3-month AI/ML training program

Join our 3-month AI/ML training program to build a strong foundation in artificial intelligence and machine learning. Learn through hands-on projects, gain valuable skills, and kickstart your career in this dynamic field.

Objective

  • Build foundational AI/ML knowledge and practical skills for entry-level tasks.
  • Understand key concepts in deep learning, with a focus on ANN.
  • Complete a guided capstone project to showcase learning.

Structure

Weekly progress combines theoretical sessions, hands-on tasks, and project work to ensure steady growth.

Month 1: Fundamentals of AI/ML and Tools

Week 1: Introduction to AI and ML

  • Topics:
    • What is AI? Branches of AI and real-world applications.
    • What is ML? Types of ML: Supervised, Unsupervised, Reinforcement Learning.
    • Overview of ML workflows.
  • Tasks:
    • Identify ML applications in day-to-day life.
    • Set up Python and essential libraries (NumPy, Pandas, Matplotlib).

Week 2: Python for Data Analysis

  • Topics:
    • Data exploration and manipulation using Pandas.
    • Data visualization with Matplotlib and Seaborn.
  • Tasks:
    • Perform EDA on a sample dataset.
    • Generate summary statistics and data visualizations.

Week 3: Introduction to Machine Learning Algorithms

  • Topics:
    • Linear Regression, Logistic Regression.
    • Performance metrics: Accuracy, Precision, Recall, and F1-score.
  • Tasks:
    • Build and evaluate a Linear Regression model for predicting housing prices.

Week 4: Data Preprocessing and ML Pipelines

  • Topics:
    • Handling missing data, scaling, encoding categorical variables.
    • Introduction to ML pipelines.
  • Tasks:
    • Create a pipeline for preprocessing and training a model using scikit-learn.

Month 2: Core Machine Learning and Deep Learning Foundations

Week 5: Decision Trees and Ensemble Methods

  • Topics:
    • Decision Trees: Working and applications.
    • Ensemble methods: Bagging (Random Forest), Boosting (Gradient Boosting).
  • Tasks:
    • Build a Random Forest model for classification tasks.

Week 6: Unsupervised Learning

  • Topics:
    • K-Means Clustering, Hierarchical Clustering.
    • Dimensionality Reduction: PCA.
  • Tasks:
    • Apply K-Means to segment customers in a retail dataset.

Week 7: Introduction to Deep Learning and Artificial Neural Networks (ANN)

  • Topics:
    • What is Deep Learning? ANN architecture and components.
    • Activation functions: Sigmoid, ReLU, Softmax.
    • Forward propagation and backpropagation.
  • Tasks:
    • Implement an ANN for binary classification using TensorFlow/Keras.

Week 8: Advanced ANN Concepts

  • Topics:
    • Optimizers: Gradient Descent, Adam.
    • Regularization techniques: Dropout, Batch Normalization.
  • Tasks:
    • Improve an ANN model using dropout and different optimizers.

Month 3: Capstone Project and Future Skills

Week 9: Capstone Project Planning

  • Activities:
    • Select a dataset (e.g., loan approval, e-commerce trends).
    • Define the project’s objectives and success metrics.

Week 10: Model Development

  • Activities:
    • Build an end-to-end ML model pipeline, train, and evaluate.
    • Document findings and insights.

Week 11: Deployment Basics

  • Topics:
    • Introduction to model deployment using Flask/Streamlit.
  • Tasks:
    • Create a simple interface to showcase the project.

Week 12: Presentation and Feedback

  • Activities:
    • Present the project to mentors and peers.
    • Receive feedback and refine the project.