Intellipaat Machine Learning course in Bangalore will help you to be a master in the concepts and techniques of Machine Learning with Python, which include ML algorithms, supervised and unsupervised learning, probability, statistics, decision tree, random forest, linear and logistic regression through real-world hands-on projects. Get the best Machine Learning training in Bangalore from top data scientists.
Intellipaat is one of the renowned names in the domain of e-learning, offering you the most comprehensive and career-oriented Machine Learning online course in Bangalore, India. Learners will be getting in-depth knowledge and expertise in the highly coveted concepts of Python Programming such as supervised and unsupervised learning, probability, statistics, decision tree, random forest, linear and logistic regression, and a lot more through this training. Successfully completing this training course will equip you with the skill sets related to Statistics, Time Series, and different classes of ML algorithms.
Intellipaat, one of the premier Machine Learning Institutes in Bangalore provides a world class course on ML in which you can learn:
Intellipaat’s Machine Learning Python course can be joined by:
We don’t expect any prior knowledge from your side. However, a basic knowledge of programming language can be helpful when joining the Machine Learning classes in Bangalore.
The average salary of an ML Engineer in Bangalore, Karnataka, is Rs.1,095,796. per year – PayScale
Bangalore is the hub of some of the best IT companies, and due to this the demand for professionals in this domain is at an all-time high. This combined with the number of startups mushrooming in the Silicon Valley of India clarifies that the future for ML Engineers can only get better in this city.
ML market trend in city is growing at a rapid rate. The growth of technology in this city has made marketers compare it with the Silicon Valley of the USA. The focus of multinational firms on this city has created a huge competition which can be managed by analyzing the market conditions. Machine Learning with Python Programming serves this purpose, and hence aspirants wanting to become successful Machine Learning Engineers can clearly benefit from the course. By having a Machine Learning certification, it increases the possibilities of getting employed and bagging lucrative jobs in the IT city.
Some of the reasons why you should sign up for this course are as follows:
Intellipaat offers one of the best Machine Learning courses in Bangalore. This certification program is led by Machine Learning experts from leading industries in India and the US. It focuses on helping you understand the ML fundamentals such as Natural Language Processing (NLP), Python, and more. Moreover, you will work on real-world assignments and projects that will enhance your learning experience.
1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression
1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set
3.1 Introduction to classification
3.2 Linear regression vs logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate
4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-Learn to build a decision tree and a random forest
3. Visualizing the tree and changing the hyper-parameters in the random forest
5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM
1. Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier
6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA
1. Using Python library Scikit-Learn to implement k-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset
7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification
1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive
8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
3. Forecasting the future values in the series
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Project 01: Analyzing the Trends of COVID-19 with Python
Problem Statement: Understanding the trends of COVID-19 spread and checking if restrictions imposed by governments around the world have helped us curb COVID-19 cases and by what degree
Topics: In this project, we will use Data Science and Python and perform visualizations to better understand the data on COVID-19. We will also use time series analysis to make predictions about future cases.
Project 02: Customer Churn Classification
Topics: This is a real-world project that gives you hands-on experience in working with most of the ML algorithms.
Project 03: Creating a Recommendation System for Movies
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 requires you to deeply understand information filtering, recommender systems, user ‘preference’, and more. You will exclusively work on data related to user details, movie details, and others.
Case Study 01: Decision Tree
Topics: 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 02: Insurance Cost Prediction (Linear Regression)
Topics: Understand the structure of a medical insurance dataset, implement both simple and multiple linear regressions, and predict values.
Case Study 03: Diabetes Classification (Logistic Regression)
Topics: Understand the structure of a dataset (PIMA Indians Diabetes dataset); implement multiple logistic regressions and classify; fit your model on the test and train data for prediction; evaluate your model using confusion matrix, and then visualize it
Case Study 04: Random Forest
Topics: Create a model that would help in classifying whether a patient ‘is normal,’ ‘is suspected to have a disease,’ or in actuality ‘has the disease’ using the ‘Cardiotocography’ dataset
Case Study 05: Principal Component Analysis (PCA)
Topics: Read the sample Iris dataset; 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, and figure out the most optimal number that produces good quality results and predicts accurately
Case Study 06: K-means Clustering
Topics: Analyze data; extract useful columns from the dataset; visualize the data; find out the appropriate number of groups or clusters for the data to be segmented (using the elbow method); using k-means clustering, segment the data into k groups (k is found in the previous step); visualize a scatter plot of clusters, and a lot more
Intellipaat’s course on ML, one of the most reputed Machine Learning courses in Bangalore is designed by industry professionals that will help you get the best jobs in top MNCs. As part of this Machine Learning with Python 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 Machine Learning training in Bangalore, 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.
Our Alumni works at top 3000+ companies
Intellipaat provides comprehensive Machine Learning training through hands-on projects and case studies. A few of the many reasons for choosing Intellipaat ML course includes:
Intellipaat has been serving ML enthusiasts from every corner of the city. You can be living in any locality in Bengaluru, be it Marathalli, Koramangala, Btm Layout, Jayanagar, Sarjapur, Vijaynagar, Whitefield, HSR Layout, Indira Nagar, Electronic City or anywhere. You can have full-access to our Machine Learning online course sitting at home or office 24/7.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat offers self-paced training to those who want to learn at their own pace. This training also gives you the benefits of query resolution through email, live sessions with trainers, round-the-clock support, and access to the learning modules on LMS for a lifetime. Also, you get the latest version of the course material at no added cost.
Intellipaat’s self-paced training is 75 percent lesser priced compared to the online instructor-led training. If you face any problems while learning, we can always arrange a virtual live class with the trainers as well.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.