AI & Machine Learning Microdegree / Guided Internship

Unlock the limitless potential of AI and machine learning to revolutionize industries with our comprehensive course, "AI & Machine Learning Using Python: Industry-Focused Training." 🌟 This meticulously crafted program is designed to equip you with essential skills and real-world applications of AI and machine learning, leveraging the powerful Python programming language. Whether you're aiming to advance your career or kickstart new opportunities in tech-driven sectors, this course offers the perfect blend of theory, practical insights, and hands-on experience. 🚀

Why Choose This Course?

• Industry-Relevance: Learn techniques and tools directly applicable to real-world industry challenges.
• Project-Based Learning: Engage in real-world projects that simulate industry problems and solutions.

Key Learning Outcomes

• Foundational AI & ML Knowledge: Understand core concepts and algorithms in machine learning and AI.
• Python Programming: Gain fluency in Python with a focus on data libraries like Pandas, Numpy, and Scikit-learn.
• Real-World Applications: Apply ML models to industry-specific problems, enhancing decision-making and efficiency.
• Problem Solving Skills: Develop critical thinking and problem-solving skills to tackle complex challenges in your field.

Course Modules

• Module-01: Introduction to Machine Learning

  • Overview: Fundamental concepts and significance of Machine Learning.
  • Key Topics: Types of Machine Learning (Supervised, Unsupervised, Reinforcement), real-world applications, basic terminologies, and an introduction to the machine learning workflow.

• Module-02: Introduction to Python Programming

  • Overview: Basics of Python programming language.
  • Key Topics: Python syntax, data types, control structures, functions, and libraries essential for Machine Learning.

• Module-03: Introduction to Pandas

  • Overview: Introduction to the Pandas library for data manipulation and analysis.
  • Key Topics: DataFrames, Series, data cleaning, merging, and grouping data.

• Module-04: Introduction to Numpy

  • Overview: Basics of the Numpy library for numerical computing.
  • Key Topics: Numpy arrays, mathematical operations, array manipulation, and statistical operations.

• Module-05: Data Pre-processing & Data Visualization

  • Overview: Techniques for preparing data for machine learning models and visualizing data.
  • Key Topics: Handling missing values, normalization, standardization, data visualization libraries like Matplotlib and Seaborn.

• Module-06: Linear & Logistic Regression

  • Overview: Fundamental linear models for regression and classification.
  • Key Topics: Simple and multiple linear regression, logistic regression, cost function, gradient descent, and evaluation metrics.

• Module-07: Supervised Learning Techniques

  • Overview: Advanced supervised learning algorithms.
  • Key Topics: Decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and model evaluation.

• Module-08: Unsupervised Learning

  • Overview: Introduction to unsupervised learning techniques.
  • Key Topics: Clustering algorithms (K-means, hierarchical clustering), dimensionality reduction techniques (PCA, t-SNE).

• Module-09: Introduction to Neural Networks and Deep Learning

  • Overview: Basics of neural networks and deep learning.
  • Key Topics: Neural network architecture, backpropagation, activation functions, introduction to deep learning frameworks like TensorFlow and Keras.

• Module-10: Advanced Machine Learning and AI Topics

  • Overview: Advanced topics in machine learning and AI.
  • Key Topics: Ensemble methods, reinforcement learning, natural language processing (NLP), and computer vision.

• Module-11: Evaluation Metrics and Model Optimization

  • Overview: Techniques for evaluating the performance of machine learning models.
  • Key Topics: Accuracy, precision, recall, F1 score, ROC-AUC, cross-validation, and overfitting/underfitting.

• Module-12: Ethical AI and Future Trends

  • Overview: Ethical considerations in AI and emerging trends.
  • Key Topics: Bias and fairness in AI, privacy issues, the impact of AI on society, and future directions in AI research.

• Module-13: Capstone Project: Predictive Analytics for Industry-Specific Applications

  • Overview: Engage in a comprehensive project that mirrors real-world applications, focusing on creating an AI-driven loan eligibility prediction system.
  • Key Topics: Data handling, feature engineering, model development and evaluation, ethical considerations, and practical deployment.

Target Audience

This course is designed for high school and college students, as well as professionals looking to transition into the field of machine learning and AI.

Prerequisites

Basic knowledge of Python Programming is recommended but not required. The course will cover the necessary Python programming skills.

Outcome

By the end of this course, students will have a comprehensive understanding of machine learning and AI, enabling them to develop, evaluate, and deploy machine learning models effectively. 📊🤖