All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]

 Buy Now
  

Price: 59.99$

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: Un Supervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring Un Supervised Clusters Performace – Silhouette Index Chapter: Un Supervised 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, LDAChapter: Ensemble Methods: Bagging and Boosting- Bagging – Random Forest (Regression and Classification)- Boosting – Gradient Boosting (Regression and Classification)