Artificial Intelligence Certification

Explore the fascinating and fast-moving field of artificial intelligence with online courses on Vepsun. AI give us human-like machines? Or is it just another industry buzzword? We look at the history of AI and describe its true potential

Beginner 0(0 Ratings) 0 Students enrolled
Created by Sanjeev Singh Last updated Sat, 12-Sep-2020 English
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Curriculum for this course
0 Lessons 00:00:00 Hours
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01 Learning Path
Introduction to Python
  • Concepts of Python Programming
  • Configuration of Development Environment
  • Variable and Strings
  • Functions, Control Flow and Loops
  • Tuple, Lists and Dictionaries
  • Standard Libraries
Data Science Fundamentals
  • Introduction to Data Science
  • Real World Use-Cases of Data Science
  • Walkthrough of Data Types
  • Data Science Project Lifecycle
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
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
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
03 Learning Path
Introduction to Machine Learning
  • What is Machine Learning?
  • Use Cases of Machine Learning
  • Types of Machine Learning - Supervised to Unsupervised methods
  • Machine Learning Workflow
Logistic Regression
  • Introduction to Logistic Regression
  • Logistic Regression Use Cases
  • Understand Use of odds & Logic Function to Perform Logistic Regression
  • Predicting Credit card Default Cases
Linear Regression
  • Introduction to Linear Regression
  • Use Cases of Linear Regression
  • How to Fit a Linear Regression Model?
  • Evaluating and Interpreting Results from Linear Regression Models
  • Predict Bike Sharing Demand
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
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
  • 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 Na├»ve 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
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
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
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