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Artificial Intelligence Training and Placement Course

  • Classroom Course
  • Expert Trainers
  • 24×7 Doubt support
  • Assignments and Quiz
  • Live projects
  • Course Duration : 1.5 month (45 Days)

About

Artificial Intelligence is not centered on programming, but most of the homework requires an understanding of programming and working out small programs. Artificial Intelligence (AI) influences almost every aspect of our lives. This exciting technology is used across a wide range of industries, and the need for AI specialists is only likely to grow.

By the end of this course you will..

  • Gain familiarity with fundamental approaches to problem solving and inference and areas of application.
  • exhibit familiarity with primary and advanced algorithm used in AI to exploiting regularity in data and areas of application.
  • Deep Understanding of computational theories of aspects of human intelligence and the role of those theories in applications.

Batch Timings

  • Weekdays : Mon, Tue, Wed, Thu, Fri (2 hrs)
  • Weekends : Sat, Sun (4-5hrs)

Modes Of Teaching


Course Content

Artificial Intelligence Training
1. Intro to Data Science
1.1 What is Data Science?
1.2 What is Machine Learning?
1.3 What is Deep Learning?
1.4 What is Artificial Intelligence?
1.5 What is Data Analytics?
2. Intro to Python
2.1. Variables
2.2. Data Types
2.3. Keywords
2.4. Operators
2.5. Comments
2.6. IF Else
2.7. Loops
2.8. For Loop
2.9. While Loop
2.10. Break
2.11. Continue
2.12. Pass
2.13. Strings
2.14. Lists
2.15. Tuples
2.16. Sets
2.17. Dictionary
2.18. Dictionary Function
2.19. Built In Function
2.20. Lambda Function
2.21. Regex
2.22. Arrays, Input and output
2.23. Assignment and Quiz
3. Python Packages
3.1 NumPy
3.2 Scipy
3.3 Pandas
3.4 Pytorch
3.5 Seaborn
3.6 Scikit-Learn
3.7 Matplot lib
4. Importing Data
4.1. Reading CSV Files
4.2. Saving in python data
4.3. Loading python data objects
4.4. Writing Data to CSV Files
5. Manipulating Data
5.1. Rows and Observations
5.2. Rounding Number
5.3. Selecting Columns and Fields
5.4. Merging Data
5.5. Data Aggregations
5.6. Data Munging
6. Statistics
6.1. Central Tendency
6.1.1. Mean
6.1.2. Median
6.1.3. Mode
6.2. Probability basics
6.2.1 What is Probability?
6.2.1. Types of Probability?
6.2.2. ODDS Ratio
6.3. Standard Deviation
6.3.1. Data Deviation and data Distribution
6.3.2. Variance
6.4. Bias Variance
6.4.1. Underfitting
6.4.2. Overfitting
6.5. Distance Metrices
6.5.1. Euclidean Distance
6.5.2 Manhattan Distance
6.6. Outlier Analysis
6.6.1. Inter Quartile Range
6.6.2. Box & Wishker Plot
6.6.3. Upper Wishker
6.6.4. Lower Wishker
6.6.5. Scatter Plot
6.7. Missing Value Treatment
6.7.1. What is NA?
6.7.2. Central Imputation
6.7.3. KNN Imputation
6.7.4. Dummification
6.8. Correlation
6.8.1. Pearson Correlation
6.8.2. Positive and Negative Correlation
7. Error Metrics
7.1. Classification
7.1.1 Confusion Matrix
7.1.2 Precision
7.1.3 Recall
7.1.4 Specificity
7.1.5 F1 Score
7.2. Regression
7.2.1 MSE
7.2.2 RMSE
7.2.3 MAPE
8. Machine Learning and Supervised Learning
8.1 Linear Regression
8.1.1 Linear Equation
8.1.2 Slope
8.1.3 Intercept
8.1.4 R Square value
8.2 Logistic Regression
8.2.1 ODDS Ratio
8.2.2 Probability of success
8.2.3 Probability of Failure of bias variance Tradeoff
8.2.4 ROC curve
8.2.5 Bias Variance Tradeoff
8.3 Unsupervised Learning
8.3.1 K means Clustering
8.3.2 K means ++
8.3.3 Hierarchical Clustering
8.4 Support Vector machine
8.4.1 Support Vector
8.4.2 Hyper planes
8.4.3 2-D case
8.4.4 Linear Hyperplane
8.5 SVM Kernal
8.5.1 Linear
8.5.2 Radial
8.5.3 Polynomial
8.6 Other Machine learning Algorithm
8.6.1 K Nearest Neighbor
8.6.2 Naive Bayes Classifier
8.6.3 Decision Tree C50
8.6.4 Decision Tree CART
8.6.5 Random Forest
9. Artificial Intelligence
9.1 AI Introduction
9.2 Perceptron
9.3 Multi Layer Perceptron
9.4 Markov Decision Process
9.5 Logical Agent & First Order Logic
9.6 AI Applications
10.1 Deep Learning Algorithms
10.1.1 CNN – Convolutional Neural Network
10.1.2 RNN – Recurrent Neural Network
10.1.3 ANN – Artificial Neural Network
11.1 Introduction to Natural Language Processing
11.1.1 Text pre-processing
11.1.2 Noise Removal
11.1.3 Lexicon Normalization
11.1.4 Lemmatization
11.1.5 Stemming
11.1.6 Object Standardization
11.2 Text to features
11.2.1 Syntactical Parsing
11.2.2 Dependency Grammar
11.2.3 Part of speech Tagging
11.2.4 Entity Parsing
11.2.5 Named Entity Recognition
11.2.6 Topic Modelling
11.2.7 N-Grams
11.2.8 TF – IDF
11.2.9 Frequency / Density Features
11.2.10 Word Embedding’s
11.3 Tasks of NLP
11.3.1 Text Classification
11.3.2 Text Matching
11.3.3 Levenshtein Distance
11.3.4 Phonetic Matching
11.3.5 Flexible string matching

Contact Us

Training Center

Chennai Branch : Saidapet Center Address

No. 31A, E Jones Rd, Periyapet, Saidapet Chennai, Tamil Nadu

Pin Code – 600015 (IN)

Call Us:

(+91) 4442064048

(+91) 9385252523

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