Machine Learning Training in Abuja Nigeria

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Godstime Edet

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About Course

COURSE OVERVIEW

Welcome to the Comprehensive Machine Learning course! In this immersive journey, you’ll delve into the fascinating world of machine learning, where algorithms and data come together to enable computers to learn and make intelligent decisions. This course is designed to provide you with a strong foundation in the theories, techniques, and practical applications of machine learning.

LEARNING OUTCOME

At the end of this course every student will comfortably be able to:

Gain hands-on experience in preprocessing and exploring diverse types of data.
Learn about a wide array of machine learning algorithms, from basic to advanced.
Explore methods for model evaluation, selection, and fine-tuning.
Develop the ability to implement machine learning projects using popular libraries and frameworks.

ASSESSMENT AND GRADING

Weekly quizzes to reinforce concepts
Hands-on coding assignments implementing algorithms
Mid-term and final projects applying machine learning techniques to real-world problems
Class participation in discussions and ethical debates

Syllabus

WEEK 1: INTRODUCTION TO MACHINE LEARNING
Learn the basics of machine learning algorithms

What is machine learning

00:00
Types of machine learning (supervised, unsupervised, reinforcement learning)

00:00
Machine learning workflow

00:00
Python programming basics for machine learning

00:00

WEEK 2: DATA PREPOCESSING AND EXPLORATION
Analyzing categorical and numerical data for statistical decision

WEEK 3: SUPERVISED LEARNING ALGORITHM
Using labeled datasets to train algorithms and patterns to classify data and predict outcomes accurately

WEEK 4: MODEL EVALUATION AND VALIDATION
Authenticating trained model prediction and testing entire model and its performance in various circumstances

WEEK 5: UNSUPERVISED LEARNING ALGORITHM
Using algorithms to analyze and cluster unlabeled dataset

WEEK 6: NEURAL NETWORK AND DEEP LEARNING
Learning processes using AI

WEEK 7: NATURAL LANGUAGE PROCESSING
Making human input language decipherable

WEEK 8: MODEL DEPLOYMENT AND PRODUCTIONALISATION
Making a model available via real-time APIs.

WEEK 9: ETHICS AND BIAS IN MACHINE LEARNING
Validation before production

WEEK 10: SPECIAL TOPICS IN MACHINE LEARNING
Non-stationary data, model for optimum solution and probabilistic reasoning with algorithms.

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