This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.
Bonus introductions include natural language processing and deep learning.
Below Topics are covered
Chapter – Introduction to Machine Learning
– Machine Learning?
– Types of Machine Learning
Chapter – Setup Environment
– Installing Anaconda, how to use Spyder and Jupiter Notebook
– Installing Libraries
Chapter – Creating Environment on cloud (AWS)
– Creating EC2, connecting to EC2
– Installing libraries, transferring files to EC2 instance, executing python scripts
Chapter – Data Preprocessing
– Null Values
– Correlated Feature check
– Data Molding
– Imputing
– Scaling
– Label Encoder
– On-Hot Encoder
Chapter – Supervised Learning: Regression
– Simple Linear Regression
– Minimizing Cost Function – Ordinary Least Square(OLS), Gradient Descent
– Assumptions of Linear Regression, Dummy Variable
– Multiple Linear Regression
– Regression Model Performance – R-Square
– Polynomial Linear Regression
Chapter – Supervised Learning: Classification
– Logistic Regression
– K-Nearest Neighbours
– Naive Bayes
– Saving and Loading ML Models
– Classification Model Performance – Confusion Matrix
Chapter: UnSupervised Learning: Clustering
– Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method
– Hierarchical Clustering: Agglomerative, Dendogram
– Density Based Clustering: DBSCAN
– Measuring UnSupervised Clusters Performace – Silhouette Index
Chapter: UnSupervised Learning: Association Rule
– Apriori Algorthm
– Association Rule Mining
Chapter: Deploy Machine Learning Model using Flask
– Understanding the flow
– Serverside and Clientside coding, Setup Flask on AWS, sending request and getting response back from flask server
Chapter: Non-Linear Supervised Algorithm: Decision Tree and Support Vector Machines
– Decision Tree Regression
– Decision Tree Classification
– Support Vector Machines(SVM) – Classification
– Kernel SVM, Soft Margin, Kernel Trick
Chapter – Natural Language Processing
Below Text Preprocessing Techniques with python Code
– Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation
– Count Vectorizer, Tfidf Vectorizer. Hashing Vector
– Case Study – Spam Filter
Chapter – Deep Learning
– Artificial Neural Networks, Hidden Layer, Activation function
– Forward and Backward Propagation
– Implementing Gate in python using perceptron
Chapter: Regularization, Lasso Regression, Ridge Regression
– Overfitting, Underfitting
– Bias, Variance
– Regularization
– L1 & L2 Loss Function
– Lasso and Ridge Regression
Chapter: Dimensionality Reduction
– Feature Selection – Forward and Backward
– Feature Extraction – PCA, LDA
Chapter: Ensemble Methods: Bagging and Boosting
– Bagging – Random Forest (Regression and Classification)
– Boosting – Gradient Boosting (Regression and Classification)
![Machine Learning: Learn Deep Learning (Neural Network) from Scratch in Python [Hindi].](https://classbaze.com/wp-content/uploads/2020/11/3130490_d9e8_2.jpg.webp)