Data science is an essential part of many industries today, given the massive amounts of data that are produced. Its popularity has grown over the years and companies have started implementing data science techniques to grow their business and increase customer satisfaction. Machine learning is the backbone of Data science. This course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, SQL and concepts of Machine Learning.

**Duration : **4 Months

**Ideal for : **Freshers and professionals with 1-2 years of Industry experience who want to start their career in IT Sector as Data Analyst, Data Engineers or Data Scientist for various domains of Data science, Artificial Intelligence and Machine learning.

- Python? History of Python.
- Python Data Types
- What are Keywords, What are variables
- Introduction to Python
- Control Flow Statements
- Data Structures
- Functions
- Object oriented Programming
- Exception Handling and GUI
- Project Discussion

- What is SQL?
- Why we need SQL ?
- What is Database Management System (DBMS)?
- Types of DBMS
- Execution of SQL Query
- Difference between SQL & MYSQL
- Introduction to MYSQL
- Installation of MYSQL Server
- Download Sample Database
- Basic SQL Keywords
- Joins
- DML / DDL

- Introduction
- Distributions and Various
- Referential Statistics
- Case Studies

- Industry Case Studies
- OLA Ride Time Prediction
- Gmail Spam/Not Spam Prediction
- Amazon Reviews Sentiment Predictions
- Data Science Vs DataAnalysis Vs Machine Learning Vs Deep Learning
- Introduction to Numpy, Pandas, Sci-kit Learn and
- Matplotlib Library
- Importing data from different Sources

- Basic Terminologies and Basic Maths
- Traditional Programming Vs Machine Learning
- Types of Machine Learning Problems
- Supervised and Unsupervised Learning
- Classification and Regression
- Over fitting and Under fitting
- What is a point and a Vector?
- Distance between 2 points, Distance between a point and a line.
- Equation of a line, Equation of a Plane, Equation of a hyperplane.
- Dot product and Projection of one vector onto another.
- Basics of Differentiation
- KNN(K nearest Neighbour) Algorithm
- Geometric Intuition of KNN
- Mathematical Intuition of KNN
- Limitations of KNN
- What are Hyper-parameters?
- Hyper-parameters Tuning
- Why do we need Cross-Validation?
- Code Walkthrough on KNN

- Supervised Learning continues
- Naive Bayes algorithm
- What is Conditional Probability
- What is Naive about Naive Bayes?
- Geometric Intuition of Naive Bayes
- Mathematical Intuition of Naive Bayes
- Limitations of Naive Bayes
- Hyperparameter Tuning in Naive Bayes
- Code Walkthrough of Naive Bayes
- Introduction to Logistic Regression
- Mathematical Intuition of Logistic Regression
- Why do we need sigmoid function?
- Regularisation (L1 and L2)
- Limitations of Logistic Regression
- Code Walkthrough of Logistic Regression
- Introduction to Linear Regression
- Geometric and Mathematical Intuition
- Assumptions of Linear Regression
- Limitations of Linear Regression
- Code Walkthrough of Linear Regression
- Optimisation Theory
- Convex and Non Convex Functions
- Gradient Descent , Stochastic Gradient Descent
- Introduction to SVM (Support Vector Machine
- Geometric Intuition
- Mathematical Intuition

- Decision Tree and Assembles
- Decision Tree
- Geometric Intuition of Decision Tree
- Mathematical Intuition of Decision Tree
- Entropy and Gini Impurity
- Information Gain
- Limitations of Decision Tree
- Code Walkthrough of Decision Tree
- What is Ensembles
- Bagging and Boosting
- What is Ensembles?
- Bagging and Boosting
- Concept of Bootstrapping
- Introduction to Random Forest
- Variance and Bias
- Geometric Intuition of Random Forest
- Why Random Forest is so famous?
- Code Walkthrough of Random Forest

- Performance Matrix & Different Situations in Supervised Learning
- Accuracy
- Why Accuracy as a metric will fail in most of the real world cases?
- Precision and Recall
- F1 Score
- Confusion Matrix
- Log-loss
- ROC-AUC Curve
- RMSE(Root Mean Square Error)
- R2(Coeficient of Determinant)
- MAD(Median Absolute Deviation)
- How to Handle Outliers in the data?
- How to deal with the imbalance data?
- How to handle categorial data?
- Scaling of Features
- Curse of Dimensionality

- Unsupervised learning & Dimension Reduction
- What is Unsupervised Learning?
- What is Clustering?
- K-Means Clustering
- Hierarchal Clustering
- Why Dimensions Reduction?
- PCA(Principle Component Analysis)

- Machine Learning Project
- Business Problem
- Contraints
- Data Collection
- Formulate Business Problem to Machine Learning
- Problem , Data Cleaning
- Data Preprocessing
- EDA(Exploratory Data Analysis)
- Modelling
- Evaluating the Performance of the models
- Retrain if necessary , Deployment