Fundamentals of AI and Data Science - Techfellah Technology Research Center

About Course

The Fundamentals of AI and Data Science course is an introductory-level training program designed to provide participants with a solid understanding of the basic concepts and principles in the fields of Artificial Intelligence (AI) and Data Science. This course is suitable for individuals who have little to no prior experience in these domains but want to explore the foundations of AI and data analysis.

In a Fundamentals of AI and Data Science course, participants typically learn:

Introduction to AI: Understanding the basic definition and applications of AI, including machine learning and deep learning.
Data Collection and Preprocessing: Learning how to gather and clean data to prepare it for analysis.
Statistical Concepts: Exploring basic statistical concepts used in data analysis.
Machine Learning Algorithms: An overview of common machine learning algorithms and their applications.
Data Visualization: Creating simple visualizations to present data insights.
Introduction to Neural Networks: Understanding the basics of neural networks and their role in AI.
Practical Exercises: Engaging in hands-on exercises and small projects to apply learned concepts.
Ethical Considerations: Discussing ethical implications and considerations when working with data and AI.

The Fundamentals of AI and Data Science course serves as a stepping stone for individuals interested in pursuing a more in-depth study of AI and data science. It lays the groundwork for further exploration of advanced topics and more complex algorithms in subsequent courses. Additionally, it provides participants with a broad understanding of how AI and data science play a significant role in various industries and applications, making it a valuable course for those considering a career in these rapidly growing fields.

Course Content

Introduction to Artificial Intelligence

Overview of artificial intelligence and its applications
History and evolution of AI
Introduction to machine learning and deep learning

Data Analysis with Python

Supervised Learning – Part 1

About Course

What Will You Learn?

Course Content

Introduction to Artificial Intelligence

Overview of artificial intelligence and its applications

History and evolution of AI

Introduction to machine learning and deep learning

Data Analysis with Python

Introduction to data analysis and data preprocessing

Working with data using Python libraries (NumPy, Pandas, etc.)

Data visualization techniques with Matplotlib and Seaborn

Supervised Learning – Part 1

Introduction to supervised learning

Regression analysis and linear regression

Evaluating regression models and handling overfitting

Supervised Learning – Part 2

Classification algorithms (Logistic Regression, k-Nearest Neighbors, etc.)

Model evaluation metrics for classification

Handling imbalanced datasets and class imbalance

Unsupervised Learning

Introduction to unsupervised learnin

Clustering algorithms (k-Means, hierarchical clustering, etc.)

Dimensionality reduction with Principal Component Analysis (PCA)

Introduction to Deep Learning

Introduction to neural networks and deep learning

Building and training neural networks with TensorFlow/Keras

Convolutional Neural Networks (CNNs) for image recognition

Natural Language Processing (NLP)

Introduction to NLP and its applications

Text processing and sentiment analysis with NLP

Building NLP models with libraries like NLTK and spaCy

Time Series Analysis and Forecasting

Understanding time series data and its characteristics

Time series forecasting techniques (ARIMA, LSTM, etc.)

Implementing time series forecasting with Python

Reinforcement Learning

Introduction to reinforcement learning and its applications

Building and training reinforcement learning agents

Implementing Q-learning and Deep Q Networks (DQNs)

Advanced Topics in AI and Data Science

Introduction to generative models (GANs, VAEs, etc.)

AI ethics and responsible AI development

Introduction to big data and data engineering

Data Science Project – Part 1

Understanding the data science project lifecycle

Data collection, cleaning, and preparation

Exploratory data analysis (EDA) and feature engineering

Data Science Project – Part 2

Model selection and hyperparameter tuning

Model evaluation and performance metrics

Presenting and visualizing data science results

Data Science Project – Part 3

Finalizing the data science project

Communicating findings and insights

Best practices for reproducibility and documentation

Final Project

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

The project should involve solving a real-world problem and implementing advanced AI and data science techniques.