AI / ML using Python

AI & ML develogyinfosystems

Python Basics for AI & ML

1
Pandas, NumPy, Matplotlib, Seaborn

Statistical Learning

1
Descriptive Statistics
2
Probability – Probability Distributions – Central Limit theorem
3
Inferential Statistics – SamplingConcept of Hypothesis testing
4
Statistical Methods- Z/t tests (one sample, paired and unpaired), ANOVA, correlations, Chi-/squared

SUPERVISED LEARNING

1
Linear regression
2
Multiple regression
3
Logistic regression
4
Time Series Forecasting
5
Naive Bayes classifiers

SUPERVISED LEARNING

1
K-NN classification
2
Support vector machines
3
Decision Trees
4
Random forest
5
Ensemble Modelling– o Bagging o Boosting o Stacking

UNSUPERVISED LEARNING

1
K-means clustering
2
Hierarchical clustering
3
Association Rules
4
Dimension Reduction-PCA Singular Value Decomposition

NEURAL NETWORKS BASICS

1
Gradient Descent
2
Batch Normalization
3
Hyper parameter tuning
4
Tensor Flow & Keras for Neural Networks & Deep Learning
5
Introduction to Perceptron & Neural Networks
6
Activation and Loss functions
7
Deep Neural Networks

CONVOLUTIONAL NEURAL NETWORKS (CNN)

1
Introduction to Convolutional Neural Networks
2
CNN Applications
3
Architecture of a CNN
4
Convolution and Pooling layers in a CNN
5
Understanding and Visualizing a CNN
6
Transfer Learning and Fine-tuning CNN

RECURRENT NEURAL NETWORKS (RNN)

1
Intro to RNN Model
2
Application use cases of RNN
3
Modelling sequences
4
Training RNNs with Backpropagation
5
Long Short-Term Memory (LSTM)
6
Recurrent Neural Network Model

1
Text Data Processing
2
• Image processing/Classification
3
Social Media Analysis
4
Sentimental Analysis

Be the first to add a review.

Please, login to leave a review
Add to Wishlist
Lectures: 42

Recent Comments

    Social Network