Machine Learning Training

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4627 Reviews 4.9

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

✔ 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

✔ Supervised Learning

✔ Classification
✔ Regression

✔ Un-Supervised Learning

✔ Association Rules
✔ Clustering

✔ Time Series Analysis

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

✔ 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

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

✔ 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



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5th NOV 2022



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Machine Learning Training FAQ'S

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.

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.

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.

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


Excellent institute for Online WebMethods Course and I recommend VISWA Technologies thank you For arranging such a great faculty
Today is the last class for online RPA course i am thankful to Chaitanya sir for such a great training and I thank to admin team
Bodicherla Jagadeeswar
Bodicherla Jagadeeswar
Recently completed online Microsoft Intune training course By "Rajendra Prasad" and I recommend VISWA Online Trainings for this course and also thank you admin team
Penchalakumar Ganji
Penchalakumar Ganji
Best institute for Linux, AWS online course by Musab sir. I thank Chaitanya sir for arranging our classes
Konduru Akhil
Konduru Akhil
Hi, Overall training classes are very good.All the instructions are kind and patient.I look forward to take more classes from Viswa online trainings.
Eswar Tech
Eswar Tech
I enroll for aws solution architecture course. It was great learning with viswa technologies. Thanks team.
Manikanta Goud
Manikanta Goud
I am really satisfied. Best training institute. Thanks Chaitu
S Rambabu 6228
S Rambabu 6228
Best online training institute. I have more experience with java srinath trainer.
gaddam Manojkumar
gaddam Manojkumar
They have a professional approach by their trainer and support team. Personally, I liked the instructor's way of teaching and also helping us if we have any doubts after the class.
Ramesh Metta
Ramesh Metta
They have teachers with strong expertise. The AWS course i have taken was good and had full support from team.

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