ARTIFICIAL INTELLIGENCE COURSE IN BANGALORE

People Technologies People Technologies Artificial Intelligence (AI) Training in Bangalore

People Technologies provides Advance Level Artificial Intelligence (AI) Training in Bangalore - JP Nagar, Jayanagar, BTM Layout, Koramangala and Marathahalli with guaranteed placements across 100+ top paying clients with an average of 4 Lpa as starting salary package


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Best Artificial Intelligence (AI) Training Course in Bangalore

With the biggest Real time Artificial Intelligence (AI) training team in Bangalore, People Technologies has successfully conducted Basic, Intermediate and Advanced level Artificial Intelligence (AI) training batches in Bangalore with the latest updated Syllabus, practical scenarios on high configuration lab setup for students practice on Advanced concepts. Dedicated team of placement officers (Biggest Placement team in Bangalore) and with 100's of MNC's, High funded Startups approaching us daily for placements, your dream job in Artificial Intelligence is just a course away.


    100+ Hours Of Training & Practice
    Industry Expert Trainers
    Updated & Current Training Syllabus
    Individual Student Focus
    Certification Guidance
    Live Startup Internships
    Offer Letter Before Training Starts*
    Salaries Starting @ 35k/month

WHY YOU SHOULD OPT FOR ARTIFICIAL INTELLIGENCE TRAINING IN BANGALORE WITH PEOPLE TECHNOLOGIES?

People Technologies as a top educational institution on Artificial Intelligence (AI) training in Bangalore is headed by a senior team of management, experienced trainers and exclusive placement team who have worked with top training institutes in Bangalore exclusively on Artificial Intelligence (AI) course and are backed by experience of handling multiple training institute in India and global locations. We do bulk trainings on Artificial Intelligence (AI) in colleges, corporate and institutes across Bangalore in various direct and channel partner training vendors. The complete team has an experience of handling Artificial Intelligence (AI) training courses in various segments i.e. freshers, experienced, corporate and online trainings. Our USP is training students on practical concepts and avoid boring and long theoretical classes where usually student leaves the training mid way. We have successfully trained and placed over 1000's students on Artificial Intelligence (AI) and that is one the reason we are considered the best place to do trainings in Bangalore.

With an experienced in-house team of global certified trainers on Artificial Intelligence (AI) and also have a huge network of 40+ external freelancers, consultants and corporate trainers globally from various MNC’s who take our Artificial Intelligence trainings and consulting work. We have hired advanced training services from few international locations trainers apart from our Indian based Artificial Intelligence (AI) trainers. We undertake customization for Artificial Intelligence (AI) training for each batch or individual participant to meet their exact project or placement needs. Our motto as a top training institute on Artificial Intelligence (AI) is to impart quality education and also to make sure that we teach only what the participant has come to learn. Our Artificial Intelligence Syllabus are different for freshers who might not need advanced knowledge and for working professionals who are already working on Artificial Intelligence (AI) need not waste time on learning basic. So we keep our student segregated in different batches to make sure a right fit Artificial Intelligence (AI) training is imparted.

Since the inception, People technologies has been No.1 and the most preferred training partners for individuals and companies in Bangalore for in-depth training and consulting expertise on Artificial Intelligence (AI). Once the training is completed we assist the students to gear up for global certifications on Artificial Intelligence (AI) by giving them dedicated session on preparation and successfully clearing the global certification. Our Artificial Intelligence (AI) training contents are modified regularly and updated keeping the latest demand into consideration. We take the help of the senior most resources globally on Artificial Intelligence (AI) to make sure the training content is comparatively more advanced than other training institutes in Bangalore. We have a dedicated team of junior lab instructors who help the student to practice the concepts in real time scenarios and on practical setup for clear understanding which helps having an edge during interviews in comparison to other institute students. Students are taken through a series of FAQ interview documents on Artificial Intelligence (AI) to make sure they are well prepared before attending any interview.

So reading all the above points on our strength by training candidates on Artificial Intelligence (AI), we hope that your ideas are clear now to select People technologies as your preferred training partner in Bangalore. So what are you waiting for?? go ahead and call 91-8197096400 today to get enrolled for one of the most happening course for freshers and working professionals. We will make sure that you practice on the servers, desktops, laptops which have high configuration and a supportive and top class support and training staff to make your learning more interesting and comfortable.


ARTIFICIAL INTELLIGENCE SYLLABUS

  • Chapter - 01 Artificial Intelligence Programming Introduction to Python

    • Introduction to Python
    • Configuration of Development Environment
    • Variable and Strings
    • Functions, Control Flow and Loops
    • Tuple, Lists and Dictionaries
    • Standard Libraries
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 02 Data Science Fundamentals :

    • Introduction to Data Science
    • Real world use-cases of Data Science
    • Walkthrough of data types
    • Data Science project lifecycle
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 03 Introduction to NumPy:

    • Basics of NumPy Arrays
    • Mathematical operations in NumPy
    • NumPy Array manipulation
    • NumPy Array broadcasting
    Theory (00:45 to 01:00 Hrs)|Practical (00:45 to 01:00 Hrs)
    Total (01:30 to 02:00 Hrs)

  • Chapter - 04 Data Manipulation with Pandas

    • Data Structures in Pandas-Series and DataFrames
    • Data cleaning in Pandas
    • Data manipulation in Pandas
    • Handling missing values in datasets
    • Hands-on: Implement NumPy arrays and Pandas DataFrames
    Theory (00:30 to 00:45 Hrs)|Practical (00:30 to 00:45 Hrs)
    Total (01:00 to 01:30 Hrs)

  • Chapter - 05 Data Visualization in Python

    • Plotting basic charts in Python
    • Data visualization with Matplotlib
    • Statistical data visualization with Seaborn
    • Hands-on: Coding sessions using Matplotlib, Seaborn packages
    Theory (00:45 to 01:00 Hrs)|Practical (00:45 to 01:00 Hrs)
    Total (01:30 to 02:00 Hrs)

  • Chapter - 06 Exploratory Data Analysis

    • Introduction to Exploratory Data Analysis (EDA) steps
    • Plots to explore relationship between two variables
    • Histograms, Box plots to explore a single variable
    • Heat maps, Pair plots to explore correlations
    • Perform EDA to explore survival using titanic dataset
    Theory (01:30 to 01:15 Hrs)|Practical (01:30 to 01:15 Hrs)
    Total (03:00 to 02:30 Hrs)

  • Chapter - 07 Introduction to Machine Learning

    • What is Machine Learning?
    • Use Cases of Machine Learning
    • Types of Machine Learning - Supervised to Unsupervised methods
    • Machine Learning workflow
    Theory (02:00 to 04:10 Hrs)|Practical (02:00 to 04:10 Hrs)
    Total (04:00 to 08:20 Hrs)

  • Chapter - 08 Linear Regression

    • Introduction to Linear Regression
    • Use cases of Linear Regression
    • How to fit a Linear Regression model?
    • Evaluating and interpreting results from Linear Regression models
    • Predict Bike sharing demand
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 09 Logistic Regression

    • Introduction to Logistic Regression
    • Logistic Regression use cases
    • Understand use of odds & Logit function to perform logistic regression
    • Predicting credit card default cases
    Theory (00:30 to 00:45 Hrs)|Practical (00:30 to 00:45 Hrs)
    Total (01:00 to 01:30 Hrs)

  • Chapter - 10 Decision Trees & Random Forest

    • Introduction to Decision Trees & Random Forest
    • Understanding criterion(Entropy & Information Gain) used in Decision Trees
    • Using Ensemble methods in Decision Trees
    • Applications of Random Forest
    • Predict passenger survival using Titanic Data set
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 11 Model Evaluation Techniques

    • Introduction to evaluation metrics and model selection in Machine Learning
    • Importance of Confusion matrix for predictions
    • Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
    • Use AUC-ROC curve to decide best model
    • Applying model evaluation techniques to Titanic dataset
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 12 Dimensionality Reduction using PCA

    • Unsupervised Learning: Introduction to Curse of Dimensionality
    • What is dimensionality reduction?
    • Technique used in PCA to reduce dimensions
    • Applications of Principle component Analysis (PCA)
    • Optimize model performance using PCA on SPECTF heart data
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 13 KNearestNeighbours

    • Introduction to KNN
    • Calculate neighbours using distance measures
    • Find optimal value of K in KNN method
    • Advantage & disadvantages of KNN
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 14 Naive Bayes Classifier

    • Introduction to Naive Bayes Classification
    • Refresher on Probability theory
    • Applications of Naive Bayes Algorithm in Machine Learning
    • Classify spam emails based on probability
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 15 K-means Clustering

    • Introduction to K-means clustering
    • Decide clusters by adjusting centroids
    • Find optimal 'k value' in K-means
    • Understand applications of clustering in Machine Learning
    • Segment hands in Poker data and segment flower species in Iris flower data
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

  • Chapter - 16 Support Vector Machines

    • Introduction to SVM
    • Figure decision boundaries using support vectors
    • Identify hyperplane in SVM
    • Applications of SVM in Machine Learning
    • Predicting wine quality using SVM
    Theory (01:00 to 01:15 Hrs)|Practical (01:00 to 01:15 Hrs)
    Total (02:00 to 02:30 Hrs)

ARTIFICIAL INTELLIGENCE TRAINING METHODOLOGY & MATERIALS

  1. You will get to practice on Real Industry Artificial Intelligence Projects & Applications of various Algorithms
  2. Advanced Lab facilities and well equiped class rooms for practising Artificial Intelligence course
  3. Flexible timing with week end batches on Artificial Intelligence for Working Professinal
  4. We conduct Mock interviews as per Artificial Intelligence Industry that will further boost your confidance