Course Overview

1. What is Machine Learning?
2. Applications of Machine Learning
3. Machine learning methods (Supervised, Unsupervised, Reinforcement)
4. Machine learning algorithms (Regression, Classification, Clustering, Association)
5. Python libraries suitable for Machine Learning
6. Basic ideas about AI, Machine learning for AI, other approaches towards AI

1. Installing Necessary Applications and Creating Environment
2. Hello World
3. Others Settings as Required

1. Types of Machine Learning Algorithms
2. Labelled Dataset, Unlabeled Dataset
3. Model Fitting (Overfitting, Good Fitting, Underfitting)
4. Training and Testing Data
5. Important module and library (Numpy, Pandas, Matplotlib, Sklearn, ……….)
6. Load the Dataset
7. Separate Training and Testing Data
8. Train Using Training Data (Through the Algorithm)
9. Split Dataset for Train and Test
10. Test or Predict Using Test Data
11. Accuracy of The Model
12. Predict the output manually

Data Visualization, Visualize the Data in graph

1. Difference between data and information

1. Data types, Data pre-processing, Dealing with missing data
2. Standardizing data, why and when to standardize, Scaling

1. Data Exploration and Data Preparation, Data Wrangling
2. Analyzing data to fetch the information

1. Getting data for training, scraping

1. Correlation

Categorical Data

1. Introduction to regression and also explain the applications of regression
2. Linear Regression
3. Non-Linear Regression

1. Bayesian Linear Regression

1. Polynomial regression
2. Gradient Descent
3. Cost function
4. Regularization
5. Ridge and lasso Regression
6. Linear Regression, Linear Regression with Scikit-Learn

1. Evaluate Regression Model Performance

1. Learning Curve
2. Correlation Analysis and Feature Selection
3. Understanding MNIST Dataset
4. Flatten a Matrix

1. Introduction to Classification & also explain the applications of classification
2. Logistic Regression, Sigmoid function
3. Decision tree Classifier Algorithm
   1. Introduction to Decision Tree
   2. Training and Visualizing a Decision Tree
   3. Visualizing Boundary
   4. Tree Regression, Regularization and Over Fitting
4. K-Nearest Neighbors (K-NN) Classifier Algorithm
   1. KNN Introduction
   2. Addition Materials

1. Random Forests Classifier Algorithm

1. SVM (Support Vector Machine)

1. 1. Support Vector Machine (SVM) Concepts
   2. Linear SVM Classification
   3. Polynomial Kernel
   4. Radial Basis Function
   5. Support Vector Regression

1. Stochastic Gradient Descent (SGD) Classifier Algorithm

Limitations of linear classifier and evaluate abilities of non-linear classifiers using a data set

1. Application of Unsupervised learning, examples, and applications
2. Clustering

1. 1. Introduction to Clustering
   2. Introduction to k-Means Clustering
   3. Introduction to Hierarchical Clustering
   4. Density Based Clustering

1. Measuring the distance between two clusters
2. k-means algorithm

Limitations of K-means clustering

1. Introduction to dimensionality reduction and also explain the applications of it’s
2. Feature selection & extraction
3. Dimensionality reduction via Principal component analysis
4. Eigenvalue and Eigenvectors
5. Hands on PCA on MNSIT data

1. LDA vs PCA

1. What is reinforcement learning
2. Applications of reinforcement learning
3. Components of RL
4. Approaches to RL
5. RL algorithms
6. Deep reinforcement learning

1. What is NLP? Why NLP
2. Applications of NLP
3. Components of NLP
4. NLP techniques

1. Why deep learning?
2. Introduction to Neural Networks
3. Neural networks Architecture
4. Applications of neural networks
5. Biological Neuron vs Artificial Neuron
6. Artificial Neural networks, layers
7. Classification basics using Artificial neural network

1. Dealing with Image in ANN

1. Over and Under Fitting

Machine Learning Workflow

1. Introduction to CNN, idea and math, why and when CNN
2. Using CNN for Image classification
3. Multiclass classification with CNN
4. Neural Network Revision
5. Visualizing CNN

Training Your CNN

1. Intro to Recommender Systems
2. Content-based Recommender Systems
3. Collaborative Filtering

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