Topics (High level): 

• Getting Started with Python
• Virtual Environments
• Data Types in Python
• Indexing & Slicing
• In-built Functions and Methods
• Lists, Tuples, Dictionaries, Sets
• List and Dictionary Comprehensions
• Conditional Statements
• Control Flow : Loops & Iterations
• Conditional & Infinite Looping
• Advanced Looping Concepts
• Functional Programming in Python
• Custom functions in Python
• Higher order functions
• Lexical Scoping in Python
• Lambda & Map Functions
• Decorators
• Errors and Exception Handling
• OOPs in Python
• Inheritance, Encapsulation
• File I/O Operations
• Coding Best Practices
• Competitive Coding

Topics (High level): 

• Introduction to Databases and SQL.
• Installing MySQL.
• DDL - Create, Alter and Drop.
• SQL Data Types
• Setting up the data
• Data Retrieval - Select, Where, Count, Distinct, Like
• Data Retrieval - Order by, Limit, Offset.
• Get Summary Info. using Group by.
• Group by Having clause.
• Creating additional columns.
• Intro. to Joins.
• Joins - INNER, LEFT, RIGHT.
• Joins - Outer.
• Sub Queries, Correlated sub-queries.
• Window functions - RANK, ROWNUM, NTILE, LEAD, LAG.
• DML - Insert, Delete, Update.
• DCL - Grant, Revoke.
• Reading the data from MySQL table using Python.
• Writing data to MySQL table using Python.
• Inserting multiple records at once to the table using Python

Topics (High level): 

• Numerical Python (NumPy). 
• Data wrangling using Pandas.
• Data Visualization using Matplotlib and Seaborn.

Topics (High level): 

• Types of data, sample and population.
• Estimates of location.
• Estimates of location - coding.
• Estimates of variability.
• Coefficient of variation.
• Descriptive statistics.
• Intro. to Probability, Random Experiment and Random variable.
• Calculating Probability.
• Conditional probability.
• Bayes Theorem and problems.
• Discrete Random variable.
• Probability Mass Function (PMF).
• Bernoulli Distribution.
• Bernoulli Distribution and PMF using Python.
• Binomial distribution.
• Geometric, Hyper Geometric distribution.
• Continuous random variable, Probability Density Function (PDF).
• Cumulative Distribution Function (CDF).
• Gaussian Distribution.
• Standard Normal Distribution, Z Score.
• Normal distribution coding.
• Normal approximation to Binomial.
• Log normal distribution.
• Law of large numbers.
• Central Limit Theorem (CLT).
• Verifying CLT.
• Confidence intervals.
• Margin of error, t-distribution.
• Hypothesis Testing.
• Z Test.
• One sample and two sample t-test.
• t-test implementation.
• Paired t-test.
• ANOVA test.
• Chi Square test.
• Covariance.
• Correlation - Pearson,
• Spearman and Kendall's Tau

Topics (High level): 

• Machine Learning Life Cycle
• Predictive Modeling Steps
• EDA Steps
• Variable Types
• Variable Identification
• Categorical Encoding - Label, Ordinal Encoding
• Categorical Encoding - One Hot Encoding
• Categorical Encoding - Frequency Encoding
• Missing Value Identification
• Univariate Analysis - Descriptive Statistics
• Univariate Analysis - Data Profiling
• What are Outliers?
• Impact of Outliers - Why are they bad?
• Identifying Outliers - Box Plot approach
• Identifying Outliers - Z Score method
• Identifying Outliers - Modified Z Score method
• Outlier Treatment - Ways to handle Outliers
• Multivariate Outlier Identification
• Multivariate Outlier Identification - Implementation
• Need for Scaling
• Standardization and Normalization of data
• Intro. to Bivariate Analysis
• Continuous-Continuous
• Categorical-Categorical : Hypothesis Testing
• Categorical-Categorical : Visualizations
• Categorical-Continuous
• Quantile-Quantile Plot (QQ Plot)
• Kolmogorov Smirnov Test (KS Test)

Topics (High level): 

• Introduction to Linear Algebra
• Vector Operations
• Vector Dot Product
• Projection of a Vector
• Basis, Span and Linear Dependence
• System of Linear equations
• Solving System of Linear equations
• Types of Matrices
• Linear Transformations
• Eigen Vectors, Eigen Values
• Eigen Decomposition
• Deriving Eigen Vectors and Values using Python

Topics (High level): 

• Getting Started With ML.
• Implementing simple Supervised Algorithm.
• K Nearest Neighbors (K-NN).
• Performance measures of Classification model.
• Linear Regression.
• Performance measures of Regression model.
• Solving optimization problems.
• Constrained optimization - Ridge, Lasso and Elastic Net.
• Logistic regression.
  Support Vector Machines (SVM).
• Principal Component Analysis (PCA).
• Decision trees.
• Ensemble models.
• Random forest.
• Xgboost.
• Gradient bossting.
• Ada boost.
• Model Stacking and Blending.
• Case Study : Microsoft Malware Detection.
• Case Study : Vesta Fraud Detection.
• Foundations of Natural Language Processing (NLP).
• Case Study : Classification of Amazon reviews.

Topics (High level): 

• Implementing simple Un-Supervised Algorithm.
• Clustering - KMeans, KMeans++, K-Medoids.
• Clustering - Hierarchical, DBSCAN.
• Recommender Systems and types.
• Matrix Factorization - SVD.

Topics (High level): 

• Fundamentals of Deep Learning.
• Intro. to Google Colab. Why use Colab?
• Perceptron, MLP.
• Visualizing Neural Network.
• Training a single neuron model.
• Training MLP.
  Back propagation.
• Activation Functions : Sigmoid, Tanh, ReLu, Leaky ReLu, Softmax
• Vanishing Gradient Descent.
• Performance of deep learning models.
• Weight Initialization.
• Batch Normalization.
• Model generalization - Dropouts.
• Variants of Gradient Descent.
• Optimizers : AdaGrad, Adam, RMSProp.
• Case Study : Image classification.
• Convolution Neural Nets.
• LSTM
• GRU
• RNN, Deep RNN

Topics (High level): 

• Ways of deploying ML or DL model.
• ML Ops overview.