Artificial Intelligence Certification

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Artificial Intelligence Certification Certified By Microsoft.


Let’s Learn! Technology doesn’t innovate. People do. That is the reason we put individuals first, so we can engage them to arrive at their maximum capacity with innovation. The fast pace of innovation and business today requests a learning approach that fits the necessities of both the individual and the organization. We built a learning system to reflect that need. Adapting today requires a guided methodology through the intricate number of formal and casual learning alternatives. It requires a methodology that envelops the top learning techniques utilized today and adjusts them to help hierarchical results.

Our learning ecosystem is designed to support how learning is done today and evolves to meet advances in technology and individual learning needs. Integrating the world’s largest collection of proprietary and IT partner content, resources, and expertise with a global instructor pool of more than 300 real-world experts, Vepsun Technologies delivers custom learning to global organizations no matter where their workforce is located to drive quantifiable results..

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Executive Program in Artificial Intelligence Technology Certified by Microsoft.

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A Great Place For Education | Vepsun.in


01 Learning Path Introduction to Python

Data Science Fundamentals

 Concepts of Python Programming

 Introduction to Data Science

 Configuration of Development Environment

 Real World Use-Cases of Data Science

 Variable and Strings

 Walkthrough of Data Types

 Functions, Control Flow and Loops

 Data Science Project Lifecycle

 Tuple, Lists and Dictionaries  Standard Libraries

Introduction to NumPy  Basics of NumPy Arrays  Mathematical Operations in NumPy  NumPy Array Manipulation  NumPy Array Broadcasting

02 Learning Path Data Manipulation with Pandas  Data Structures in Pandas-Series and Data Frames  Data Cleaning in Pandas  Data Manipulation in Pandas

 Handling Missing Values in Datasets  Hands-on: Implement NumPy Arrays and Pandas Data Frames

Data Visualization in Python  Plotting Basic Charts in Python  Data Visualization with Matplotlib  Statistical Data Visualization with Seaborn

 Hands-on: Coding Sessions Using Matplotlib, Seaborn Package

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Exploratory Data Analysis  Introduction to Exploratory Data Analysis (EDA) Steps  Plots to Explore Relationship Between Two Variables

 Histograms, Box plots to Explore a Single Variable  Heat Maps, Pair plots to Explore Correlations


03 Learning Path Introduction to Machine Learning

Linear Regression

 What is Machine Learning?

 Introduction to Linear Regression

 Use Cases of Machine Learning

 Use Cases of Linear Regression

 Types of Machine Learning - Supervised to

 How to Fit a Linear Regression Model?

Unsupervised methods  Machine Learning Workflow

Logistic Regression

 Evaluating and Interpreting Results from Linear Regression Models  Predict Bike Sharing Demand

 Introduction to Logistic Regression  Logistic Regression Use Cases  Understand Use of odds & Logic Function to Perform Logistic Regression  Predicting Credit card Default Cases

04 Learning Path Decision Trees & Random Forest  Introduction to Decision Trees & Random Forest  Understanding Criterion (Entropy & Information Gain) used in Decision Trees  Using Ensemble Methods in Decision Trees  Applications of Random Forest

Dimensionality Reduction using PCA  Introduction to Curse of Dimensionality  What is Dimensionality Reduction?  Technique Used in PCA to Reduce Dimensions  Applications of Principle Component Analysis

(PCA)  Optimize Model Performance using PCA on SPECTF heartdata 5

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Model Evaluation Techniques  Introduction to Evaluation Metrics and Model Selection in Machine Learning  Importance of Confusion Matrix for Predictions  Measures of Model Evaluation - Sensitivity, Specificity, Precision, Recall & f-score  Use AUC-ROC Curve to Decide Best Model


05 Learning Path K-NearestNeighbours  Introduction to K-NN  Calculate Neighbours using Distance Measures  Find Optimal Value of K in K-NN Method  Advantage & Disadvantages of K-NN

K-Means Clustering  Introduction to K-Means Clustering  Decide Clusters by Adjusting Centroids  Understand Applications of Clustering in Machine Learning  Segment Hands in Pokerdata

Naive Bayes Classifier  Introduction to Naïve Bayes Classification  Refresher on Probability Theory  Applications of Naive Bayes Algorithm in Machine Learning  Classify Spam Emails Based on Probability

Support Vector Machines  Introduction to SVM  Figure Decision Boundaries Using Support Vectors  Identify Hyperplane in SVM  Applications of SVM in Machine Learning

06 Learning Path Time Series Forecasting  Components of Time Series Data  Interpreting Autocorrelation & Partial Autocorrelation Functions  Introduction to Time Series Analysis  Stationary Vs Non Stationary Data  Stationary data and Implement ARIMA model

Recommendation Systems  Introduction to Recommender Systems  Types of Recommender Systems -

Collaborative, Content Based & Hybrid  Types of Similarity Matrix (Cosine, Jaccard, Pearson Correlation)  Segment Hands in Poker DataBuild Recommender systems on Movie data using K-NN Basics

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Apriori Algorithm  Applications of Apriori algorithm  Understand Association rule  Developing product Recommendations using Association Rules  Analyse Online Retail Data using Association Rules


07 Learning Path Linear Discriminant Analysis    

Recap of Dimensionality Reduction Concepts Types of Dimensionality Reduction Dimensionality Reduction Using LDA Apply LDA to Determine Wine Quality

Ensemble Learning  Introduction to Ensemble Learning

 What are Bagging and Boosting techniques?  What is Bias Variance Trade Off?  Predict Wage (annual income) Classes from Adult Census Data

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Anomaly Detection  Introduction to Anomaly Detection  How Anomaly Detection Works?  Types of Anomaly Detection: Density Based, Clustering etc. NET Based Commands  Detect Anomalies on Electrocardiogram Data


Artificial Intelligence

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