AI Training in Bangalore | Artificial Intelligence Course with Placement

Curriculum

39 Sections
148 Lessons
60 Hours

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Introduction to Data Science
5
- 1.1
  What is Data Science? (Copy)
- 1.2
  What is Machine Learning? (Copy)
- 1.3
  What is Deep Learning? (Copy)
- 1.4
  What is AI? (Copy)
- 1.5
  Data Analytics & its types (Copy)
Introduction to Python
5
- 2.1
  What is Python? (Copy)
- 2.2
  Why Python? (Copy)
- 2.3
  Installing Python (Copy)
- 2.4
  Python IDEs (Copy)
- 2.5
  Jupyter Notebook Overview (Copy)
Python Basics
11
- 3.1
  Python Basic Data types (Copy)
- 3.2
  Lists (Copy)
- 3.3
  Slicing (Copy)
- 3.4
  IF statements (Copy)
- 3.5
  Loops (Copy)
- 3.6
  Dictionaries (Copy)
- 3.7
  Tuples (Copy)
- 3.8
  Functions (Copy)
- 3.9
  Array (Copy)
- 3.10
  Selection by position & Labels (Copy)
- 3.11
  Hands-on (Copy)
Python Packages
5
- 4.1
  Pandas (Copy)
- 4.2
  Numpy (Copy)
- 4.3
  Sci-kit Learn (Copy)
- 4.4
  Mat-plot library (Copy)
- 4.5
  Hands-on (Copy)
Importing Data
5
- 5.1
  Reading CSV files (Copy)
- 5.2
  Saving in Python data (Copy)
- 5.3
  Loading Python data objects (Copy)
- 5.4
  Writing data to CSV file (Copy)
- 5.5
  Hands-on (Copy)
Manipulating Data
7
- 6.1
  Selecting rows/observations (Copy)
- 6.2
  Rounding Number (Copy)
- 6.3
  Selecting columns/fields (Copy)
- 6.4
  Merging data (Copy)
- 6.5
  Data aggregation (Copy)
- 6.6
  Data munging techniques (Copy)
- 6.7
  Hands-on (Copy)
Statistics Basics
0
Central Tendency
5
- 8.1
  Mean (Copy)
- 8.2
  Median (Copy)
- 8.3
  Mode (Copy)
- 8.4
  Skewness (Copy)
- 8.5
  Normal Distribution (Copy)
Probability Basics
3
- 9.1
  What does mean by probability? (Copy)
- 9.2
  Types of Probability (Copy)
- 9.3
  ODDS Ratio? (Copy)
Standard Deviation
2
- 10.1
  Data deviation & distribution (Copy)
- 10.2
  Variance (Copy)
Bias variance Trade off
2
- 11.1
  Underfitting (Copy)
- 11.2
  Overfitting (Copy)
Distance metrics
2
- 12.1
  Euclidean Distance (Copy)
- 12.2
  Manhattan Distance (Copy)
Outlier analysis
7
- 13.1
  What is an Outlier? (Copy)
- 13.2
  Inter Quartile Range (Copy)
- 13.3
  Box & whisker plot (Copy)
- 13.4
  Upper Whisker (Copy)
- 13.5
  Lower Whisker (Copy)
- 13.6
  Scatter plot (Copy)
- 13.7
  Cook’s Distance (Copy)
Missing Value Treatment
4
- 14.1
  What is a NA? (Copy)
- 14.2
  Central Imputation (Copy)
- 14.3
  KNN imputation (Copy)
- 14.4
  Dummification (Copy)
Correlation
2
- 15.1
  Pearson correlation (Copy)
- 15.2
  positive & Negative correlation (Copy)
Hands-on
0
Error Metrics
0
Classification
5
- 18.1
  Confusion Matrix (Copy)
- 18.2
  Precision (Copy)
- 18.3
  Recall (Copy)
- 18.4
  Specificity (Copy)
- 18.5
  F1 Score (Copy)
Regression
3
- 19.1
  MSE (Copy)
- 19.2
  RMSE (Copy)
- 19.3
  MAPE (Copy)
Hands-on
0
Machine Learning
0
Supervised Learning
0
Linear Regression
4
- 23.1
  Linear Equation (Copy)
- 23.2
  Slope (Copy)
- 23.3
  Intercept (Copy)
- 23.4
  R square value (Copy)
Logistic regression
5
- 24.1
  ODDS ratio (Copy)
- 24.2
  Probability of success (Copy)
- 24.3
  Probability of failure Bias Variance Tradeoff (Copy)
- 24.4
  ROC curve (Copy)
- 24.5
  Bias Variance Tradeoff (Copy)
Hands-on
0
Unsupervised Learning
4
- 26.1
  K-Means (Copy)
- 26.2
  K-Means ++ (Copy)
- 26.3
  Hierarchical Clustering (Copy)
- 26.4
  Hands-on (Copy)
SVM
5
- 27.1
  Support Vectors (Copy)
- 27.2
  Hyperplanes (Copy)
- 27.3
  2-D Case (Copy)
- 27.4
  Linear Hyperplane (Copy)
- 27.5
  Hands-on (Copy)
SVM Kernel
4
- 28.1
  Linear (Copy)
- 28.2
  Radial (Copy)
- 28.3
  Polynomial (Copy)
- 28.4
  Hands-on (Copy)
Other Machine Learning algorithms
6
- 29.1
  K – Nearest Neighbour (Copy)
- 29.2
  Naive Bayes Classifier (Copy)
- 29.3
  Decision Tree – CART (Copy)
- 29.4
  Decision Tree – C50 (Copy)
- 29.5
  Random Forest (Copy)
- 29.6
  Hands-on (Copy)
ARTIFICIAL INTELLIGENCE
0
AI Introduction
6
- 31.1
  Perceptron (Copy)
- 31.2
  Multi-Layer perceptron (Copy)
- 31.3
  Markov Decision Process (Copy)
- 31.4
  Logical Agent & First Order Logic (Copy)
- 31.5
  AL Applications (Copy)
- 31.6
  Hands-on (Copy)
Deep Learning
0
Deep Learning Algorithms
4
- 33.1
  CNN – Convolutional Neural Network (Copy)
- 33.2
  RNN – Recurrent Neural Network (Copy)
- 33.3
  ANN – Artificial Neural Network (Copy)
- 33.4
  Hands-on (Copy)
Introduction to NLP
7
- 34.1
  Text Pre-processing (Copy)
- 34.2
  Noise Removal (Copy)
- 34.3
  Lexicon Normalization (Copy)
- 34.4
  Lemmatization (Copy)
- 34.5
  Stemming (Copy)
- 34.6
  Object Standardization (Copy)
- 34.7
  Hands-on (Copy)
Text to Features (Feature Engineering)
11
- 35.1
  Syntactical Parsing (Copy)
- 35.2
  Dependency Grammar (Copy)
- 35.3
  Part of Speech Tagging (Copy)
- 35.4
  Entity Parsing (Copy)
- 35.5
  Named Entity Recognition (Copy)
- 35.6
  Topic Modelling (Copy)
- 35.7
  N-Grams (Copy)
- 35.8
  TF – IDF (Copy)
- 35.9
  Frequency / Density Features (Copy)
- 35.10
  Word Embedding’s (Copy)
- 35.11
  Hands-on (Copy)
Tasks of NLP
6
- 36.1
  Text Classification (Copy)
- 36.2
  Text Matching (Copy)
- 36.3
  Levenshtein Distance (Copy)
- 36.4
  Phonetic Matching (Copy)
- 36.5
  Flexible String Matching (Copy)
- 36.6
  Hands-on (Copy)
Computer Vision
5
- 37.1
  Image Processing Basics (Copy)
- 37.2
  OpenCV (Copy)
- 37.3
  Face Detection & Recognition (Copy)
- 37.4
  Object Detection (Copy)
- 37.5
  AI Vision Projects (Copy)
AI Project & Deployment
4
- 38.1
  End-to-End AI Project (Copy)
- 38.2
  Real-time Use Case Implementation (Copy)
- 38.3
  Model Deployment (Flask / FastAPI) (Copy)
- 38.4
  Cloud Basics (AWS / Azure – Intro) (Copy)
Capstone Projects (Real-Time)
4
- 39.1
  AI Chatbot (Copy)
- 39.2
  Recommendation System (Copy)
- 39.3
  Face Recognition System (Copy)
- 39.4
  Predictive Analytics Project (Copy)

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AI Training in Bangalore – Artificial Intelligence Course with Placement Support

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