Artificial Intelligence Courses

1. Introduction to Artificial Intelligence

Content:

  • Overview of AI history and evolution
  • Key AI technologies (Machine Learning, Deep Learning, NLP)
  • Real-world applications of AI in healthcare, finance, and automation
  • Introduction to AI tools and platforms

2. Machine Learning Essentials

Explore the foundations of machine learning, including algorithms, predictive modeling, and data analysis techniques.
Content:

  • Supervised and unsupervised learning concepts
  • Key ML algorithms: regression, classification, clustering
  • Hands-on with Python libraries: Scikit-learn and TensorFlow
  • Case studies: fraud detection and customer segmentation

3. Deep Learning with Neural Networks

Dive into deep learning to create models capable of image recognition, text analysis, and decision-making.
Content:

  • Introduction to neural networks and backpropagation
  • Convolutional Neural Networks (CNNs) for computer vision
  • Recurrent Neural Networks (RNNs) for time series data
  • Tools: TensorFlow, Keras, and PyTorch

Data Science Courses

1. Foundations of Data Science

Content:

  • Basics of data science workflows
  • Data cleaning and preprocessing techniques
  • Statistical analysis and visualization with Python
  • Mini-project: Analyzing sales trends

2. Data Analysis with Python

Unlock the potential of Python for data manipulation and visualization with hands-on projects.
Content:

  • Working with Pandas for data manipulation
  • Visualizing data using Matplotlib and Seaborn
  • Data aggregation and filtering techniques
  • Project: Analyzing COVID-19 trends

3. Data Visualization with Tableau

Learn to transform raw data into compelling visual stories using Tableau’s interactive dashboards.
Content:

  • Building and customizing dashboards
  • Using Tableau for business intelligence
  • Connecting Tableau to real-time data sources
  • Project: Creating a sales performance dashboard

4. Big Data and Hadoop

Discover how to handle and analyze massive datasets using Hadoop and Spark for real-time insights.
Content:

  • Introduction to HDFS and MapReduce
  • Overview of Apache Spark for big data processing
  • Real-time analytics with structured streaming
  • Mini-project: Retail data analysis