Machine Learning Course in Bangalore

1. Basics of using data to train machines
2. Artificial neural network concepts
3. Multiple variables in linear regression using Python
4. Classifying data in logistic regression using Python
5. K-Nearest Neighbor algorithms
6. Data Modeling
7. Decision Tree Classifier

• Software Professionals looking for a career switch and fresh graduates
• Professionals in Analytics, Data Science, E-commerce, and Search Engine domains

We don’t expect any prior knowledge from your side. However, a basic knowledge of programming language can be helpful.

The average salary of a Machine Learning 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 Machine Learning professionals 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 Machine Learning Engineers can only get better in this city.

Machine Learning market trend in Bangalore 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 and grab lucrative jobs in this city.

Machine Learning is one of the most exciting and attractive fields to get into. Being used for self-driving vehicles or for search engines like Google, Machine Learning is extensively used for making our lives simple. Intellipaat’s Machine Learning with R course emphasizes on providing hands-on experience through assignments and a real-time project that have wide implications in various industries.

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.

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.

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.

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.

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.

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 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.

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.

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

Topics:

• 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