Fundamentals of Data Science and Machine Learning Introduction to Data Science The need for Data Science BigData and Data Science’ Data Science and machine learning  Data Science Life Cycle Data Science Platform Data Science Use Cases  Skill Required for Data Science Mathematics For Data Science Linear Algebra Vectors Matrices Optimization Theory Of optimization Gradients Descent Introduction to Statistics Descriptive vs. Inferential Statistics Types of data Measures of central tendency and dispersion Hypothesis & inferences Hypothesis Testing Confidence Interval Central Limit Theorem Probability and Probability Distributions Probability Theory Conditional Probability Data Distribution Binomial Distribution Normal Distribution Machine Learning Python for ML along with Module1 Introduction to NumPy Array Operations Arrays Functions Array Mathematics Array Manipulation Array I/O Importing Files with Numpy Data Manipulation with Pandas Data Frames I/O Selection in DFs Retrieving in DFs Applying Functions Reshaping the DFs – Pivot Combining DFs Merge Join Data Alignment SciPy Matrices Operations Create matrices Inverse, Transpose, Trace, Norms , Rank etc Matrices Decomposition Eigen Values & vectors SVDs MatPlotLib Basics of Plotting Plots Generation Customization Store Plots SciKit LearnBasics Data Loading Train/Test Data generation Preprocessing Generate Model Evaluate Models Machine Learning Exploratory Data Analysis Data Exploration Missing Value handling Outliers Handling Feature Engineering Feature Selection Importance of Feature Selection in Machine Learning Filter Methods Wrapper Methods Embedded Methods Machine Learning: Supervised Algorithms Classification Introduction to Machine Learning Logistic Regression Naïve Bays Algorithm K-Nearest Neighbor Algorithm Decision Tress (SingleTree) Support Vector Machines Model Ensemble Bagging Random Forest Boosting Gradient Boosted Trees Model Evaluation and performance K-Fold Cross Validation ROC, AUC etc… Machine Learning: Regression Simple Linear Regression Multiple Linear Regression Decision Tree and Random Forest Regression Machine Learning: Unsupervised Learning Algorithms Similarity Measures Cluster Analysis and Similarity Measures K-chical Clustering Principal means Clustering HierarComponents Analysis Association Rules Mining & Market Basket Analysis Text Mining Basics Term Document Matrix TF-IDF Twitter Sentiment Analysis Project Work Machine Learning end to end Project blueprint Regression predictive modeling – House Price Prediction Classification predictive modeling – Binary Classification Widespread coverage for each Topic Various Approaches to Solve Data Science Problem Pros and Cons of Various Algorithms and approaches