Data Science and Machine learning

Service Description

Here's a detailed syllabus for a Data Science and Machine Learning course: Module 1: Introduction to Data Science and Machine Learning Introduction to Data Science and its applications Overview of Machine Learning and its types Understanding supervised and unsupervised learning Module 2: Exploratory Data Analysis (EDA) Data Preprocessing techniques Data Visualization techniques Exploring data distributions and relationships Module 3: Regression Analysis Understanding Linear Regression Simple Linear Regression and Multiple Linear Regression Regularization techniques - Ridge and Lasso Regression Module 4: Classification Algorithms Understanding Classification and its types Logistic Regression and Decision Trees Naive Bayes Classifier and K-Nearest Neighbors Module 5: Clustering Algorithms Introduction to Clustering K-Means Clustering and Hierarchical Clustering Density-Based Clustering - DBSCAN Module 6: Feature Engineering and Feature Selection Introduction to Feature Engineering and Feature Selection Techniques for Feature Engineering - Imputation, Binning, Scaling, etc. Techniques for Feature Selection - Filter, Wrapper, and Embedded methods Module 7: Model Evaluation and Validation Model Evaluation metrics - Accuracy, Precision, Recall, F1-Score, etc. Cross-Validation techniques - K-Fold, Stratified K-Fold, and Leave-One-Out Overfitting and Underfitting - Techniques to prevent them Module 8: Introduction to Deep Learning Understanding Artificial Neural Networks (ANNs) Introduction to Deep Learning and its applications Types of Deep Learning models - Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), etc. Module 9: Introduction to Natural Language Processing (NLP) Overview of NLP and its applications Preprocessing techniques for NLP - Tokenization, Stemming, Lemmatization, etc. Techniques for Text Classification and Sentiment Analysis Module 10: Introduction to Big Data Analytics Overview of Big Data Analytics Introduction to Apache Hadoop and Spark Handling Big Data with Hadoop MapReduce and Spark RDDs This syllabus is just an example and may vary depending on the specific course and institution offering it.