Machine Learning Training

Viswa Online Trainings is one of the world’s leading online IT training providers. We deliver a comprehensive catalog of courses and online training for freshers and working professionals to help them achieve their career goals and experience our best services.

4627 Reviews 4.9
4.7/5

Learners : 1080

Duration :   Days

About Course

Our Machine learning Training is an exciting topic about designing machines that can learn from examples. The course covers the necessary theory, principles, and algorithms for machine learning. The methods are based on statistics and probability– which have now become essential to designing systems exhibiting artificial intelligence. Best Online Training

Machine Learning Training Course Syllabus

Introduction to MachineLearning

✔ What is MachineLearning
✔ Who is a Data Scientist
✔ How ML is Different from Programming
✔ Languages
✔ Use cases of ML
✔ Techniques used in ML

✔ Data Collection
✔ Data Cleansing
✔ Data Visualization
✔ Web Scrapping

Machine Learning Algorithms

✔ Supervised Learning

✔ Classification
✔ Regression

✔ Un-Supervised Learning

✔ Association Rules
✔ Clustering

✔ Time Series Analysis

Classification Using Nearest Neighbours

✔ Understanding classification using Nearest Neighbours
✔ The KNN algorithm
✔ Different Distance Metrics
✔ Choosing an appropriate k
✔ Preparing data for use with KNN
✔ Strengths And Weaknesses of KNN
✔ Collecting data
✔ Exploring and preparing the data

✔ normalizing numeric data
✔ creating training and test datasets

✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ z-score standardization
✔ Testing alternative values of k
✔ Why KNN is Lazy Learner?
✔ Case Study

Classification Using Naive Bayes

✔ Understanding naive Bayes

✔ Basic concepts of Bayesian methods
✔ Probability
✔ Joint probability
✔ Conditional probability with Bayes theorem

✔ The Naive Bayes algorithm

✔ The naive Bayes classification
✔ The Laplace estimator
✔ Using numeric features with naive Bayes
✔ Strengths and Weakness

✔ Spam Filtering Case Study Machine Learning

✔ Collecting data
✔ Exploring and preparing the data
✔ processing text data for analysis
✔ creating training and test datasets
✔ Visualizing text data-word clouds
✔ creating indicator features for frequent words

✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ Case Study

Classification Using Decision Trees Machine Learning

✔ Understanding decision trees
✔ Divide conquer
✔ The Decision tree algorithm

✔ Choosing the best split
✔ Pruning the decision tree

✔ Entropy
✔ Information Gain.
✔ Strengths And Weakness
✔ Identifying risky bank loans using Decision trees

✔ Collect data
✔ Exploring and preparing the data
✔ Data preparation-creating random training and test datasets
✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ Boosting the accuracy of decision trees
✔ Making some mistakes more costly than others

Forecasting Numeric Data –Regression

✔ Understanding Regression
✔ Simple linear Regression
✔ Least-squares Estimation
✔ Correlations
✔ Multiple linear regression
✔ Predicting medical expenses using Linear Regression

✔ Collecting data
✔ Exploring and preparing data
✔ Exploring relationships among features- the correlation matrix
✔ Visualizing relationships among features –the scatterplot matrix
✔ Training a model on the data
✔ Evaluating model performance

Logistic Regression

✔ ODD’s Ratio
✔ Applying Logistic Regression
✔ Training a model on the data
✔ Evaluating the Model Performance
✔ Improving of Model Performance
✔ Other Types of Regressions

✔ Polynomial Regression
✔ Ridge Regression
✔ Lasso Regression
✔ Quantile Regression

Market Basket Analysis using Association Rules

✔ Understanding association rules
✔ The Apriori algorithm for association rule learning
✔ Measuring rule interest –support and confidence
✔ Building a set of rules with the Apriori
✔ Identifying frequently purchased groceries with association rules
✔ Creating a sparse matrix for transaction data
✔ Visualizing item support –item frequency plots
✔ Visualizing transaction data-plotting the sparse matrix
✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance
✔ Sorting the set of association rules
✔ Taking subsets of association rules
✔ Saving association rules to a file or data frame

Finding Groups of Data- Clustering with K-Means

✔ Understanding clustering
✔ Clustering as a machine learning task
✔ The K-means algorithm for clustering
✔ Using distance to assign and update cluster
✔ Choosing the appropriate number of cluster
✔ Finding Teen market segments using
✔ K-means clustering
✔ Collecting data
✔ Exploring and preparing the data
✔ Dummy coding missing values
✔ Imputing missing values
✔ Training a model on the data
✔ Evaluating model performance
✔ Improving model performance

Evaluating Model Performance

✔ Working with classification prediction data
✔ Using confusion matrices to measure performance
✔ Beyond accuracy – another measure of performance
✔ The kappa statistic
✔ Sensitivity and specificity
✔ Precision and recall
✔ The F – measure
✔ Visualizing performance ROC curves
✔ Precision and Recall Curves
✔ Estimating future performance

✔ The holdout method
✔ Cross-validation
✔ Bootstrap sampling

Improving Model Performance

✔ Tuning models for better performance
✔ Hyper Parameters
✔ Creating a simple tuned model
✔ Customizing the tuning process
✔ Improving model performance with meta-learning

✔ Understanding ensembles
✔ Bagging
✔ Boosting
✔ Random forests
✔ Training random forests
✔ Evaluating random forests Performance

Sentimental Analysis Machine Learning

✔ Extracting the Data from Twitter/Facebook using API
✔ Cleaning the Data
✔ Performing Text Analysis
✔ Performing Sentimental Analysis

Real Time Scenarios Machine Learning

✔ Deployment of Models
✔ Practical Issues
✔ Project Implementation
✔ Case Studies
✔ Cloud Based API’s

Live Instructor Based Training With Software
Lifetime access and 24×7 support
Certification Oriented content
Hands-On complete Real-time training
Get a certificate on course completion
Flexible Schedules
Live Recorded Videos Access
Study Material Provided

Machine Learning Training - Upcoming Batches

7th NOV 2022

8 AM IST

Weekday

Coming Soon

AM IST

Weekday

5th NOV 2022

8 AM IST

Weekend

Coming Soon

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Weekend

Don't find suitable time ?

CHOOSE YOUR OWN COMFORTABLE LEARNING EXPERIENCE

Live Virtual Training

  • Schedule your sessions at your comfortable timings.
  • Instructor-led training, Real-time projects
  • Certification Guidance.
Preferred

Self-Paced Learning

  • Complete set of live-online training sessions recorded videos.
  • Learn technology at your own pace.
  • Get access for lifetime.

Corporate Training

  • Learn As A Full Day Schedule With Discussions, Exercises,
  • Practical Use Cases
  • Design Your Own Syllabus Based
For Business

Machine Learning Training FAQ'S

What do you understand by Machine learning?

Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed.

For example, Robots are coded in such a way that they can perform the tasks based on data they collect from sensors. They automatically learn programs from data and improve with experiences.

What is the difference between Data Mining and Machine Learning?

Data mining can be described as the process in which structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used.

Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.

Why overfitting occurs?

The possibility of overfitting occurs when the criteria used for training the model is not as per the criteria used to judge the efficiency of a model.

What is the method to avoid overfitting?

Overfitting occurs when we have a small dataset, and a model is trying to learn from it. By using a large amount of data, overfitting can be avoided. But if we have a small database and are forced to build a model based on that, then we can use a technique known as cross-validation. In this method, a model is usually given a dataset of known data on which training data set is run and a dataset of unknown data against which the model is tested. The primary aim of cross-validation is to define a dataset to "test" the model in the training phase. If there is sufficient data, 'Isotonic Regression' is used to prevent overfitting.

Get ahead in your career by learning Machine Learning through VISWA Online Trainings

Reviews

vishal meda
vishal meda
2023-04-15
They give trainings properly and trainers are well versed with them where i recommend to all viswa trainings are good!!
Ntr fan
Ntr fan
2023-04-01
I just finished sap bods training in Hyderabad. Excellent course and curriculum 100% doubt clarification sessions. Thanks Chaitanya
Shiva Krishna
Shiva Krishna
2023-04-01
I recently completed informatica online training with Chaitanya. Course was built by excellent trainer. And process of learning was streamlined. Thanks
Mohammad ali syed
Mohammad ali syed
2023-03-27
It was great and smooth understandable training. You can learn lots.
Govinda Bhatia
Govinda Bhatia
2023-03-19
Not recommended as there will be no server access working to do practical after training. Also there will be no fix for the same. So it's wastage of money. If server access not at all working then no meaning to provide server access. Also it not working for single day properly. Need to followup daily but in response you told will fix that sir at home once he will back will fix. After he came back again it's not working and not able to fix for single day also Every time new excuse it's wastage of money.
M Leela mohan
M Leela mohan
2023-03-15
I took SQL Server and MSBI Online training with Murali Krishna. I must say the course content was highly qualitative and the trainer covered all concepts. Overall it was a good experience with VISWA Online Trainings.
HARIKRISHNA BANDLA
HARIKRISHNA BANDLA
2023-03-13
Attended live Virtual training for IoT Trainer was very good. He had excellent knowledge of IoT and was very good at explaining concepts in detail.…
Lakshmi Lakshmi
Lakshmi Lakshmi
2023-01-18
Best sap commerce cloud and Spartacus training institute in india. He provides a great mix of listening, speaking, and practical learning activities and a very safe, supportive learning environment. He maintains a friendly relationship with the students during class. He not only teaches but also monitors our practice status on daily basis.
Ch Chandranath
Ch Chandranath
2023-01-18
I have undergone Oracle Tuning training. I can proudly say that this is one of the best training institutes available in the market. The way Mr. Kumar teaches the concepts and makes them understandable is very commendable and unique. Even a novice can clearly understand the concepts clearly after attending his classes.

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