Certified Machine Learning Certification & Training
Trusted by 1000s of global organizations, NovelVista is the leading Accredited Training Organization (ATO) to conduct Machine Learning Master Training & Certification Course.
- Industry Expert Trainers
- Online learning session
- Accredited Trainer
- Exam fee included
Certified Machine Learning Master Course Overview
The Certified Machine Learning Master course is aimed to deliver knowledge about Machine Learning and its practices within an organization. Machine Learning is a subset of Artificial Intelligence that works on a model of training data to make predictions and decisions while not being programmed to do so and thus, improves automatically through experience. Our Certified Machine Learning Master course helps you out with all the details of the recent tools and technologies that can be used in Machine Learning, end to end Machine Learning Algorithms, various models and approaches of Machine Learning along with the basics. While the AI-ML market is expected to expand up to $8.81 billion by 2020, organizations are looking forward to bringing Machine Learning experts on board more than the other professionals. Certified Machine Learning Master Course shapes you to bring in the skillsets of Machine Learning that the industry requires right now and prepares you to face any kind of business challenges.
What You Will Get?
Learning Outcome
After the completion of the course, the participants would be able to:
Course Curriculum
Module 1. Introduction to Python Programming+
- Overview of Python
- History of Python
- Python Basics – variables, identifiers, indentation
- Data Structures in Python (list, string, sets, tuples,dictionary)
- Statements in Python (conditional, iterative, jump)
- OOPS concepts
- Exception Handling
- Regular Expression
Module 2. Introduction to various packages and related function+
- Numpy, Pandas and Matplotlib
- Pandas Module
- Series
- Data Frames
- Numpy Module
- Numpy arrays
- Numpy operations
- Matplotlib module
- Plotting information
- Bar Charts and Histogram
- Box and Whisker Plots
- Heatmap
- Scatter Plots
Module 3. Data Wrangling using Python+
- NumPy – Arrays
- Data Operations (Selection , Append , Concat ,Joins)
- Univariate Analysis
- Multivariate Analysis
- Handling Missing Values
- Handling Outliers
Module 4. Introduction to Machine Learning with Python+
- What is Machine Learning?
- Introduction to Machine Learning
- Types of Machine Learning
- Basic Probability required for Machine Learning
- Linear Algebra required for Machine Learning
Module 5. Supervised Learning - Regression+
- Simple Linear Regression
- Multiple Linear Regression
- Assumptions of Linear Regression
- Polynomial Regression
- R2 and RMSE
Module 6. Supervised Learning – Classification+
- Logistic Regression
- Decision Trees
- Random Forests
- SVM
- Naïve Bayes
- Confusion Matrix
Module 7. Dimensionality Reduction+
- PCA
- Factor Analysis
- LDA
Module 8. Unsupervised Learning - Clustering+
- Types of Clustering
- K-means Clustering
- Agglomerative Clustering
Module 9. Additional Performance Evaluation and Model Selection+
- AUC / ROC
- Silhouette coefficient
- Cross Validation
- Bagging
- Boosting
- Bias v/s Variance
Module 10. Recommendation Engines+
- Need of recommendation engines
- Types of Recommendation Engines
- Content Based
- Collaborative Filtering
Module 11. Association Rules Mining+
- What are Association Rules?
- Association Rule Parameters
- Apriori Algorithm
- Market Basket Analysis
Module 12. Time Series Analysis+
- What is Time Series Analysis?
- Importance of TSA
- Understanding Time Series Data
- ARIMA analysis
Module 13. Reinforcement Learning+
- Understanding Reinforcement Learning
- Algorithms associated with RL
- Q-Learning Model
- Introduction to Artificial Intelligence
Module 14. Artificial Neural Networks and Introduction to Deep Learning+
- History of Neural Network
- Perceptron
- Forward Propagation
- Introduction to Deep Learning