Artificial Intelligence (AI)

This Introduction to AI provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised, and reinforcement learning; be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.


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What you will learn:


By the end of this AI training, you will be able to accomplish the following:

  • Understand the meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
  • Design and build your own intelligent agents, applying them to create practical Artificial Intelligence projects, including games, machine learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, and agent decision-making functions
  • Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions
  • Learn how to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
  • Master the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engine, and time series modeling
  • Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
  • Master advanced topics in Artificial Intelligence, such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces

Course content


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
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
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
Introduction to Machine Learning
  • What is Machine Learning?
  • Use Cases of Machine Learning
  • Types of Machine Learning – Supervised to Unsupervised methods
  • Machine learning workflow
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
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
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
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
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 heart data
  • Introduction to KNN
  • Calculate neighbours using distance measures
  • Find optimal value of K in KNN method
  • Advantage & disadvantages of KNN
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
K-means Clustering
  • Introduction to K-means clustering
  • Decide clusters by adjusting centroids
  • Understand applications of clustering in Machine Learning
  • Segment hands in Poker data
Support Vector Machines
  • Introduction to SVM
  • Figure decision boundaries using support vectors
  • Identify hyper plane in SVM
  • Applications of SVM in Machine Learning
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
Apriori Algorithm
  • Applications of Apriori algorithm
  • Understand Association rule
  • Developing product recommendations using association rules
  • Analyse online retail data using association rules
Recommendation Systems
  • Introduction to Recommender systems
  • Types of Recommender systems – collaborative, content based & Hybrid
  • Types of similarity matrix (Cosine, Jaccard, Pearson correlation)
  • Build Recommender systems on Movie data using KNN basics
Linear Discriminant Analysis
  • Recap of dimensionality reduction concepts
  • Types of dimensionality reduction
  • Dimensionality reduction using LDA
  • Apply LDA to determine Wine Quality
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
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
  • Optimization techniques: Linear Programming using Excel solver
  • Optimization in ML
  • Neural networks
  • Workflow of a Neural network & analogy with biological neurons



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Course Prerequisites


Participants in this program should have:

  • An understanding of the fundamentals of Python programming
  • Basic knowledge of statistics

Who can attend

  • Developers aspiring to be an Artificial Intelligence Engineer or Machine Learning Engineer
  • Analytics Managers who are leading a team of analysts
  • Information Architects who want to gain expertise in Artificial Intelligence algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in Artificial Intelligence and machine learning
  • Experienced professionals who would like to harness Artificial Intelligence in their fields to get more insight

Number of Hours: 60hrs


Artificial Intelligence Certification For Working Professionals

Key features

  • One to One Training
  • Online Training
  • Fastrack & Normal Track
  • Resume Modification
  • Mock Interviews
  • Video Tutorials
  • Materials
  • Real Time Projects
  • Virtual Live Experience
  • Preparing for Certification


DASVM Technologies offers 300+ IT training courses with 10+ years of Experienced Expert level Trainers.

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  • Online Training
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  • Resume Modification
  • Mock Interviews
  • Video Tutorials
  • Materials
  • Real Time Projects
  • Materials
  • Preparing for Certification

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