Who should attend
Hailed as the best online Machine Learning certification course by several industry experts and thousands of professionals, our ML training program is curated and designed for –
- Professionals working in the field of Data Science, Analytics, BI, Search Engine, and E-commerce domains
- Professionals seeking a career change
- Undergraduates and freshers
What are the prerequisites for taking up this Machine Learning online course?
Everyone can take this Machine Learning online course regardless of their prior knowledge and experience.
About the course
Intellipaat offers an industry-specific Machine Learning course that focuses on key modules such as Python, Algorithms, Statistics & Probability, Supervised & Unsupervised Learning, Decision Trees, Random Forests, Linear & Logistic regression, etc. With these key concepts, you will be well prepared for the role of Machine Learning (ML) engineer. In addition, it is one of the most immersive Machine Learning online courses, which includes hands-on projects and 24-hour learning support to help you gain deep expertise. So, become an ML professional and learn how to create and implement real-world projects such as movie recommendations, chatbot creation, and more. Register now for this Machine Learning certification program!
- Instructor Led ML Training: 32 Hrs
- Self-paced Videos: 32 Hrs
- Exercises & Project Work: 64 Hrs
- Industry-recognized Certification & Job Assistance
- Flexible Schedule
- Lifetime free upgrade
- 24 x 7 Lifetime Support & Access
About Machine Learning Course
Intellipaat's Machine Learning certification course is an online instructor-led training program that is curated and developed by SMEs over the years to meet the needs of the current data-driven industry. Our online Machine Learning certification course provides a detailed overview of Machine Learning topics such as: Using real-time data, creating algorithms using different ML techniques, Regression, Classification, and Time Series Modelling. This Machine Learning training program covers the most popular and widely used Deep Learning technologies and their applications, as well as Natural Language Processing, thus, paving the way for a solid foundation of Machine Learning. Further, in this Machine Learning online course, you will learn how to extract predictions from data using Python!
What will you learn in this Machine Learning course online training?
Our SMEs at Intellipaat has played an important role in the development and dissemination of Machine Learning online courses. In addition, over the years, our trainers have trained more than 600,000 learners, many of whom work in senior positions at large multinational companies. Some of the many concepts that you will introduced with include:
- Training machines using data
- Representation of artificial neural networks
- Perform linear regression on multiple variables using Python
- Categorize data by Python using Logistic regression
- Use a vector machine algorithm
- Designing machine learning systems
- Principal component analysis of data modeling, etc.
Why should I learn Machine Learning?
Do you know Machine Learning job growth is 350%? Also, Automation has become the new face of technology. In this era, Machine Learning has evolved to be one of the hottest technologies out there. By leveraging Intellipaat Machine Learning online training, you will be exposed to numerous job opportunities that will not only be high-paying but also learn-worthy.
What are the objectives of the Intellipaat Machine Learning course?
Intellipaat’s Machine Learning training course are offered online by ML experts who work in top MNCs. By attending our Machine Learning classes, you will master the key concepts of such as Python programming, Supervised and Unsupervised learning, Naïve Bayes, NLP, Deep Learning fundamentals, Time Series Analysis, and more. Each session ends with assignments and tasks that you need to solve based on the available data set. Further, you will work on many industry-specific projects that will solidify your skills and help you find a rewarding job! Further, we will also help you in your career with our exclusive job support services.
What skills will I develop?
You will gain insights into the concepts of types of Machine Learning, recommendation engine, and time series modeling. Further, once you complete all the live-instructor-led Machine Learning classes, you will become proficient in:
- Statistical & Heuristic facets of Machine Learning
- Implementing models such as Support Vector Machines, Kernel SVM, Naïve Bayes, etc.
- Validating ML models and holistically capture the theoretical concepts of ML.
How do I become a Machine Learning professional?
Intellipaat offers one of the best online Machine Learning programs that will help you become proficient in the ML domain. Our expert instructors will make sure that you are familiar with the course modules. On top of that, you will be working on real-world projects that would further enhance your understanding.
What kind of projects will I work on this Machine Learning training?
As part of this ML course, Intellipaat offers you the opportunity to work on the latest, relevant and most valuable real-world projects. This way you can implement what you have learned in practice.
- Each training program includes multiple projects that will thoroughly test your skills, learning and practical knowledge to make you fit for the industry.
- You will work on exciting projects in the fields of e-commerce, automation, marketing, sales, banking, internet, insurance and more.
- Upon successful project completion, your skills will be equivalent to six months of comprehensive industry experience.
Machine Learning Course Content
Introduction to Machine Learning
Need of Machine Learning, introduction to Machine Learning, types of Machine Learning, such as supervised, unsupervised and reinforcement learning, why Machine Learning with Python and applications of Machine Learning.
Supervised Learning and Linear Regression
Introduction to supervised learning, types of supervised learning, such as regression and classification, introduction to regression, simple linear regression, multiple linear regression, assumptions in linear regression, and math behind linear regression.
Hands-on Exercise – Implementing linear regression from scratch with Python, Using Python library Scikit-learn to perform simple linear regression and multiple linear regression, Implementing train–test split and predicting the values on the test set.
Classification and Logistic Regression
Introduction to classification, linear regression vs logistic regression, math behind logistic regression, detailed formulas, log it function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR.
Hands-on Exercise – Implementing logistic regression from scratch with Python, Using Python library Scikit-learn to perform simple logistic regression and multiple logistic regression, Building a confusion matrix to find out accuracy, true positive rate, and false positive rate.
Decision Tree and Random Forest
Introduction to tree-based classification, understanding a decision tree, impurity function, entropy, to understand the concept of information gain for the right split of node, impurity function, information gain, to understand the concept of information gain for the right split of node, impurity function, Gini index, to understand the concept of Gini index for the right split of node, overfitting, pruning, pre-pruning, post-pruning, cost-complexity pruning, introduction to ensemble techniques, understanding bagging, introduction to random forests, and finding the right number of trees in a random forest.
Hands-on Exercise – Implementing a decision tree from scratch in Python, Using Python library Scikit-learn to build a decision tree and a random forest, Visualizing the tree and changing the hyper parameters in the random forest.
Naïve Bayes and Support Vector Machine (self paced)
Introduction to probabilistic classifiers, understanding Naïve Bayes, math behind the Bayes theorem, understanding a support vector machine (SVM), Kernel functions in SVM, and math behind SVM.
Hands-on Exercise – Using Python library Scikit-learn to build a Naïve Bayes classifier and a support vector classifier.
Types of unsupervised learning, such as clustering and dimensionality reduction, types of clustering, introduction to k-means clustering, math behind k-means, and dimensionality reduction with PCA.
Hands-on Exercise – Using Python library Scikit-learn to implement K-means clustering, Implementing PCA (principal component analysis) on top of a dataset.
Natural Language Processing and Text Mining (self paced)
Introduction to Natural Language Processing (NLP), introduction to text mining, importance and applications of text mining, how NPL works with text mining, writing and reading to word files, OS modules, Natural Language Toolkit (NLTK) environment, and text mining: its cleaning and pre-processing and text classification.
Hands-on Exercise – Learning Natural Language Toolkit and NLTK Corpora, Reading and writing .txt files from/to a local drive, Reading and writing .docx files from/to a local drive.
Introduction to Deep Learning
Introduction to Deep Learning with neural networks, biological neural network vs artificial neural network, understanding perceptron learning algorithm, introduction to Deep Learning frameworks, and Tensor Flow constants, variables and place-holders.
Time Series Analysis (Self-paced)
What is time series?, its techniques and applications, time series components, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, the ARIMA model, time series in Python, sentiment analysis in Python (Twitter sentiment analysis), and text analysis.
Hands-on Exercise – Analyzing time series data, the sequence of measurements that follow a non-random order to recognize the nature of phenomenon, and forecasting the future values in the series.
Machine Learning Projects & Case Studies
What projects and case studies I will be working on in this Machine Learning certification course?
Case Study 1: Decision Tree
Topics: To understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by using Scikit-learn
Case Study 2: Insurance Cost Prediction (Linear Regression)
Topics: To understand the structure of a medical insurance dataset, implement both simple and multiple linear regression, and predict values
Case Study 3: Diabetes Classification (Logistic Regression)
Topics: To understand the structure of a dataset (PIMA Indians Diabetes dataset), and implement multiple logistic regression and classify[I3] . Fit your model on the test and train data for prediction and evaluate your model using confusion matrix and then visualize it
Case Study 4: Random Forest
Topics: To create a model that would help in classifying whether a patient is ‘Normal’, ‘Suspected to have disease,’ or in actuality ‘Has the disease’ on the ‘Cardiotocography’ dataset
Case Study 5: Principal Component Analysis (PCA)
Topics: Read the sample iris dataset given to you, use PCA to figure out the number of most important principal features, and then reduce the number of features using PCA. Train and test the Random Forest Classifier algorithm to check if reducing the number of dimensions is causing the model to perform poorly. Figure out the most optimal number that produces good quality results and predicts accuracy
Case Study 6: K-means Clustering
- Analyze data
- Extract useful columns from the dataset
- Visualize data
- Find out the appropriate number of groups or clusters for data to be segmented into (using the elbow method)
- Using k-means clustering, segment data into k groups (k is found in the previous step)
- Visualize a scatter plot of clusters, and a lot more
Project 1: Customer Churn Classification
Topics: This is a real-world project that gives you hands-on experience in working with most of the Machine Learning algorithms. The main components of the project include the following:
- Manipulating data to extract meaningful insights
- Visualizing data to find out patterns among different factors
- Implementing these algorithms: linear regression, decision tree, and Naïve Bayes
Project 2: Recommendation for Movie, Summary
Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference,’ and so on. You will exclusively work on data related to user details, movie details, and others. The main components of the project include the following:
- Recommendation for movies
- Two types of predictions: Rating prediction and item prediction
- Important approaches: Memory-based and model-based
- Knowing user-based methods in K-Nearest Neighbor
- Understanding the item-based method
- Matrix factorization
- Decomposition of singular value
- Data Science project discussion
- Collaboration filtering
- Business variables overview
Machine Learning Certification
Intellipaat’s course on Machine Learning is designed by industry professionals that will help you get the best jobs in top MNCs. As part of this ML training, you will be engaged in real-time projects and assignments that have huge implications in real-world industry scenarios. This way, you can expedite your career effortlessly.
At the end of this ML certification training course, you will find a quiz test that perfectly reflects the type of questions asked in the Machine Learning Certification exam, it will further help get a higher score.
Intellipaat Course Completion Certification will be issued after the project has been completed (after expert review) and upon scoring at least 60 percent on the quiz. You would be glad to know that Intellipaat certification is recognized by more than 100 top multinational companies, including Cisco, Ericsson, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered Bank, IBM, Infosys, Genpact, TCS, Hexaware and more.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented ...
A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data...
Videos and materials
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