Machine Learning
Duration: 14 Days
Course Fee: Rs.999 /-
Rs.16500
Course Overview:
This syllabus is designed to provide participants with an immersive and hands-on introduction to machine learning. The course covers fundamental machine learning concepts, algorithms, model evaluation, and practical implementation using popular Python libraries such as scikit-learn and TensorFlow. Participants will gain a solid foundation in both supervised and unsupervised learning techniques.
Prerequisites:
Basic understanding of programming concepts, preferably in Python. Familiarity with basic mathematics and statistics concepts is beneficial but not mandatory.
What you will learn
Introduction to Machine Learning
Materials included
Free certificate
Life time video access
Future Support
Live sessions on Google Meet
Requirements
Basic understanding of programming concepts
Familiarity with basic mathematics and statistics concepts
Course Syllabus
Day 1-2: Introduction to Machine Learning
- Overview of machine learning and its applications
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Introduction to Python libraries: NumPy, Pandas, and Matplotlib
Day 3-4: Data Preprocessing and Exploration
- Data cleaning and handling missing values
- Feature scaling and normalization
- Exploratory Data Analysis (EDA)
- Feature engineering and transformation
Day 5-6: Supervised Learning - Regression
- Linear regression
- Polynomial regression
- Regularization techniques: Lasso and Ridge regression
- Model evaluation metrics for regression
Day 7-8: Supervised Learning - Classification
- Logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- Model evaluation metrics for classification
Day 9-10: Unsupervised Learning - Clustering
- K-means clustering
- Hierarchical clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Evaluation metrics for clustering
Day 11-12: Unsupervised Learning - Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Autoencoders for dimensionality reduction
- Applications of dimensionality reduction
Day 13-14: Introduction to Neural Networks and TensorFlow
- Basics of neural networks
- Introduction to TensorFlow
- Building and training a simple neural network
- Transfer learning with pre-trained models
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