Asfak Ali
PhD(Pursuing) Department of Electronic and Telecommunication, JU
Machine Learning
About this Course
Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without explicit programming. It involves training a computer system on a large dataset, allowing it to identify patterns, extract insights, and make informed decisions based on the data.
Machine learning techniques can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, enabling it to predict or classify new, unseen data accurately. Unsupervised learning, on the other hand, deals with unlabeled data and aims to discover hidden patterns or structures within the data. Reinforcement learning involves training an agent to interact with an environment, learning to take actions that maximize a reward signal.
Machine learning has numerous applications across various fields, including image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous vehicles, and healthcare. It has revolutionized industries by providing valuable insights, improving efficiency, and enabling automation. However, it also raises ethical considerations, such as bias in algorithms and privacy concerns. Continual advancements in machine learning algorithms and computing power continue to drive innovation and shape the future of technology.
1. The fundamentals of machine learning and its applications.
2. Types of machine learning algorithms, such as supervised and unsupervised learning.
3. Data preprocessing techniques, including data cleaning and feature scaling.
4. The concept of model training and evaluation using different performance metrics.
5. Techniques for handling overfitting and underfitting in machine learning models.
6. Cross-validation methods to assess model performance.
7. Understanding the bias-variance tradeoff and its impact on model accuracy.
8. Feature selection and dimensionality reduction techniques.
9. Regression models for predicting continuous variables.
10. Classification models for predicting categorical variables.
11. Clustering algorithms for unsupervised learning tasks.
12. Model deployment and integration into real-world applications.
August January
Start Date : 2023-08-01
End Date : 2024-01-05
Place : Jadavpur
Timing : 4pm - 6pm
Trainer
Research Scholar, JU
Syllabus
1. Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- Applications of Machine Learning
2. Data Preprocessing
- Data Cleaning
- Handling Missing Data
- Feature Scaling
- Handling Categorical Data
- Data Transformation
3. Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- Naive Bayes Classifier
- k-Nearest Neighbors (k-NN)
4. Model Evaluation and Selection
- Accuracy, Precision, Recall, F1-Score
- Confusion Matrix
- Cross-Validation
- Bias-Variance Tradeoff
- Overfitting and Underfitting
5. Unsupervised Learning Algorithms
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rules
6. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Perceptron
- Feedforward Neural Networks
- Backpropagation Algorithm
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Introduction to Deep Learning Frameworks (TensorFlow, Keras, PyTorch)
7. Feature Selection and Dimensionality Reduction
- Feature Selection Techniques
- Dimensionality Reduction Techniques (PCA, LDA)
8. Model Deployment and Deployment
- Model Serialization
- Web Application Development for Machine Learning
- Model Deployment on Cloud Platforms (AWS, Azure, Google Cloud)
9. Ethics and Bias in Machine Learning
- Bias and Fairness Issues
- Privacy and Security Concerns
- Responsible AI Practices
10. Case Studies and Projects
- Real-world machine learning applications
- Hands-on projects to reinforce concepts
Reviews
4.5 |
6 reviews |
5 stars
66%
4 stars
16%
3 stars
16%
2 stars
0%
1 star
0%
Top Comments...
SARA MUKHERJEE
June 17, 2023 at 09:20 am
The course was fantastic! I really enjoyed every part, every video, every quiz, every assignment of the course. It was a pretty memorable ride to have come this far.
SAYAN PRADHAN
June 17, 2023 at 09:08 am
This was one of the best courses I have ever experienced. There was a subtle beauty in the course's planning . The effort made in his teaching was quite evident, and there was a remarkable balance in the difficulty of the course - no matter whether you are a beginner or experienced with Machine learning, you will enjoy this course!
ARITRA RAY
June 17, 2023 at 08:51 am
Enjoyed thoroughly the course. The mathematics concepts we’re well explained through exercises that help us visualize the concept behind each equation. The exercises were well thought out to help the student bridge theory to practical coding. People not familiar with mathematical coding will start to understand the pattern behind and be self-sufficient. Thank you for building this wonderful course.
SHUVAM DAS
June 17, 2023 at 07:15 am
Great course! and according to me, the ML roadmap that best matches the one I thought to approach the ML topic based on all my experiences. So I recommend this course of Andrew to everyone.
ANKANA PARI
June 17, 2023 at 07:06 am
I had to put extra effort on this one as it delivers broader knowledge on Neural Networks and Decision Trees. Really liked the Fairness, Bias and Ethics section, I'll keep those into consideration.
Shubham Bhakat
June 29, 2023 at 08:40 pm
Hello Everyone Shubham Here I am Currently Pursuing M .SC IN PHYSICS and Recently Joined MACHINE LEARNING Course. Course Are amazing Teacher and Mentor Always Support to Whenever I need. Placement Also Good As Previous Student Feedback. Thank you Code Assembly