### Description

The program consists of a full-fledged transition into Data Science world. Along with the theoretical teaching, there will be case studies associated with each module, as well as there will be a quiz and a case study for the students to complete, to check his understanding of the course with progression. It will be good if the student has more than 3 years of experience. The course includes ML Algorithms, Bayesian Regression, Random Forest, Classification, Decision Trees, SVM, Linear Regression, Neural Networks, Clustering, ML Applications, Weka, Python, Machine learning Libraries, and Cloud-based Machine Learning implementations.

This is an instructor-led course with an average batch size of students. In the hours of online Live training, you will get both the theoretical and practical knowledge needed to build the necessary skills. The institute’s holistic approach is stemmed to meet the long-term needs of the student and hence they provide 100% job/placement assistance with the option of seeking a trial class before the enrolment.

What Will I Learn?

• Descriptive Statistics, Probability and Conditional Probability and Correlation between Variables
• Linear Regression, Multiple Variable Linear Regression and Logistic Regression
• Introduction to NLP, Stop Words and Tokenization

#### Specifications

• Free Demo
• Interactive Learning
• Missed Class Recovery
• Interview Training

AI ML Program

• Introduction to AI/ML
• Topics and Applications
• Course Overview

The root of AI and Machine Learning: Statistics

• Introduction to Statistics
• Descriptive Statistics
• Probability and Conditional Probability
• Correlation between Variables
• Statistical Hypothesis Tests
• Inferential Statistics
• Probability Distributions
• EDA: Exploratory Data Analysis

Machine Learning: Supervised learning

• Linear Regression
• Multiple Variable Linear Regression
• Logistic Regression
• Decision Trees
• Naive Bayes Classifiers
• k-NN Classification
• Support Vector Machines
• Model Evaluation and Case Studies

Unsupervised learning

• K-means Clustering
• Hierarchical Clustering
• Dimension Reduction-PCA

Ensemble Techniques

• Bagging
• Random Forests
• Boosting

Recommendation Systems

• Introduction to Recommendation Systems
• Popularity based model
• Content-based Recommendation System
• Collaborative Filtering (User similarity & Item similarity)
• Hybrid Models

Artificial Intelligence: Introduction to Neural Networks and Deep Learning

• Introduction to Perceptron & Neural Networks
• Activation and Loss functions
• Batch Normalization
• TensorFlow & Keras for Neural Networks
• Hyper Parameter Tuning

Sequential Models and NLP

• Introduction to Sequential data
• RNNs and its mechanisms
• Vanishing & Exploding gradients in RNNs
• LSTMs - Long short-term memory
• GRUs - Gated recurrent unit
• LSTMs Applications
• Time series analysis
• LSTMs with an attention mechanism
• Neural Machine Translation

Computer vision

• Introduction to Convolutional Neural Networks
• Convolution, Pooling, Padding & its mechanisms
• Forward Propagation & Backpropagation for CNN's
• CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
• Transfer Learning

• Object Detection
• YOLO, R-CNN, SSD
• Semantic Segmentation
• U-Net
• Face Recognition using Siamese Networks

NLP Basics(Natural Language Processing)

• Introduction to NLP
• Stop Words
• Tokenization
• Stemming and lemmatization
• Bag of Words Model
• Word Vectorizer
• TF-IDF
• POS Tagging
• Named Entity Recognition

Introduction to GANs (Generative adversarial networks)

• Introduction to GANs
• Generative Networks
• How GANs work?
• DCGANs - Deep Convolution GANs
• Applications of GANs

Introduction to Reinforcement Learning (RL)

• RL Framework
• Component of RL Framework
• Examples of RL Systems
• Types of RL Systems
• Q-learning

Focus on One Programming Language: R or Python

• Introduction to Python
• Python for Data Science
• Data Visualization in Python

Optional: Optimization Module

• Basics of optimization
• Linear Programming
• Nonlinear Programming
• Integer Programming

#### Ms.Nidhi Rai

The trainer has 7 years of industry experience as a data scientist. The trainer is an expert in Python, R, SAS, SPSS, Statistics, GAMS, and Optimization Techniques

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### Description

The program consists of a full-fledged transition into Data Science world. Along with the theoretical teaching, there will be case studies associated with each module, as well as there will be a quiz and a case study for the students to complete, to check his understanding of the course with progression. It will be good if the student has more than 3 years of experience. The course includes ML Algorithms, Bayesian Regression, Random Forest, Classification, Decision Trees, SVM, Linear Regression, Neural Networks, Clustering, ML Applications, Weka, Python, Machine learning Libraries, and Cloud-based Machine Learning implementations.

This is an instructor-led course with an average batch size of students. In the hours of online Live training, you will get both the theoretical and practical knowledge needed to build the necessary skills. The institute’s holistic approach is stemmed to meet the long-term needs of the student and hence they provide 100% job/placement assistance with the option of seeking a trial class before the enrolment.

What Will I Learn?

• Descriptive Statistics, Probability and Conditional Probability and Correlation between Variables
• Linear Regression, Multiple Variable Linear Regression and Logistic Regression
• Introduction to NLP, Stop Words and Tokenization

#### Specifications

• Free Demo
• Interactive Learning
• Missed Class Recovery
• Interview Training
₹30,900 ₹ 30,900

Hurry up!! Limited seats only

### Related Classes

₹30,900 ₹30,900

Hurry up!! Limited seats only