Machine Learning

• Getting Started with Machine Learning
• An Introduction to Machine Learning
• What is Machine Learning ?
• Introduction to Data in Machine Learning
• Demystifying Machine Learning
• ML – Applications
• Best Python libraries for Machine Learning
• Artificial Intelligence | An Introduction
• Machine Learning and Artificial Intelligence

10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions

• Introduction to Data in Machine Learning
• Understanding Data Processing
• Python | Create Test DataSets using Sklearn
• Python | Generate test datasets for Machine learning
• Python | Data Preprocessing in Python
• Data Cleaning
• Feature Scaling – Part 1
• Feature Scaling – Part 2
• Python | Label Encoding of datasets

10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
12. Dummy variable trap in Regression Models

• Getting started with Classification
• Basic Concept of Classification
• Types of Regression Techniques
• Classification vs Regression
• ML | Types of Learning – Supervised Learning
• Multiclass classification using scikit-learn

• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer

• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple Linear Regression using Python
• Multiple Linear Regression using R
• Locally weighted Linear Regression
• Generalized Linear Models
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression

• Softmax Regressionusing TensorFlow
• Logistic Regression :
  • Understanding Logistic Regression
  • Why Logistic Regression in Classification ?
  • Logistic Regression using Python
  • Cost function in Logistic Regression
  • Logistic Regression using Tensorflow
• Naive BayesClassifiers
• Support Vector:
  • Support Vector Machines(SVMs) in Python
  • SVM Hyperparameter Tuning using GridSearchCV
  • Support Vector Machines(SVMs) in R
  • Using SVM to perform classification on a non-linear dataset
• Decision Tree:
  • Decision Tree
  • Decision Tree Regression using sklearn
  • Decision Tree Introduction with example
  • Decision tree implementation using Python
  • Decision Tree in Software Engineering
• Random Forest:
  • Random Forest Regression in Python
  • Ensemble Classifier
  • Voting Classifier using Sklearn

Bagging classifier

• ML | Types of Learning – Unsupervised Learning
• Supervised and Unsupervised learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• Random Initialization Trap in K-Means
• ML | K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python

• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model

• Reinforcement learning
• Reinforcement Learning Algorithm : Python Implementation using Q-learning
• Introduction to Thompson Sampling
• Genetic Algorithm for Reinforcement Learning
• SARSA Reinforcement Learning
• Q-Learning in Python

• Introduction to Dimensionality Reduction
• Introduction to Kernel PCA
• Principal Component Analysis(PCA)
• Principal Component Analysis with Python
• Low-Rank Approximations
• Overview of Linear Discriminant Analysis (LDA)
• Mathematical Explanation of Linear Discriminant Analysis (LDA)
• Generalized Discriminant Analysis (GDA)
• Independent Component Analysis

10. Feature Mapping
11. Extra Tree Classifier for Feature Selection
12. Chi-Square Test for Feature Selection – Mathematical Explanation
13. ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
14. Python | How and where to apply Feature Scaling?
15. Parameters for Feature Selection
16. Underfitting and Overfitting in Machine Learning

• Introduction to Natural Language Processing
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Removing stop words with NLTK in Python
• Tokenize text using NLTK in python
• How tokenizing text, sentence, words works
• Introduction to Stemming
• Stemming words with NLTK
• Lemmatization with NLTK

10. Lemmatization with TextBlob
11. How to get synonyms/antonyms from NLTK WordNet in Python?

• Introduction to Artificial Neutral Networks | Set 1
• Introduction to Artificial Neural Network | Set 2
• Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
• Introduction to ANN | Set 4 (Network Architectures)
• Activation functions
• Implementing Artificial Neural Network training process in Python
• A single neuron neural network in Python

• Introduction to Convolution Neural Network
• Introduction to Pooling Layer
• Introduction to Padding
• Types of padding in convolution layer
• Applying Convolutional Neural Network on mnist dataset

• Introduction to Recurrent Neural Network
• Recurrent Neural Networks Explanation
• seq2seq model
• Introduction to Long Short Term Memory
• Long Short Term Memory Networks Explanation
• Gated Recurrent Unit Networks(GAN)
• Text Generation using Gated Recurrent Unit Networks

• Introduction to Generative Adversarial Network
• Generative Adversarial Networks (GANs)
• Use Cases of Generative Adversarial Networks
• Building a Generative Adversarial Network using Keras
• Modal Collapse in GANs

• Deploy your Machine Learning web app (Streamlit) on Heroku
• Deploy a Machine Learning Model using Streamlit Library
• Deploy Machine Learning Model using Flask
• Python – Create UIs for prototyping Machine Learning model with Gradio
• How to Prepare Data Before Deploying a Machine Learning Model?
• https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rp
• Deploying Scrapy spider on ScrapingHub

• Rainfall prediction using Linear regression
• Identifying handwritten digits using Logistic Regression in PyTorch
• Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
• Python | Implementation of Movie Recommender System
• Support Vector Machine to recognize facial features in C++
• Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
• Credit Card Fraud Detection
• NLP analysis of Restaurant reviews
• Applying Multinomial Naive Bayes to NLP Problems

10. Image compression using K-means clustering
11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
12. How Does Google Use Machine Learning?
13. How Does NASA Use Machine Learning?
14. 5 Mind-Blowing Ways Facebook Uses Machine Learning
15. Targeted Advertising using Machine Learning
16. How Machine Learning Is Used by Famous Companies?

• Pattern Recognition | Introduction
• Calculate Efficiency Of Binary Classifier
• Logistic Regression v/s Decision Tree Classification
• R vs Python in Datascience
• Explanation of Fundamental Functions involved in A3C algorithm
• Differential Privacy and Deep Learning
• Artificial intelligence vs Machine Learning vs Deep Learning
• Introduction to Multi-Task Learning(MTL) for Deep Learning
• Top 10 Algorithms every Machine Learning Engineer should know

10. Azure Virtual Machine for Machine Learning
11. 30 minutes to machine learning
12. What is AutoML in Machine Learning?
13. Confusion Matrix in Machine Learning