Kids IT Courses

Machine Learning

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.

Machine learning is a field of artificial intelligence that focuses on creating algorithms and models that enable computers to learn from data and make decisions without explicit programming. It involves training systems on large datasets to identify patterns and make predictions or classifications. Machine learning encompasses various techniques, including supervised learning, where models are trained on labeled data, and unsupervised learning, which involves finding hidden patterns in unlabeled data. It also includes reinforcement learning, where models learn through trial and error by interacting with their environment. Applications of machine learning are broad and include areas such as image and speech recognition, natural language processing, and predictive analytics. By leveraging advanced algorithms and large amounts of data, machine learning can automate complex tasks, uncover insights, and drive innovation across numerous industries.
A Machine Learning course is designed to introduce students to the concepts, algorithms, and techniques used in the field of artificial intelligence to enable computers to learn from data. The course typically covers foundational topics like supervised and unsupervised learning, classification, regression, clustering, and deep learning. Students are introduced to popular machine learning algorithms, such as decision trees, neural networks, and support vector machines, and learn how to implement them using programming languages like Python along with libraries such as TensorFlow and Scikit-learn. 

The course emphasizes practical applications, teaching students to handle real-world datasets, preprocess data, and build predictive models to solve problems in industries such as finance, healthcare, and technology. Students also explore evaluation metrics to measure model accuracy and techniques for optimizing performance. By the end of the course, learners will have developed the ability to design, implement, and deploy machine learning models, making them well-prepared for careers in data science, AI development, and related fields.

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