Master Data Science & Become an Expert

Topics: Key Elements of Machine Learning & Data Science & differences between them, Data Warehousing, Business Intelligence, Data Visualization, Data Mining, Machine Learning, Artificial Intelligence, Cloud Computing, Big Data.

Topics: What is Machine Learning (ML)? How machines learn, Basics of Classification, Regression and Clustering algorithms, Creating your first Prediction Model, Training & Model Evaluation, Choosing Machine Learning Algorithm.

Topics: Operators, Operands and Expressions, Python Data Types, Conditional statements, Loops, Lists, dictionaries, Tuples, Programming practice, Iterators & Generators, File Handling, Modules and Libraries, Classes and Objects, String Formatting, Decorators, Context Managers, Regular Expressions, List and Dictionary Comprehensions, Lambda and Argument Passing, Multiple Inheritance.

Topics: Linear Algebra (Vectors, Matrix, Eigen Values), Probability and Statistics, Hypothesis testing, Optimization.

Topics: Introduction to Numpy, Arrays, Matrices, Various operations on arrays and matrices, Introduction to Pandas, Reading csv and matlab files, Data frame object manipulation, Various operations on data frame, Visualization using Matplotlib, Scatter plots, line plots, Advance visualization using Seaborn, Histograms, heatmaps, box plots.

Topics: Basic Functionalities of a data object, Merging, Concatenation, Types of Joins, Exploring a Dataset, Analysing a dataset, Pandas Functions (Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()), GroupBy operations, Aggregation, Data Collection & Preparation, Data Mugging, Outlier Analysis, Missing value treatment, Feature Engineering, Data Transformation, Normalization vs Standardization, Creating Dummies, Dimensionality Reduction, Principal Component Analysis.

Topics: Confidence Interval, Student's t distribution, Binomial Distribution, A/B Testing, Hypothesis Testing, t-Tests, ANOVA, Chi-square test, KNN, PCA, Categorical Variables, R Square.

Topics: Linear & Logistic and Regression Techniques, Problem of Collinearity, WOE and IV, Residual Analysis, Heteroscedasticity, Homoscedasticity.

Topics: Supervised Machine Learning algorithms (Linear Regression, Multi Feature, Logistic Regression, 2 Class and Multi class, Decision/Classification Trees, Ensemble Models, Bagging, Boosting, Random Forest, K-Nearest Neighbours (KNN), Naive Bayes), Introduction to Neural Network (DeepLearning), Feed Forward Neural Network, Forward Propagation, Backward Propagation, Support Vector Machine, Unsupervised Machine Learning algorithms (Clustering with K-means Clustering), Bias-Variance Tradeoff, Regularization, Parameter tuning & grid search optimization.

Topics: Hands-on projects including Handwritten Digit Recognition, Email Spam Detection, Image Compression, Flower Species Classification, Titanic Survivor Classification, FIFA Ranking Dataset Analysis, Profit Prediction, Business Case Studies, Wine Classification, and various Kaggle competition datasets.