- 36 hours of Instructor-led online live sessions
- 12 sessions of 3 hours each (Weekend)
- 18 sessions of 2 hours each (Weekday)
- Complimentary self-paced course of ?Python Statistics for Data Science Course?
- Case Studies on Real-life Scenarios
- Lifetime Access to Learning Management System
- Practice Assignments
- 24X7 Expert Support
- Online Forum for Discussions
- Cloud lab for real-life hands-on experience
Available Courses Delivery
This course is available in the following formats:
This Course is delineated to master the concepts of Machine Learning using high-level Python programming language. It develops proficiency in all types of Machine Learning algorithms, namely, Supervised Learning, Unsupervised Learning, and Reinforcement Learning. It imparts practical knowledge to develop Machine Learning algorithms using various concepts, such as, Time Series, Linear Regression, Logistic Regression, Decision Tree, etc
- Acquaint learners with Machine Learning
- Educate learners about the various roles played by Machine Learning Engineers and Data Scientists.
- Train learners in Data Analysis, using python
- Teach various tools and techniques of predictive modeling
- Equip learners with in-depth knowledge about Machine Learning algorithms
- Introduce learners to Time Series and related concepts
- Opportunity to work in multiple industries, such as, automotive, e-commerce, social media, etc
- Be considered an expert of data pre-processing, model evaluation, and dimensional reduction
- Scope of developing Machine Learning Applications
- Options to carry out hands-on data analysis using Python
- Higher paycheck
- In-depth knowledge of Python programming
- Basic understanding of statistics and mathematics
- Hands-on experience in the development projects using Python programming will be an added advantage
Who should take up?
- Developers with plans of transition in Data Science
- Analytics Professionals
- Information Architects
- Business Analysts
- Python Professionals
- Fresh Graduates who want to enter Data Science
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Python
- Data Extraction, Wrangling, & Visualization
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
- Python Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
- Introduction to Dimensionality
- Why Dimensionality Reduction
- Factor Analysis
- Scaling dimensional model
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Hyperparameter optimization
- Grid Search vs Random Search
- Implementation of Support Vector Machine for Classification
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How Do Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering
- What is Reinforcement Learning
- Why Reinforcement Learning
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q ? Learning
- ? values
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- ACF & PACF
- What is Model Selection?
- Need of Model Selection
- Cross ? Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
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