Introduction to Artificial Intelligence and Machine Learning

About Course

Course Description:

This Introduction to Artificial Intelligence and Machine Learning course is designed to provide students with a foundational understanding of AI and ML concepts, techniques, and applications. Through a combination of lectures, hands-on programming, and real-world projects, students will learn how to build and deploy machine learning models and understand the broader implications of AI in various industries.

Course Content

Week 1-2: Introduction to AI and ML
Overview of Artificial Intelligence and Machine Learning Historical perspective and AI milestones Types of machine learning: supervised, unsupervised, and reinforcement learning. Introduction to Python programming for ML (NumPy, pandas)

Week 3-4: Data Preprocessing and Feature Engineering
Data collection and exploration Data cleaning and missing value imputation Feature selection and transformation Handling categorical data

Week 5-6: Supervised Learning
Linear regression and logistic regression Decision trees and random forests Support vector machines (SVM) Model evaluation and validation

Week 7-8: Unsupervised Learning
Clustering algorithms (K-means, hierarchical) Dimensionality reduction (PCA, t-SNE) Association rule mining Recommender systems

Week 9-10: Neural Networks and Deep Learning
Introduction to artificial neural networks (ANN) Feedforward and backpropagation Convolutional neural networks (CNN) Recurrent neural networks (RNN) Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)

Week 11-12: Advanced Topics and Applications
Natural Language Processing (NLP) and text analysis Computer vision and image recognition Reinforcement learning Real-world AI/ML projects and case studies

Additional Topics (Optional):
Model deployment and productionization Ethics and responsible AI Transfer learning and fine-tuning AI in specific industries (e.g., healthcare, finance)

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Introduction to Artificial Intelligence and Machine Learning

About Course

Course Description:

What Will You Learn?

Course Content

Week 1-2: Introduction to AI and ML
Overview of Artificial Intelligence and Machine Learning Historical perspective and AI milestones Types of machine learning: supervised, unsupervised, and reinforcement learning. Introduction to Python programming for ML (NumPy, pandas)

Week 3-4: Data Preprocessing and Feature Engineering
Data collection and exploration Data cleaning and missing value imputation Feature selection and transformation Handling categorical data

Week 5-6: Supervised Learning
Linear regression and logistic regression Decision trees and random forests Support vector machines (SVM) Model evaluation and validation

Week 7-8: Unsupervised Learning
Clustering algorithms (K-means, hierarchical) Dimensionality reduction (PCA, t-SNE) Association rule mining Recommender systems

Week 9-10: Neural Networks and Deep Learning
Introduction to artificial neural networks (ANN) Feedforward and backpropagation Convolutional neural networks (CNN) Recurrent neural networks (RNN) Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)

Week 11-12: Advanced Topics and Applications
Natural Language Processing (NLP) and text analysis Computer vision and image recognition Reinforcement learning Real-world AI/ML projects and case studies

Additional Topics (Optional):
Model deployment and productionization Ethics and responsible AI Transfer learning and fine-tuning AI in specific industries (e.g., healthcare, finance)

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Nigeria

About Course

Course Description:

What Will You Learn?

Course Content

Week 1-2: Introduction to AI and ML Overview of Artificial Intelligence and Machine Learning Historical perspective and AI milestones Types of machine learning: supervised, unsupervised, and reinforcement learning. Introduction to Python programming for ML (NumPy, pandas)

Week 3-4: Data Preprocessing and Feature Engineering Data collection and exploration Data cleaning and missing value imputation Feature selection and transformation Handling categorical data

Week 5-6: Supervised Learning Linear regression and logistic regression Decision trees and random forests Support vector machines (SVM) Model evaluation and validation

Week 7-8: Unsupervised Learning Clustering algorithms (K-means, hierarchical) Dimensionality reduction (PCA, t-SNE) Association rule mining Recommender systems

Week 9-10: Neural Networks and Deep Learning Introduction to artificial neural networks (ANN) Feedforward and backpropagation Convolutional neural networks (CNN) Recurrent neural networks (RNN) Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)

Week 11-12: Advanced Topics and Applications Natural Language Processing (NLP) and text analysis Computer vision and image recognition Reinforcement learning Real-world AI/ML projects and case studies

Additional Topics (Optional): Model deployment and productionization Ethics and responsible AI Transfer learning and fine-tuning AI in specific industries (e.g., healthcare, finance)

Student Ratings & Reviews

About Us

Quick Links

Recent Trainings

Get intouch

Contact Us

UK

Nigeria

Week 1-2: Introduction to AI and ML
Overview of Artificial Intelligence and Machine Learning Historical perspective and AI milestones Types of machine learning: supervised, unsupervised, and reinforcement learning. Introduction to Python programming for ML (NumPy, pandas)

Week 3-4: Data Preprocessing and Feature Engineering
Data collection and exploration Data cleaning and missing value imputation Feature selection and transformation Handling categorical data

Week 5-6: Supervised Learning
Linear regression and logistic regression Decision trees and random forests Support vector machines (SVM) Model evaluation and validation

Week 7-8: Unsupervised Learning
Clustering algorithms (K-means, hierarchical) Dimensionality reduction (PCA, t-SNE) Association rule mining Recommender systems

Week 9-10: Neural Networks and Deep Learning
Introduction to artificial neural networks (ANN) Feedforward and backpropagation Convolutional neural networks (CNN) Recurrent neural networks (RNN) Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)

Week 11-12: Advanced Topics and Applications
Natural Language Processing (NLP) and text analysis Computer vision and image recognition Reinforcement learning Real-world AI/ML projects and case studies

Additional Topics (Optional):
Model deployment and productionization Ethics and responsible AI Transfer learning and fine-tuning AI in specific industries (e.g., healthcare, finance)