Machine Learning is the science of computers to act without being programmed course.
For example, a self-driving vehicle can learn how the human driver is driving and then drive like him/ her.
Moreover, Or when a cucumber sorting machine learns from humans how the cucumbers are sorted and then goes on to perform the task.
Besides, Machine learning knowledge is impressive on any resume!
In our AI introductory course, we’ll build on the basics and explore image classification, along with key ideas such as neural networks. .
Students apply their knowledge to real data sets and draw poignant conclusions.
Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Unlike traditional programming, where explicit instructions are given, machine learning systems improve their performance over time by analyzing patterns and relationships within data.
Machine learning encompasses various techniques, including supervised learning, where models are trained on labeled data to make predictions, and unsupervised learning, where models identify patterns and structures in unlabeled data. It also includes reinforcement learning, where models learn by interacting with an environment and receiving feedback.
Applications of machine learning are vast and include tasks such as image and speech recognition, natural language processing, recommendation systems, and predictive analytics. By leveraging large datasets and sophisticated algorithms, machine learning systems can automate complex processes, uncover insights, and drive innovation across numerous fields, including finance, healthcare, marketing, and beyond.
Session 1 : Basic Regression Analysis
- Linear Regression
- Mean squared error
- Training set vs Test set
- Cross validation
Session 2 : Advanced Regression Analysis
- Multi-linear regression
- Feature engineering Overfitting
Session 3 : Logistic Regression
- Regression vs Classification
- Logistic Regression
- Sigmoid function
Session 4 : K-nearest Neighbors
- K-nearest neighbors
- Model-based vs memory-based
- Parametric vs non-parametric
- Evaluating performance
Session 5 : Decision Trees
- Decision tree
- Interpretability
- Bias-variance tradeoff
Session 6 : Random forest
- Random forest
- Ensemble methods
- Hyperparameters