Artificial Intelligence and Data Science - Techfellah Technology Research Center

About Course

An Artificial Intelligence (AI) and Data Science course is a comprehensive training program that focuses on teaching individuals the principles, methodologies, and tools required to work in the fields of AI and data science. AI and data science are closely related disciplines that involve extracting insights and knowledge from large datasets, using statistical, machine learning, and deep learning techniques to make data-driven predictions and decisions.

In an AI and Data Science course, participants typically learn:

Data Manipulation and Analysis: Understanding how to acquire, clean, and preprocess data for analysis.
Statistical Analysis: Applying statistical methods to explore data patterns and relationships.
Machine Learning: Learning various machine learning algorithms for classification, regression, clustering, and recommendation systems.
Deep Learning: Understanding neural networks and deep learning techniques for tasks like image recognition, natural language processing, and more.
Data Visualization: Creating meaningful visualizations to present insights and findings effectively.
Big Data and Distributed Computing: Handling large-scale datasets using technologies like Apache Hadoop and Spark.
AI Ethics and Bias: Considering ethical implications and potential biases in AI and data science projects.
Real-world Projects: Working on hands-on projects to apply the learned concepts to real-world scenarios.

An AI and Data Science course equips individuals with the skills and knowledge necessary to solve complex problems, make data-driven decisions, and build AI-powered applications. These skills are in high demand across various industries, including finance, healthcare, marketing, and more, making AI and data science professionals sought after for their ability to extract valuable insights from data and drive business growth and innovation.

Course Content

Advanced Machine Learning

Advanced supervised learning algorithms (e.g., SVM, Random Forests, etc.)
Unsupervised learning techniques (e.g., clustering, dimensionality reduction)
Semi-supervised and self-supervised learning

Deep Learning – Part 1

Deep Learning – Part 2

About Course

What Will You Learn?

Course Content

Advanced Machine Learning

Advanced supervised learning algorithms (e.g., SVM, Random Forests, etc.)

Unsupervised learning techniques (e.g., clustering, dimensionality reduction)

Semi-supervised and self-supervised learning

Deep Learning – Part 1

Neural network architectures (CNNs, RNNs, etc.)

Advanced deep learning models (e.g., GANs, Transformers)

Transfer learning and fine-tuning

Deep Learning – Part 2

Advanced optimization techniques (e.g., Adam, RMSprop, etc.)

Regularization methods (e.g., dropout, L1/L2 regularization)

Explaining deep learning models (e.g., SHAP, LIME)

Natural Language Processing (NLP) – Part 1

Word embeddings and word representation models

Text generation and language modeling

Named Entity Recognition (NER) and Part-of-Speech tagging

Natural Language Processing (NLP) – Part 2

Sequence-to-sequence models (e.g., attention-based models)

Text classification and sentiment analysis

Machine Translation with NLP

Computer Vision – Part 1

Advanced image recognition and object detection models

Image segmentation and instance segmentation

Pose estimation and facial recognition

Computer Vision – Part 2

Video analysis and action recognition

Generative models in computer vision (e.g., GANs)

Transfer learning for computer vision tasks

Time Series Analysis and Forecasting

Time series forecasting with advanced techniques

Deep learning for time series forecasting

Handling seasonality and non-stationary time series

Reinforcement Learning – Part 1

Advanced reinforcement learning algorithms (e.g., DDPG, A3C)

Model-based and model-free reinforcement learning

Policy gradient methods

Reinforcement Learning – Part 2

Deep reinforcement learning with DQN and variants

Handling exploration-exploitation trade-offs

Multi-agent and hierarchical reinforcement learning

AI Ethics and Responsible AI

Understanding the ethical implications of AI

Bias and fairness in AI systems

Guidelines for responsible AI development

Advanced Data Science Techniques

Advanced statistical modeling and hypothesis testing

Time series analysis and forecasting

Handling imbalanced datasets and rare events

Big Data and Distributed Computing

Introduction to big data and its challenges

Distributed data processing with Apache Spark

Handling big data in AI and data science projects

Final Project

Participants work on an individual or group project to apply the advanced AI and data science concepts learned throughout the course