Hands on training on Statistics, EDA Machine learning algorithms, Deep learning, NLP and Computer Vision.

Curriculum.Python for AI & ML

• Python Basics

Python Functions and Packages
Working with Data Structures,Arrays,
Vectors & Data Frames
Jupyter Notebook - Installation &
Function
Pandas, NumPy, Matplotlib, Seaborn

• Statistical Learning

Descriptive Statistics
Probability & Conditional Probability
Hypothesis Testing
Inferential Statistics
Probability Distributions

• MACHINE LEARNING
• Supervised Learning

Linear Regression
Logistic Regression
Naive Bayes Classifiers
K-NN Classification
Support Vector Machines

• Unsupervised Learning

K-means Clustering
Hierarchical Clustering
Dimension Reduction-PCA

• Ensemble Techniques

Decision Trees
Bagging
Random Forests
Boosting

• Recommendation Systems

Introduction to Recommendation
Systems
Popularity-based Model
Content-based Recommendation
System
Collaborative Filtering (User
Similarity & Item Similarity)

• ARTIFICIAL INTELLIGENCE

Introduction to Neural Networks
and Deep Learning
Introduction to Perceptron
& Neural Networks
Activation and Loss functions
Gradient Descent
Batch Normalization
TensorFlow & Keras for Neural
Networks
Hyper Parameter Tuning

• 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

• Sequential Models and NLP

Introduction to Sequential Data
RNNs and its Mechanisms
(Vanishing & Exploding Gradients)
LSTMs - Long Short-Term Memory
GRUs - Gated Recurrant Unit
LSTMs Applications
Time Series Analysis
LSTMs with Attentive Mechanism
Transformers,BERT,GPT

• Computer Vision

Introduction to Convolutional
Neural Networks
Convolution, Pooling, Paddling &
its Mechanisms
Forward Propagation &
Backpropagation for CNNs
Architectures like AlexNet,
VGGNet, InceptionNet & ResNet
Transfer Learning