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Course Overview
Course Duration : 3 Months
Machine learning is a subtechnology of artificial intelligence that is pushing many innovations today. Machine learning is about creating technology, using realworld data, that learns and adapts on its own. Today, this technology is powering so many other technologies and advancements that you may be using it without knowing it. Machine learning has become so vast that it’s taught as a separate subject. To become a machine learning expert, DigiAcharya is offering a comprehensive machine learning course.
DigiAcharya’s machine learning course covers supervised and unsupervised learning(neural networks, algorithms, support vector machines, deep learning, recommender systems), interactive labs, live projects, deep technical knowhow, training in Python, and R with access to the latest libraries. Students are also mentored and taught to solve modern problems using machine learning and develop learning models. Therefore, to get a strong career in machine learning, choose DigiAcharya’s machine learning course.
Our program is suitable for
Machine learning is an important subject, so the earlier you start the better. To be eligible to pursue DigiAcharya’s machine learning course, you need to have cleared your higher secondary with a minimum percentage of 50%. That’s all. Even professionals looking to get a competitive edge and career boost can enrol in our course. The course has been designed and curated by actual industry experts and will prepare you for an excellent career.
Seek new possibilities with machine learning course
Strike the right cord when dealing with future job prospects
Course Module
Module 1  Introduction to Machine Learning
This module deals with Python Ecosystem, Methods for Machine Learning, Understanding Data with Visualization and more. After completing this module, students will be able to understand Density Plots, Box and Whisker Plots, Multivariate Plots: Interaction among Multiple Variables and Importance of Data Feature Selection alongwith Principal Component Analysis (PCA).
Module 2:MACHINE LEARNING ALGORITHMS – CLASSIFICATION
This module deals with an Introduction to Classification and Types of Learners in Classification. After completing this module, students will have a deep understanding of Logistic Regression, Support Vector Machine (SVM), Decision Tree, Naïve Bayes and Random Forest algorithm.
Module 3: MACHINE LEARNING ALGORITHMS  REGRESSION
Regression analysis consists of a set of machine learning methods that allow us to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x). After completing this module, students will be able to understand Types of Linear Regression and Multiple Linear Regression (MLR) alongwith their Python Implementation.
Module 4:MACHINE LEARNING ALGORITHMS – CLUSTERING
Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as “A way of grouping the data points into different clusters, consisting of similar data points.This module deals with all the Clustering Algorithms. After completing this module, students will be able to work with Kmeans Algorithm, Mean Shift Algorithm and Hierarchical Clustering.
Module 5: MACHINE LEARNING ALGORITHMS  KNN ALGORITHM
The abbreviation KNN stands for “KNearest Neighbour”. It is a supervised machine learning algorithm.This module deals with KNN Algorithm and Finding Nearest Neighbors. After completing this module, students will be able to understand Performance Metrics Automatic Workflows and Improving Performance of ML Models.
PLATFORMS AND TOOLS
Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modelling. They allow the software to become more accurate in predicting outcomes without being explicitly programmed.
IBM Watson Studio
Jupyter Notebook
DataRobot
What skills you will gain
Supervised machine learning
Supervised learning, also known as supervised machine learning, is defined by its use of labelled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately. This occurs as part of the crossvalidation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve a variety of realworld problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve Bayes, linear regression, logistic regression, random forest, support vector machine (SVM), and more.
Unsupervised machine learning
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyse and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, crossselling strategies, customer segmentation, image and pattern recognition. It’s also used to reduce the number of features in a model through the process of dimensionality reduction; principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, kmeans clustering, probabilistic clustering methods, and more.
Semisupervised learning
Semisupervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labelled data set to guide classification and feature extraction from a larger, unlabelled data set. Semisupervised learning can solve the problem of having not enough labelled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
 Supervised machine learning

Supervised machine learning
Supervised learning, also known as supervised machine learning, is defined by its use of labelled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately. This occurs as part of the crossvalidation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve a variety of realworld problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve Bayes, linear regression, logistic regression, random forest, support vector machine (SVM), and more.
 Unsupervised machine learning

Unsupervised machine learning
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyse and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, crossselling strategies, customer segmentation, image and pattern recognition. It’s also used to reduce the number of features in a model through the process of dimensionality reduction; principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, kmeans clustering, probabilistic clustering methods, and more.
 Semisupervised learning

Semisupervised learning
Semisupervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labelled data set to guide classification and feature extraction from a larger, unlabelled data set. Semisupervised learning can solve the problem of having not enough labelled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
Career Roles
Full Stack Web Development Using Python Course from Digi Acharya allows the candidates to explore a new world of enormous opportunities. With this course, one can keep upskilling and aim at the topmost government or private institutions worldwide.
Choose a job as a Machine Learning Engineer in a renowned institution
Be a Computational Linguist for a business chain
Get ready to be a HumanCentered Machine Learning Designer with public or private sectors
Opt for a Data Scientist job under the IT sector of banks
Want to have an everblooming career with desirable paychecks?
Want to have an everblooming career with desirable paychecks?
FAQ's
There are absolutely no prerequisites! Since we begin from scratch anyone can take up this course. As long as you are interested and want to learn, you are welcome!
The best machine learning engineers these days are paid as much as immensely popular sports personalities! And that’s no exaggeration! According to Glassdoor.co.in, the average machine learning engineer salary is 8 lakhs per annum – and that’s just at the starting of one’s career! An experienced machine learning engineer takes home anywhere between 15 to 23 lakhs per annum.
Statistics, linear algebra, probability, and calculus are the four essential ideas that drive machine learning. While statistical ideas are essential to all models, calculus allows us to understand and optimize them. You don’t have to be an expert in mathematics to be good at machine learning. You cannot escape math when you want to be good at machine learning, but at the same time, you don’t have to be a pro at it. All you need to know are the fundamentals of arithmetic for machine learning, and you’re good to go.