Data Science & Artificial Intelligence

About Course

This short course aims to provide a foundational understanding of Data Science and Artificial Intelligence (AI). Over the course of 8 weeks, students will explore core concepts in data manipulation, statistical analysis, machine learning, and AI applications. They will also gain hands-on experience with popular tools and programming languages like Python, Pandas, Scikit-learn, and TensorFlow, and work on practical AI-driven projects.

What Will You Learn?

  • This short course aims to provide a foundational understanding of Data Science and Artificial Intelligence (AI). Over the course of 8 weeks, students will explore core concepts in data manipulation, statistical analysis, machine learning, and AI applications. They will also gain hands-on experience with popular tools and programming languages like Python, Pandas, Scikit-learn, and TensorFlow, and work on practical AI-driven projects.

Course Content

Week 1: Introduction to Data Science & AI
• Key Concepts: o What is Data Science? Overview of its role in business, research, and technology. o Introduction to Artificial Intelligence: A brief history, applications, and its relationship with Data Science. o Overview of the Data Science Lifecycle: Data collection, cleaning, exploration, analysis, and modeling. o Overview of AI Techniques: Supervised learning, unsupervised learning, reinforcement learning, and deep learning. o Tools and technologies for Data Science and AI: Python, Jupyter Notebooks, and key libraries (NumPy, Pandas, Scikit-learn, TensorFlow). • Hands-On Activity: o Introduction to Python and Jupyter Notebooks: Setting up a basic data science environment and performing simple data manipulations with Python.

Week 2: Data Collection and Data Cleaning
• Key Concepts: o Importance of data quality in Data Science. o Data Collection: Sources of data (structured vs unstructured data), APIs, web scraping, and public datasets. o Data Cleaning: Handling missing data, data transformations, outlier detection, and ensuring data consistency. o Data wrangling with Pandas: Importing data, inspecting data, cleaning, and transforming data. • Hands-On Activity: o Data Cleaning in Python using Pandas: Students will clean a messy dataset, handle missing values, filter out outliers, and format data for analysis.

Week 3: Exploratory Data Analysis (EDA)
• Key Concepts: o The importance of Exploratory Data Analysis (EDA) in understanding data before applying machine learning models. o Visualization Techniques: Using Matplotlib, Seaborn, and Plotly for data visualization. o Descriptive Statistics: Measures of central tendency, spread, correlation, and distributions. o Identifying patterns and relationships in data through visualization. • Hands-On Activity: o Exploratory Data Analysis with Python: Students will perform EDA on a dataset, visualizing relationships, distributions, and insights using Matplotlib and Seaborn.

Week 4: Introduction to Machine Learning
• Key Concepts: o What is Machine Learning? Overview of the types of machine learning: Supervised, Unsupervised, and Reinforcement Learning. o Overview of common machine learning algorithms: Linear Regression, Logistic Regression, Decision Trees, K-Nearest Neighbors, Support Vector Machines (SVM). o The Machine Learning Workflow: Problem definition, data preparation, model training, model evaluation, and model deployment. o Performance metrics: Accuracy, precision, recall, F1 score, ROC curves, etc. • Hands-On Activity: o Implementing a basic Supervised Learning model (e.g., Linear Regression) using Scikit-learn. o Model evaluation and interpretation of results using performance metrics.

Week 5: Advanced Machine Learning Models
• Key Concepts: o Ensemble Learning: Combining multiple models to improve prediction accuracy (Random Forest, Gradient Boosting, AdaBoost). o Support Vector Machines (SVM): Understanding the concept and how SVM works for classification tasks. o Clustering Algorithms: Introduction to K-Means Clustering and DBSCAN for unsupervised learning tasks. • Hands-On Activity: o Implementing and comparing models: Random Forest vs. SVM on a classification dataset using Scikit-learn. o Clustering exercise: Apply K-Means clustering to segment a dataset and interpret the results.

Week 6: Introduction to Deep Learning and Neural Networks
• Key Concepts: o Introduction to Deep Learning: What makes it different from traditional machine learning. o Overview of Artificial Neural Networks (ANN): Architecture, neurons, activation functions, layers. o Feedforward Neural Networks, Backpropagation, and optimization algorithms. o Key Deep Learning Libraries: TensorFlow and Keras. • Hands-On Activity: o Building a simple Feedforward Neural Network (ANN) for image classification using Keras and TensorFlow.

Week 7: Natural Language Processing (NLP)
• Key Concepts: o Introduction to Natural Language Processing: Techniques and challenges in processing human language data. o Text Preprocessing: Tokenization, stemming, lemmatization, removing stopwords, and vectorization (e.g., TF-IDF, Word2Vec). o Sentiment analysis and text classification. o Applications of NLP: Chatbots, language translation, and information retrieval. • Hands-On Activity: o Implementing a basic Sentiment Analysis model using Python and Scikit-learn or NLTK. o Exploring techniques for text classification on a real-world dataset (e.g., movie reviews or social media posts).

Week 8: AI in Practice and Deployment
• Key Concepts: o Deploying machine learning models into production: Tools and platforms for model deployment (Flask, FastAPI, Docker, etc.). o AI Applications: Case studies in various industries like healthcare, finance, and autonomous vehicles. o Ethics in AI: Bias, fairness, and transparency in machine learning algorithms. o Future Trends: The evolution of AI, including reinforcement learning, transfer learning, and emerging technologies like Quantum Computing. • Hands-On Activity: o Deploying a simple machine learning model as a web app using Flask or Streamlit. o Discussing ethical AI practices and evaluating potential biases in datasets and models.

Student Ratings & Reviews

No Review Yet
No Review Yet

Related Courses

$150 Original price was: $150.$125Current price is: $125.

Digital Marketing

0 Lessons 0 Enrolled
Free

Cyber Security & Digital Forensics

0 Lessons 0 Enrolled