Machine Learning

This course provides a brief introduction to the basic machine learning concepts and popular machine learning algorithms. At Allsoft we cover the most popular supervised learning algorithms, including linear regression, logistic regression, decision trees, k-nearest neighbors, an introduction to Bayesian learning and the naive Bayesian algorithm, support vector machines and kernels, and an introduction to neural networks Deep learning.

The science of computers without explicit programming is machine learning. Machine learning over the past decade has enabled us to advance ourselves, recognize language practices, efficiently browse the website, and understand the human genome much better.

Machine learning is so ubiquitous today that you may not be familiar with it a dozen times a day. Many researchers also believe that this is the best strategy for advancing human AI.

In this lesson, you will learn about the most effective machine learning methods and how to implement them and make them work for you. Above all, you not only learn the theoretical basics of learning, but also acquire the practical know-how to apply these strategies quickly and sustainably to new challenges. Finally, you'll learn about some of the best innovation practices in Silicon Valley for machine learning and AI.

This course provides a comprehensive introduction to automated learning, data analysis, and statistical pattern recognition.

Topics are: I supervised learning (algorithms, parameters / non-parametric, vector support, kernel, neural networks). (ii) Uncontrolled study (clustering, dimensionality reduction, recommendation systems, deep learning). (iii) machine learning best practices (bias / variance theory; machine learning and AI innovation process);

You will also learn how to use the learning algorithms to develop intelligent robots (perspective, surveillance), text comprehension (web search, antispam), computer vision, medical information, audio, database and other areas. The course also includes case studies and applications.

Course Outline:

• Unit-I
• Introduction to Machine Learning
• Definition of learning systems. 
• Goals and applications of machine learning
• Application of Machine Learning
• Supervised vs Unsupervised Learning
• Python libraries suitable for Machine Learning
• Unit-II
• Data Pre-processing and Data Scaling Methods
• Identifying and handling the missing values
• Encoding the categorical data
• Normalization
• Standardization
• PCA
• Unit-III
• Regression 
• Linear Regression
• Non-Linear Regression
• Model evaluation methods
• Overfitting, Underfitting
• Unit-IV
• Classification
• K-Nearest Neighbour
• Decision Tree
• Logistic Regression
• Support Vector Machines
• Model Evaluation
• Unit-V
• Unsupervised Learning
• K-means Clustering
• Hierarchical Clustering
• Density-Based Clustering
• Unit-VI
• Recommender Systems and Introduction to Deep Learning
• Content-based recommender system
• Collaborative Filtering
• Deep Learning and Biological Neurons 
• Perceptron, multilayer network,
• Introduction to deep neural network.