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Machine Learning Certification Training
One of the top providers of online IT training worldwide is VISWA Online Trainings. To assist beginners and working professionals in achieving their career objectives and taking advantage of our best services, We provide a wide range of courses and online training.
About Course
Our Machine Learning Online Training is designed to help learners gain a deep understanding of how machines learn from data and make intelligent decisions. This course covers the core concepts of Machine Learning, Artificial Intelligence (AI), and Data Science, enabling you to build predictive models, automate decision-making, and analyze complex datasets effectively.
You’ll learn how to use Python and libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras to build, train, and evaluate ML models. The course includes both supervised and unsupervised learning, along with advanced topics such as Deep Learning, Natural Language Processing (NLP), and Model Deployment.
Through hands-on projects and real-world case studies, you’ll gain practical experience in applying machine learning to industries like finance, healthcare, e-commerce, and more.
By the end of this training, you’ll be able to confidently build and deploy machine learning models and prepare for roles such as Machine Learning Engineer, Data Scientist, or AI Developer. This course is ideal for students, software engineers, analysts, and data professionals looking to upgrade their skills in the rapidly growing field of AI and ML.
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
- Data Collection
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
- 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
Machine Learning Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
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- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
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- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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Machine Learning Online Training FAQ'S
What is Machine Learning, and how is it different from traditional programming?
Machine Learning (ML) is a subset of Artificial Intelligence that enables systems to learn patterns from data and make predictions or decisions without being explicitly programmed.
In traditional programming, logic is explicitly coded; in ML, algorithms learn the logic by training on data and improving automatically over time.
What are the different types of Machine Learning?
Supervised Learning: Trains models using labeled data (e.g., regression, classification).
Unsupervised Learning: Works with unlabeled data to find hidden patterns (e.g., clustering, association).
Reinforcement Learning: Learns through feedback by performing actions and receiving rewards or penalties.
What is Overfitting, and how can you prevent it?
Overfitting occurs when a model learns the training data too well, including noise and outliers, and performs poorly on new data.
Prevention methods:
- Use cross-validation
- Apply regularization (L1/L2)
- Use dropout (for neural networks)
- Train with more data or simplify the model
What is the difference between Regression and Classification?
Regression: Predicts continuous numeric outcomes (e.g., predicting house prices).
Classification: Predicts categorical outcomes (e.g., spam or not spam).
Both are types of supervised learning, but differ in output type and evaluation metrics.
What is the Confusion Matrix, and why is it important?
A Confusion Matrix is a table used to evaluate classification models by showing actual vs. predicted outcomes. It includes:
- True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN).
It helps calculate metrics like accuracy, precision, recall, and F1-score, providing a detailed view of model performance.
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