Machine Learning Online Training

RH Soft Tech provides the best Machine Learning online training to the graduate students who wants to explore their career in the field of Artificial intelligence.  In our course, students will get in-depth training on Machine learning topics like developing algorithms using unsupervised and supervised learning, how to work with real time data, how to develop algorithms using classification, regression and time series modeling.

Being rated as one of the leading Machine Learning online training institute in India, we provides and ideal platform for the students to acquire practical and hands-on skills of Machine learning with the most widely used techniques.

Course Introduction 

Introduction to AI and Machine Learning 

  • Learning Objectives 
  • The emergence of Artificial Intelligence 
  • Artificial Intelligence in Practice 
  • Sci-Fi Movies with the concept of AI 
  • Recommender Systems 
  • Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part A 
  • Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part B 
  • Definition and Features of Machine Learning 
  • Machine Learning Approaches 
  • Machine Learning Techniques 
  • Applications of Machine Learning – Part A 
  • Applications of Machine Learning – Part B 
  • Key Takeaways 

Data Preprocessing 

  • Learning Objectives 
  • Data Exploration: Loading Files 
  • Demo: Importing and Storing Data 
  • Practice: Automobile Data Exploration I 
  • Data Exploration Techniques: Part 1 
  • Data Exploration Techniques: Part 2 
  • Seaborn 
  • Demo: Correlation Analysis 
  • Practice: Automobile Data Exploration II 
  • Data Wrangling 
  • Missing Values in a Dataset 
  • Outlier Values in a Dataset 
  • Demo: Outlier and Missing Value Treatment 
  • Practice: Data Exploration III 
  • Data Manipulation 
  • Functionalities of Data Object in Python: Part A 
  • Functionalities of Data Object in Python: Part B 
  • Different Types of Joins 
  • Typecasting 
  • Demo: Labor Hours Comparison 
  • Practice: Data Manipulation 
  • Key Takeaways 
  • Lesson-end project: Storing Test Results 

Supervised Learning 

  • Learning Objectives 
  • Supervised Learning 
  • Supervised Learning- Real-Life Scenario 
  • Understanding the Algorithm 
  • Supervised Learning Flow 
  • Types of Supervised Learning – Part A 
  • Types of Supervised Learning – Part B 
  • Types of Classification Algorithms 
  • Types of Regression Algorithms – Part A 
  • Regression Use Case 
  • Accuracy Metrics 
  • Cost Function 
  • Evaluating Coefficients 
  • Demo: Linear Regression 
  • Practice: Boston Homes I 
  • Challenges in Prediction 
  • Types of Regression Algorithms – Part B 
  • Demo: Bigmart 
  • Practice: Boston Homes II 
  • Logistic Regression – Part A 
  • Logistic Regression – Part B 
  • Sigmoid Probability 
  • Accuracy Matrix 
  • Demo: Survival of Titanic Passengers 
  • Practice: Iris Species 
  • Key Takeaways 
  • Lesson-end Project: Health Insurance Cost 

Feature Engineering 

  • Learning Objectives 
  • Feature Selection 
  • Regression 
  • Factor Analysis 
  • Factor Analysis Process 
  • Principal Component Analysis (PCA) 
  • First Principal Component 
  • Eigenvalues and PCA 
  • Demo: Feature Reduction 
  • Practice: PCA Transformation 
  • Linear Discriminant Analysis 
  • Maximum Separable Line 
  • Find Maximum Separable Line 
  • Demo: Labeled Feature Reduction 
  • Practice: LDA Transformation 
  • Key Takeaways 
  • Lesson-end Project: Simplifying Cancer Treatment 

Supervised Learning: Classification 

  • Learning Objectives 
  • Overview of Classification 
  • Classification: A Supervised Learning Algorithm 
  • Use Cases 
  • Classification Algorithms 
  • Decision Tree Classifier 
  • Decision Tree: Examples 
  • Decision Tree Formation 
  • Choosing the Classifier 
  • Overfitting of Decision Trees 
  • Random Forest Classifier- Bagging and Bootstrapping 
  • Decision Tree and Random Forest Classifier 
  • Performance Measures: Confusion Matrix 
  • Performance Measures: Cost Matrix 
  • Demo: Horse Survival 
  • Practice: Loan Risk Analysis 
  • Naive Bayes Classifier 
  • Steps to Calculate Posterior Probability: Part A 
  • Steps to Calculate Posterior Probability: Part B 
  • Support Vector Machines: Linear Separability 
  • Support Vector Machines: Classification Margin 
  • Linear SVM: Mathematical Representation 
  • Non-linear SVMs 
  • The Kernel Trick 
  • Demo: Voice Classification 
  • Practice: College Classification 
  • Key Takeaways 
  • Lesson-end Project: Classify Kinematic Data 

Unsupervised Learning 

  • Learning Objectives 
  • Overview 
  • Example and Applications of Unsupervised Learning 
  • Clustering 
  • Hierarchical Clustering 
  • Hierarchical Clustering: Example 
  • Demo: Clustering Animals 
  • Practice: Customer Segmentation 
  • K-means Clustering 
  • Optimal Number of Clusters 
  • Demo: Cluster-Based Incentivization 
  • Practice: Image Segmentation 
  • Key Takeaways 
  • Lesson-end Project: Clustering Image Data 

Time Series Modeling 

  • Learning Objectives 
  • Overview of Time Series Modeling 
  • Time Series Pattern Types Part A 
  • Time Series Pattern Types Part B 
  • White Noise 
  • Stationarity 
  • Removal of Non-Stationarity 
  • Demo: Air Passengers I 
  • Practice: Beer Production I 
  • Time Series Models Part A 
  • Time Series Models Part B 
  • Time Series Models Part C 
  • Steps in Time Series Forecasting 
  • Demo: Air Passengers II 
  • Practice: Beer Production II 
  • Key Takeaways 
  • Lesson-end Project: IMF Commodity Price Forecast 

Ensemble Learning 

  • Learning Objectives 
  • Overview 
  • Ensemble Learning Methods Part A 
  • Ensemble Learning Methods Part B 
  • Working of AdaBoost 
  • AdaBoost Algorithm and Flowchart 
  • Gradient Boosting 
  • XGBoost 
  • XGBoost Parameters Part A 
  • XGBoost Parameters Part B 
  • Demo: Pima Indians Diabetes 
  • Practice: Linearly Separable Species 
  • Model Selection 
  • Common Splitting Strategies 
  • Demo: Cross-Validation 
  • Practice: Model Selection 
  • Key Takeaways 
  • Lesson-end Project: Tuning Classifier Model with XGBoost 

Recommender Systems 

  • Learning Objectives 
  • Introduction 
  • Purposes of Recommender Systems 
  • Paradigms of Recommender Systems 
  • Collaborative Filtering Part A 
  • Collaborative Filtering Part B 
  • Association Rule Mining 
  • Association Rule Mining: Market Basket Analysis 
  • Association Rule Generation: Apriori Algorithm 
  • Apriori Algorithm Example: Part A 
  • Apriori Algorithm Example: Part B 
  • Apriori Algorithm: Rule Selection 
  • Demo: User-Movie Recommendation Model 
  • Practice: Movie-Movie recommendation 
  • Key Takeaways 
  • Lesson-end Project: Book Rental Recommendation 

Text Mining 

  • Learning Objectives 
  • Overview of Text Mining 
  • Significance of Text Mining 
  • Applications of Text Mining 
  • Natural Language Toolkit Library 
  • Text Extraction and Preprocessing: Tokenization 
  • Text Extraction and Preprocessing: N-grams 
  • Text Extraction and Preprocessing: Stop Word Removal 
  • Text Extraction and Preprocessing: Stemming 
  • Text Extraction and Preprocessing: Lemmatization 
  • Text Extraction and Preprocessing: POS Tagging 
  • Text Extraction and Preprocessing: Named Entity Recognition 
  • NLP Process Workflow 
  • Demo: Processing Brown Corpus 
  • Practice: Wiki Corpus 
  • Structuring Sentences: Syntax 
  • Rendering Syntax Trees 
  • Structuring Sentences: Chunking and Chunk Parsing 
  • NP and VP Chunk and Parser 
  • Structuring Sentences: Chinking 
  • Context-Free Grammar (CFG) 
  • Demo: Twitter Sentiments 
  • Practice: Airline Sentiment 
  • Key Takeaways 
  • Lesson-end Project: FIFA World Cup 
Course Duration: 35 – 40 hrs
Timing Week Days: 1 hrs per day (OR) 3 Times a week or Weekends: 3 hrs per day
Method: Instructor led, with exercises.
Breaks: As Required, but limited to 2 of 10 min. In weekend batch
Email ID Info@rhsofttech.com
Study Material  Machine Learning Online Training
Phone +91 9356913849
Fees Structure 50% After introduction lectures balance  After 50% Lecture
Server access Throughout the course 24/7
Timings Flexible