Artificial Intelligence-training-in-bangalore-by-zekelabs

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.
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Assignments
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Industry Level Projects
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Certification

Artificial Intelligence Course Curriculum



What is not Machine Learning
Types of ML - Supervised, Unsupervised
Unsupervised - Clustering, Association
Introduction to NumPy
Access
Methods
Introduction to Pandas
Loading CSV,JSON
Descriptive Statistics
Handling Missing Data
Handling Duplicates
Merge, Join & Concatenate
Normalizing JSON
Essential Linear Algebra
Calculus
Mean, Median, Mode, Quantile
Introduction to matplotlib, plotly, bokeh, tablue
Title, Labels, Legends, Grid, colormap, xticks, yticks
Sub Plotting
Histogram
Plotting distributions
Simple Linear Regression using Ordinary Least Squares
Regularized Regression Methods - Ridge, Lasso, ElasticNet
OnLine Learning Methods - Stochastic Gradient Descent & Passive Aggrasive
Polynomial Regression
Application - House Price, Cancer Prediction, Insurance Prediction
Introduction to Preprocessing
MinMaxScaler
Normalization
Encoding Categorical (Ordinal & Nominal) Features
Polynomial Features
Text Processing
TfIdf
Image using skimage
Introduction to Decision Trees
Decision Tree for Classification
Advantages & Limitations of Decision Trees
Introduction Bayes' Theorm
Gaussian Naive Bayes
Burnolis' Naive Bayes
Application - Text Classification
Introduction to Composite Estimators
TransformedTargetRegressor
ColumnTransformer
Application - Author classification
Cross Validation
Model Evaluation
Validation Curves
Introduction to Feature Selection
Chi-squared stats
Univariate Linear Regression Tests using f_regression
Mutual Information for discrete value
SelectKBest
SelectFromModel
PCA
Application - Credit Risk Prediction
Fundamentals of Nearest Neighbor Algorithm
Nearest Neighbors for Classification
Nearest Centroid Classifier
Introduction to Unsupervised Learning
Similarity or Distance Calculation
Types of Clustering Methods
Hierarchial Clustering - Agglomerative
Measuring Performance of Clusters
Application - Grouping similar customers
What are Outliers ?
Using Gaussian Mixture Models
Isolation Forest
Using clustering method like DBSCAN
Introduction to Support Vector Machines
Soft Margin Classifier
SVM for Regression
Application - Face recognition
What are imbalanced classes & their impact ?
UnderSampling
Making classification algorithm aware of Imbalance
Application - Fraud detection
Introduction to Ensemble Methods
AdaBoost
VotingClassifier
Understanding distance vector calculation - cosine, euclidean, manhatten
Recommendation based on similarity
Credit Risk Prediction
Face Generation
Network Spam detection
Cloth Type Prediction
Customer Churn Prediction

Frequently Asked Questions


We have options for classroom-based as well as instructor led live online training. The online training is live and the instructors screen will be visible and voice will be audible. Your screen will also be visible and you can ask queries during the live session.

The training on "Artificial Intelligence" course is a hands-on training. All the code and exercises will be done in the live sessions. Our batch sizes are generally small so that personalized attention can be given to each and every learner.

We will provide course-specific study material as the course progresses. You will have lifetime access to all the code and basic settings needed for this "Artificial Intelligence" through our GitHub account and the study material that we share with you. You can use that for quick reference

Feel free to drop a mail to us at [email protected] and we will get back to you at the earliest for your queries on "Artificial Intelligence" course.

We have tie-ups with a number of hiring partners and and placement assistance companies to whom we connect our learners. Each "Artificial Intelligence" course ends with career consulting and guidance on interview preparation.

Minimum 2-3 projects of industry standards on "Artificial Intelligence" will be provided.

Yes, we provide course completion certificate to all students. Each "Artificial Intelligence" training ends with training and project completion certificate.

You can pay by card (debit/credit), cash, cheque and net-banking. You can also pay in easy installments. You can reach out to us for more information.

We take pride in providing post-training career consulting for "Artificial Intelligence".



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