Learn the concept, syntax, semantics and gain introductory knowledge in Machine Learning and Deep Learning principles through examples that provide useful exposure to the various concepts of Data Science and Artificial Intelligence.
Course Details:
- Duration: 10 Hrs. (Concepts + Hand on Practices)
- Utilities: Python, IDE-( PyCharm or Jupyter or Spider), Anaconda
- Delivery Mode: Online Live Instructor led learning.
Prerequisites:
- The tutorial is suitable for absolute beginners. But fundamental knowledge of Python and statistics would be helpful
Batch Date: 16th Dec’24 to 21st Dec’24
Curriculum
Session-1 : Introduction-Data Science, ML, DL & AI
- Introduction of Data Science, Artificial Intelligence, ML & Deep Learning
- Evolution of AI
- Application of Artificial Intelligence
- Difference between Traditional Programming and ML Programming
- Requirements for Machine Learning Practical Implementation
- Required software and tools for Machine Learning Implementation
- Setup Anaconda
- Installation of Pycharm
- Configure Pycharm with Anaconda
Session-2 : Concept of Supervise & Unsupervised Machine Learning
- Types of Machine Learning
- Labeled Data and Unlabeled Data
- Concept of Supervised Machine Learning
- Concept of Unsupervised Machine Learning
- Regression and Classification
- Linear Regression and Logistic Regression
Session-3 : Practical implementation of Supervised Machine Learning Algorithm
- Implementation of Supervised Machine Learning Algorithms
- Regression and Classification
- Linear Regression and Logistic Regression
- Practical Implementation of Machine Learning Supervised Algorithms- Linear Regression, Logistic Regression, Concept of Sigmoid Function
Session-4 : Practical implementation of Unsupervised Machine Learning Algorithm
- Concepts and Steps of Unsupervised Machine Learning Algorithm and Clustering
- Practical Implementation of Machine Learning Unsupervised Algorithms- K-Means Clustering.
Session-5 : Introduction to Deep Learning and Neural Networks
- A revolution in Artificial Intelligence
- Limitations of Machine Learning
- What is Deep Learning?
Advantage of Deep Learning over Machine learning
- How Deep Learning Works?
- The Neuron
- Introduction to Neural Networks
Session-6 : Deep dive into Neural Networks
- Neural Network Layers
- Neural Network Architecture
- Activation Functions
- Training a Perceptron
- Generalization, Overfitting, Under fitting
