Data Science & Machine Learning

Data Science & Machine Learning course is offered by AstirMind. AstirMind is a market-leading learning outsourcing company that provides managed training services.

Syllabus:

• Python For AI & ML
• Python Basics
• Python Functions, Packages & Routines Probability
• Jupyter Notebook – Installation & Function
• Pandas, NumPy, Matplotlib, Seaborn
• Working with Data Structures, Arrays, Vectors& Data Frames
• Statistical Learning
• Descriptive Statistics
• Probability & Conditional
• Hypothesis Testing
• Probability Distribution – Types of distribution –binomial, Poisson & normal distribution
• Supervised Learning
• Multiple Variable Linear Regression
• Logistic Regression
• Naïve Bayes Classifiers
• Multiple Regression
• K-NN Classification
• Support Vector Machines
• Unsupervised Learning
• K-Means Clustering
• High-Dimensional Clustering
• Hierarchical Clustering
• Dimension Reduction-PCA
• Ensemble Techniques
• Decision Trees
• Bagging
• Random Forests
• Boosting system
• Recommendation Systems
• Introduction to recommendation system
• Popularity based model
• Hybrid Models
• Content Based recommendation
• Collaborative filtering
• Neural Network Basics
• Gradient Descent
• Batch Normalization
• Hyper Parameter tuning
• Activation and Loss function
• Deep Neural Networks
• Intro to Perceptron & Neural Networks
• Computer Vision
• Intro to Convolutional Neural Networks
• Convolution, Pooling, Padding & Mechanism
• Transfer Learning
• Forward propagation & Back propagation for CNNs
• CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
• Statistical NLP
• Bag of Words Model
• POS Tagging
• Tokenization
• Word Vectorizer
• TF-IDF
• Named Entity Recognition
• Stop Words
• Advanced Computer Vision
• Semantic Segmentation
• Siamese Networks
• YOLO
• Object & face recognition
• Sequential NLP
• Intro to Sequential data Adversarial Net Intro
• Vanishing & Exploding gradients in RNNs
• LSTMs
• GRUs – Gated recurrent unit
• Case study: Sentimental analysis
• RNNs and its mechanisms
• Time series analysis
• LSTMs with attention mechanism
• Case study: Machine Translation
• GANs
• GAN - Generative
• How GANs work?
• Auto Encoders
• Applications of GANs
• Languages & Tools
• Python
• Python ML library scikit-learn
• NLP library NLTK
• Keras
• Data Libraries like Pandas, Numpy, Scipy
• Python visualization library Matplotlib
• Tensor Flow
• Seaborn
• Reinforcement Learning
• Value based methods Q-Learning
• Policy based methods