Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed. A deep learning model is designed to continually analyze data with a logical structure similar to how a human would draw conclusions
What you'll learn
- Train machine learning algorithms to predict house prices,
- Identify handwriting, detect cancer cells & more
- Solve any problem in your business,
- Job or personal life with powerful Machine Learning models
- Go from zero to hero in Python, Seaborn, Matplotlib,
- Scikit-Learn, SVM, unsupervised Machine Learning etc
Requirements
- Desktop/ Laptop
- Internet connectivity min 2 Mbps
Target Audience
- Data Scientist
- DL Engineer
- Data Engineer
- ML Engineer
- Software Developer
Overview and Application of Data Science
Terminologies commonly used in data science
Data Science roles
Data Science Life Cycle: Essential Stages
Data Science project organization challenges
Formulation of problems and framework to choose the right problems
Hypothesis Formation
Build Comprehensive Hypothesis set
Excel Basics, Formulas and Functions
Excel Charts, Pivot Tables, Sort, Filter, What-if Analysis tool
Build business simulation using Excel
Exercise-0
Basics of databases
ACID and BASE Model Properties of a Database
Working with SQL, Extract data from databases containing multiple tables
Performing Data Analysis using SQL
Exercise #1
Loading datasets and establishing table relationships
Work with different type of charts and dashboards
Working with Map visualizations
Working with power query for data manipulation
Writing DAX expressions
Exercise – 2
Python Basics Programming (Conditional, Looping, Functions)
Exercise-3
Python Numpy Arrays
Creating, Accessing, Manipulating Numpy Array
Numpy Data Types
Array Attributes
Data Operations
Common Arithmetic and Statistical Methods
Sort, Search, Count
Exercise-4
Creating, Accessing, Manipulating Pandas Data
Series and The Series and DataFrame
DataFrame Attributes & Basic Functions
Iteration on Data
Logical Indexing; Sorting & Reindexing
Merging, Joining & Concatenation of Data
Pandas File Handling
Grouping Data
Function Application
Missing Data & Treatment
Date & Time Functionality
Exercise – 4
Visualization Libraries in Python
MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc
Decorating the plots using Matplotlib
(labels, colors, markers, legend, grids, figure sizes etc)
The Subplots and axes in matplotlib
Showing Images
Exercise – 5
Easy and advanced Data Visualization from Seaborn
Categorical, Distributive, Regression, Matrix, Grid Plots
Customizing Color Themes
Exercise – 6
Univariate, Bi-variate and Multivariate analysis
Work with different type of tests like t-test, z-test, chi-square test, anova
Work with Missing values, outliers, data pre-processing
Exercise-7
Learn Important ML Basics Concepts (Train, Test, Validate, Bias , Variance,
Overfitting, Underfitting)
Work with Evaluation metrics (Classification and Regression both)
Work with different validation techniques
Perform data cleaning and Preprocessing
Linear Models, Decision Tree, k-NN
Math Behind each Machine Learning Algorithm
Building Classification and Regression Models
Hyperparameter Tuning to improve model
Exercise-10
Minor Project
Kernel Nearest Neighbors (KNN)
Support Vector Machines (SVMs)
The Hyperplane Concept
Trade-off between biases-variances
Exercise – 11
Minor Project
Introduction to unsupervised learning and clustering
Working of clustering algorithms (k-means clustering)
Evaluation metrics for unsupervised learning problems
Exercise-12
Minor Project
Introduction to Neural Networks
Activation Function, Optimizers, Loss Functions
Image Classification
Transfer Learning
RNN, LSTM, GRU
Text Classification
Exercise -13
Minor Project
Introduction with Flask
Deployment of Machine Learning Models over Cloud Services
QUERY SESSION
Capstone Project : Designing Custom Machine Learning Model
Development & Deployment
