Certified Machine Learning Certification & Training

• 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

• 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

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

• What is Machine Learning?
• Introduction to Machine Learning
• Types of Machine Learning
• Basic Probability required for Machine Learning
• Linear Algebra required for Machine Learning

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

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

• PCA
• Factor Analysis
• LDA

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

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

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

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

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

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

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

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

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

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

• Validate your Machine Learning Skills
• Higher salary structure
• Broadened up career choices
• Practical skills to implement Machine Learning in your organization effectively
• Improved potential to become a part of the era of Artificial Intelligence

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