaly detection for fraud identification in transactional data
• Silhouette analysis for determining the optimal number of clusters
• Practical session: Segementing a customer database into distinct personas based on purchasing behavior

Module 8: Introduction to Deep Learning and Neural Networks

• Understanding the structure of an Artificial Neural Network (ANN)
• Introduction to Keras and TensorFlow for building deep models
• The role of Activation Functions, Loss Functions, and Optimizers
• Building a Simple Perceptron and Multi-Layer Perceptron architecture
• Training loops, epochs, and batch sizes in deep learning
• Practical session: Creating a neural network to recognize handwritten digits (MNIST dataset)

Module 9: Natural Language Processing (NLP) Fundamentals

• Text preprocessing: Tokenization, Stemming, Lemmatization, and Stop-word removal
• Converting text to numbers: Bag of Words and TF-IDF vectorization
• Sentiment Analysis: Classifying text based on emotional tone
• Introduction to Word Embeddings and their importance in semantic meaning
• Building a basic text classification pipeline with Scikit-Learn
• Practical session: Building a spam filter to automatically classify emails

Module 10: Model Deployment and Best Practices

• Serializing models using Pickle and Joblib for future use
• Creating a simple web API for model serving using Flask
• Best practices for model versioning and experiment tracking
• Understanding the basics of MLOps and automated pipelines
• Ethical considerations: Dealing with data bias and model interpretability
• Practical session: Deploying a trained machine learning model as a local web service

Requirements:

Participants should be reasonably proficient in English.

Applicants must live up to Armstrong Global Institute 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@armstrongglobalinstitute.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 / +254725012095 / +254724452588

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: Armstrong Global Training & Development Center 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 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.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@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588