Artificial Intelligence Training

Artificial Intelligence Course:
Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively in many fields such as image recognition, robotics, search engines, and self-driving cars. In this Artificial Intelligence training at Bangalore, we will explore various real-world scenarios. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This artificial intelligence course is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This artificial intelligence course is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques.

Artificial Intelligence Course Curriculum

1. Black Box Introduction to Machine Learning

What is not Machine Learning

What is Machine Learning

Types of ML - Supervised, Unsupervised

Supervised - Classification, Regression

Unsupervised - Clustering, Association

Machine Learning Pipeline

2. Essential NumPy

Introduction to NumPy

Creation

Access

Stacking and Splitting

Methods

Broadcasting

3. Pandas for Machine Learning

Introduction to Pandas

Understanding Series & DataFrames

Loading CSV,JSON

Connecting databases

Descriptive Statistics

Accessing subsets of data - Rows, Columns, Filters

Handling Missing Data

Dropping rows & columns

Handling Duplicates

Function Application - map, apply, groupby, rolling, str

Merge, Join & Concatenate

Pivot-tables

Normalizing JSON

Application - EDA on Employee data, sales data

4. Essential Maths & Statistics

Essential Linear Algebra

Probability Concepts

Calculus

Understanding distributions

Mean, Median, Mode, Quantile

Other statistics Concepts

5. Understanding Visualization

Introduction to matplotlib, plotly, bokeh, tablue

Basic Plotting

Title, Labels, Legends, Grid, colormap, xticks, yticks

Color, linewidth

Sub Plotting

Scatterplot

Histogram

Bar Graphs

Plotting distributions

Plotting 3D data

6. Linear Models for Classification & Regression

Simple Linear Regression using Ordinary Least Squares

Gradient Descent Algorithm

Regularized Regression Methods - Ridge, Lasso, ElasticNet

Logistic Regression for Classification

OnLine Learning Methods - Stochastic Gradient Descent & Passive Aggrasive

Robust Regression - Dealing with outliers & Model errors

Polynomial Regression

Bias-Variance Tradeoff

Application - House Price, Cancer Prediction, Insurance Prediction

7. Preprocessing for Machine Learning

Introduction to Preprocessing

StandardScaler

MinMaxScaler

RobustScaler

Normalization

Binarization

Encoding Categorical (Ordinal & Nominal) Features

Imputation

Polynomial Features

Custom Transformer

Text Processing

CountVectorizer

TfIdf

HashingVectorizer

Image using skimage

8. Decision Trees

Introduction to Decision Trees

The Decision Tree Algorithms

Decision Tree for Classification

Decision Tree for Regression

Advantages & Limitations of Decision Trees

Application - Clothe Prediction

9. Naive Bayes

Introduction Bayes' Theorm

Naive Bayes Classifier

Gaussian Naive Bayes

Multinomial Naive Bayes

Burnolis' Naive Bayes

Naive Bayes for out-of-core

Application - Text Classification

10. Composite Estimators using Pipelines & FeatureUnions

Introduction to Composite Estimators

Pipelines

TransformedTargetRegressor

FeatureUnions

ColumnTransformer

GridSearch on pipeline

Application - Author classification

11. Model Selection & Evaluation

Cross Validation

Hyperparameter Tuning

Model Evaluation

Model Persistance

Validation Curves

Learning Curves

12. Feature Selection & Dimensionality Reduction

Introduction to Feature Selection

VarianceThreshold

Chi-squared stats

ANOVA using f_classif

Univariate Linear Regression Tests using f_regression

F-score vs Mutual Information

Mutual Information for discrete value

Mutual Information for continues value

SelectKBest

SelectPercentile

SelectFromModel

Recursive Feature Elemination

PCA

SVD

Application - Credit Risk Prediction

13. Nearest Neighbors

Fundamentals of Nearest Neighbor Algorithm

Unspervised Nearest Neighbors

Nearest Neighbors for Classification

Nearest Neighbors for Regression

Nearest Centroid Classifier

Application -

14. Clustering Techniques

Introduction to Unsupervised Learning

Clustering

Similarity or Distance Calculation

Clustering as an Optimization Function

Types of Clustering Methods

Partitioning Clustering - KMeans & Meanshift

Hierarchial Clustering - Agglomerative

Density Based Clustering - DBSCAN

Measuring Performance of Clusters

Comparing all clustering methods

Application - Grouping similar customers

15. Anomaly Detection

What are Outliers ?

Statistical Methods for Univariate Data

Using Gaussian Mixture Models

Fitting an elliptic envelope

Isolation Forest

Local Outlier Factor

Using clustering method like DBSCAN

16. Support Vector Machines

Introduction to Support Vector Machines

Maximal Margin Classifier

Soft Margin Classifier

SVM Algorithm for Classification

SVM for Regression

Hyper-parameters in SVM

Application - Face recognition

17. Dealing with Imbalanced Classes

What are imbalanced classes & their impact ?

OverSampling

UnderSampling

Connecting Sampler to pipelines

Making classification algorithm aware of Imbalance

Anomaly Detection

Application - Fraud detection

18. Ensemble Methods

Introduction to Ensemble Methods

RandomForest

AdaBoost

GradientBoostingTree

VotingClassifier

Application - Malicious data detection

19. Recommendation Engine

Understanding distance vector calculation - cosine, euclidean, manhatten

Types of Recommendation Engines

Recommendation based on similarity

Application - Grouping videos based on description

20. Projects

Credit Risk Prediction

Insurance Amount Prediction

Face Generation

Text Classification

Network Spam detection

Horror Novel author Prediction

Cloth Type Prediction

Employee Attrition Prediction

Customer Churn Prediction

Video recommandation based on description

Frequently Asked Questions

What are the modes of training for "Artificial Intelligence" course?

This "Artificial Intelligence" course is an instructor-led training (ILT). The trainer travels to your office location and delivers the training within your office premises. If you need training space for the training we can provide a fully-equipped lab with all the required facilities. The online instructor-led training is also available if required. Online training is live and the instructor's screen will be visible and voice will be audible. Participants screen will also be visible and participants can ask queries during the live session.

Will I be provided with any study material during the "Artificial Intelligence" training?

Participants will be provided "Artificial Intelligence"-specific study material. Participants will have lifetime access to all the code and resources needed for this "Artificial Intelligence". Our public GitHub repository and the study material will also be shared with the participants.

What is the pedagogy of zekeLabs?

All the courses from zekeLabs are hands-on courses. The code/document used in the class will be provided to the participants. Cloud-lab and Virtual Machines are provided to every participant during the "Artificial Intelligence" training.

What is the duration of this course?

The "Artificial Intelligence" training varies several factors. Including the prior knowledge of the team on the subject, the objective of the team learning from the program, customization in the course is needed among others. Contact us to know more about "Artificial Intelligence" course duration.

What would be the venue for the "Artificial Intelligence" training?

The "Artificial Intelligence" training is organised at the client's premises. We have delivered and continue to deliver "Artificial Intelligence" training in India, USA, Singapore, Hong Kong, and Indonesia. We also have state-of-art training facilities based on client requirement.

Who is the trainer for "Artificial Intelligence" training?

Our Subject matter experts (SMEs) have more than ten years of industry experience. This ensures that the learning program is a 360-degree holistic knowledge and learning experience. The course program has been designed in close collaboration with the experts working in esteemed organizations such as Google, Microsoft, Amazon, and similar others.

Can we customize this course based on our requirements?

Yes, absolutely. For every training, we conduct a technical call with our Subject Matter Expert (SME) and the technical lead of the team that undergoes training. The course is tailored based on the current expertise of the participants, objectives of the team undergoing the training program and short term and long term objectives of the organisation.

How can I reach out to you if I have any other queries regarding the "Artificial Intelligence" course?

Drop a mail to us at [email protected] or call us at +91 8041690175 and we will get back to you at the earliest for your queries on "Artificial Intelligence" course.

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