Artificial Intelligence Certification Training Course
Curriculum
21 Sections
150 Lessons
60 Hours
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Introduction to Data Science
5
1.0
What is Data Science?
1.1
What is Machine Learning?
1.2
What is Deep Learning?
1.3
What is AI?
1.4
Data Analytics & its types
Introduction to Python
5
2.0
What is Python?
2.1
Why Python?
2.2
Installing Python
2.3
Python IDEs
2.4
Jupyter Notebook Overview
Python Basics
11
3.0
Python Basic Data types
3.1
Lists
3.2
Slicing
3.3
IF statements
3.4
Loops
3.5
Dictionaries
3.6
Tuples
3.7
Functions
3.8
Array
3.9
Selection by position & Labels
3.10
Hands-on
Python Packages
5
4.0
Pandas
4.1
Numpy
4.2
Sci-kit Learn
4.3
Mat-plot library
4.4
Hands-on
Importing Data
5
5.0
Reading CSV files
5.1
Saving in Python data
5.2
Loading Python data objects
5.3
Writing data to CSV file
5.4
Hands-on
Manipulating Data
7
6.0
Selecting rows/observations
6.1
Rounding Number
6.2
Selecting columns/fields
6.3
Merging data
6.4
Data aggregation
6.5
Data munging techniques
6.6
Hands-on
Statistics Basics
36
7.0
Central Tendency
7.1
Mean
7.2
Median
7.3
Mode
7.4
Skewness
7.5
Normal Distribution
7.6
Probability Basics
7.7
What does mean by probability?
7.8
Types of Probability
7.9
ODDS Ratio?
7.10
Standard Deviation
7.11
Data deviation & distribution
7.12
Variance
7.13
Bias variance Trade off
7.14
Underfitting
7.15
Overfitting
7.16
Distance metrics
7.17
Euclidean Distance
7.18
Manhattan Distance
7.19
Outlier analysis
7.20
What is an Outlier?
7.21
Inter Quartile Range
7.22
Box & whisker plot
7.23
Upper Whisker
7.24
Lower Whisker
7.25
Scatter plot
7.26
Cook’s Distance
7.27
Missing Value Treatment
7.28
What is a NA?
7.29
Central Imputation
7.30
KNN imputation
7.31
Dummification
7.32
Correlation
7.33
Pearson correlation
7.34
positive & Negative correlation
7.35
Hands-on
Error Metrics
11
8.0
Classification
8.1
Confusion Matrix
8.2
Precision
8.3
Recall
8.4
Specificity
8.5
F1 Score
8.6
Regression
8.7
MSE
8.8
RMSE
8.9
MAPE
8.10
Hands-on
Machine Learning
0
Supervised Learning
12
10.0
Linear Regression
10.1
Linear Equation
10.2
Slope
10.3
Intercept
10.4
R square value
10.5
Logistic regression
10.6
ODDS ratio
10.7
Probability of success
10.8
Probability of failure Bias Variance Tradeoff
10.9
ROC curve
10.10
Bias Variance Tradeoff
10.11
Hands-on
Unsupervised Learning
4
11.0
K-Means
11.1
K-Means ++
11.2
Hierarchical Clustering
11.3
Hands-on
SVM
5
12.0
Support Vectors
12.1
Hyperplanes
12.2
2-D Case
12.3
Linear Hyperplane
12.4
Hands-on
SVM Kernal
4
13.0
Linear
13.1
Radial
13.2
Polynomial
13.3
Hands-on
Other Machine Learning algorithms
6
14.0
K – Nearest Neighbour
14.1
Naive Bayes Classifier
14.2
Decision Tree – CART
14.3
Decision Tree – C50
14.4
Random Forest
14.5
Hands-on
ARTIFICIAL INTELLIGENCE
0
AI Introduction
6
16.0
Perceptron
16.1
Multi-Layer perceptron
16.2
Markov Decision Process
16.3
Logical Agent & First Order Logic
16.4
AL Applications
16.5
Hands-on
Deep Learning
0
Deep Learning Algorithms
4
18.0
CNN – Convolutional Neural Network
18.1
RNN – Recurrent Neural Network
18.2
ANN – Artificial Neural Network
18.3
Hands-on
Introduction to NLP
7
19.0
Text Pre-processing
19.1
Noise Removal
19.2
Lexicon Normalization
19.3
Lemmatization
19.4
Stemming
19.5
Object Standardization
19.6
Hands-on
Text to Features (Feature Engineering)
11
20.0
Syntactical Parsing
20.1
Dependency Grammar
20.2
Part of Speech Tagging
20.3
Entity Parsing
20.4
Named Entity Recognition
20.5
Topic Modelling
20.6
N-Grams
20.7
TF – IDF
20.8
Frequency / Density Features
20.9
Word Embedding’s
20.10
Hands-on
Tasks of NLP
6
21.0
Text Classification
21.1
Text Matching
21.2
Levenshtein Distance
21.3
Phonetic Matching
21.4
Flexible String Matching
21.5
Hands-on
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