Certified Machine Learning Certification & Training

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 Machin 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 AI-ML market is expected to expand up to $8.81 billion by 2020, organizations are looking forward to bring 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.

 

Key Features:

• Both online and classroom Certified Machine Learning Master Training
• Brief and to the point Certified Machine Learning Master course syllabus
• Soft copy and hard copy of accredited study material 
• Industry expert trainers

 

Course Curriculum:

Certified Machine Learning Master Course’s curriculum shares a deep understanding of:

• The basics of Machine Learning
• The best practices of machine learning
• Various machine learning modules
• Machine learning approaches
• Different Machine learning algorithms
• Future scopes and to trends of machine learning

Syllabus:

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

2. Introduction to various packages and related functions

• 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

3. Data Wrangling using Python

• NumPy – Arrays
• Data Operations (Selection , Append , Concat ,Joins)
• Univariate Analysis
• Multivariate Analysis
• Handling Missing Values
• Handling Outliers

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
•  

5. Supervised Learning - Regression

• Simple Linear Regression
• Multiple Linear Regression
• Assumptions of Linear Regression
• Polynomial Regression
• R2 and RMSE

6. Supervised Learning – Classification

• Logistic Regression
• Decision Trees
• Random Forests
• SVM
• Naïve Bayes
• Confusion Matrix

7. Dimensionality Reduction

• PCA
• Factor Analysis
• LDA

8. Unsupervised Learning - Clustering

• Types of Clustering
• K-means Clustering
• Agglomerative Clustering

9. Additional Performance Evaluation and Model Selection

• AUC / ROC
• Silhouette coefficient
• Cross Validation
• Bagging
• Boosting
• Bias v/s Variance

10. Recommendation Engines

• Need of recommendation engines
• Types of Recommendation Engines
• Content Based
• Collaborative Filtering

11. Association Rules Mining

• What are Association Rules?
• Association Rule Parameters
• Apriori Algorithm
• Market Basket Analysis

12. Time Series Analysis

• What is Time Series Analysis?
• Importance of TSA
• Understanding Time Series Data
• ARIMA analysis

13. Reinforcement Learning

• Understanding Reinforcement Learning
• Algorithms associated with RL
• Q-Learning Model
• Introduction to Artificial Intelligence

14. Artificial Neural Networks and Introduction to Deep Learning

• History of Neural Network
• Perceptron
• Forward Propagation
• Introduction to Deep Learning

 

Eligibility:

You are eligible to take up Certified Machine Learning Master course if you belong from any of the following fields:

• IT Professionals
•  Software Developers
•  Process Managers
•  Project Managers
•  Data Analysis Professionals
•  Web Developers
•  Application Developers

 

Pre-Requisites:

There are no pre-requisites for getting certified as a Machine Learning Master. Although, basic knowledge and working knowledge of programming and statistics will be required.

Benefits:

Certification Exam:

• Multiple-choice exam of 40 marks.
• You need to acquire 26+ marks to clear the exam.
• Exam will be moderated by our trainer

Faculty:

Machine Learning Master training is conducted by accredited trainers. Trainers for this course are highly experienced professionals with more than 10 years of industry experience. Currently, they are serving in multinational companies and have trained more than 5000 professionals.

Their core expertise includes:

• Cloud Consulting & Implementation
• GAP Analysis, ISO Audits
• Project Management
• Machine Learning
• Deep Learning
• Artificial Intelligence 
• Supervised Learning
• Unsupervised Learning

---