Top Hiring Company
Industry Trends
Course curriculum / Syllabus
Course Introduction
- Course Introduction
- Accessing Practice Lab
AI and Machine Learning Overview
- Learning Objectives
- Emergence of Artificial Intelligence
- Artificial Intelligence in Practice
- Sci-Fi Movies using AI concept
- Recommender Systems
- Machine Learning, Artificial Intelligence, and Data Science
- Definition and Features of Machine Learning
- Machine Learning Approaches
- Machine Learning Techniques
- Applications of Machine Learning
Data Preprocessing
- Learning Objectives
- Data Exploration Loading Files
- Data Exploration Techniques
- Seaborn
- Data Wrangling
- Missing Values in a Dataset
- Outlier Values in a Dataset
- Data Manipulation
- Functionalities of Data Object in Python
- Different Types of Joins
- Typecasting
Supervised Learning
- Learning Objectives
- Supervised Learning
- Supervised Learning- Real-Life Scenario
- Understanding the Algorithm
- Supervised Learning Flow
- Types of Supervised Learning
- Types of Classification Algorithms
- Types of Regression Algorithms
- Regression Use Case
- Accuracy Metrics
- Cost Function
- Evaluating Coefficients
- Challenges in Prediction
- Logistic Regression
- Sigmoid Probability
- Accuracy Matrix
Feature Engineering
- Learning Objectives
- Feature Selection
- Regression
- Factor Analysis
- Factor Analysis Process
- Principal Component Analysis (PCA)
- First Principal Component
- Eigenvalues and PCA
- Linear Discriminant Analysis
- Maximum Separable Line
- Find Maximum Separable Line
Supervised Learning Classification
- Learning Objectives
- Overview of Classification
- Classification: A Supervised Learning Algorithm
- Use Cases of Classification
- 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
- Naive Bayes Classifier
- Steps to Calculate Posterior Probability
- Support Vector Machines : Linear Separability
- Support Vector Machines : Classification Margin
- Linear SVM : Mathematical Representation
- Non-linear SVMs
- The Kernel Trick
Unsupervised Learning
- Learning Objectives
- Overview
- Example and Applications of Unsupervised Learning
- Clustering
- Hierarchical Clustering
- Hierarchical Clustering Example
- K-means Clustering
- Optimal Number of Clusters
Time Series Modeling
- Learning Objectives
- Overview of Time Series Modeling
- Time Series Pattern Types
- White Noise
- Stationarity
- Removal of Non-Stationarity
- Time Series Models
- Steps in Time Series Forecasting
Ensemble Learning
- Ensemble Learning
- Overview
- Ensemble Learning Methods
- Working of AdaBoost
- AdaBoost Algorithm and Flowchart
- Gradient Boosting
- XGBoost
- XGBoost Parameters
- Model Selection
- Common Splitting Strategies
Recommender Systems
- Learning Objectives
- Introduction
- Purposes of Recommender Systems
- Paradigms of Recommender Systems
- Collaborative Filtering
- Association Rule Mining
- Association Rule Mining: Market Basket Analysis
- Association Rule Generation: Apriori Algorithm
- Apriori Algorithm Example
- Apriori Algorithm: Rule Selection
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
- 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)
machine learning certification FAQ’s:
1.What is Machine Learning?
Machine learning is a technology which will help the professionals in implementing artificial intelligence. The technology will help the machines to communicate with others and there will be no human intervention. The course will help you in taking decisions regarding the growth of an organization.
2.Do the candidates require coding knowledge?
Yes! Candidates should have basic knowledge of coding done in R programming, Python, Java, etc.
3.Does the course can be done by beginners?
Yes! Beginners can do the course provided that they have some basic knowledge of machine learning.
4.How mock test sessions are arranged?
You will be given a test similar to the real exam. The test will include the types of questions which you will have to face in the real exam.
5.Do we also get the facility of mock interview?
Yes! We will provide the facility of mock interview.
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Mock projects
Real world project samples for practical sessions
