Course Description
Data Science Certification from SGIT, Steinbeis University, Germany:
Accelerate your career with Data Science certification from SGIT, Steinbeis University Germany , one of the leading universities in Germany. This course is a perfect blend of theory, case studies and capstone projects. The course curriculum has been designed by Steinbeis University and considered to be the best in the industry. Get noticed by recruiters across the globe with the international certification. Post certification one will gain the alumnus status in Steinbeis University.
What is the certification process?
Post completion of the training, one should take an online examination facilitated by the university and should attain 60% or more to complete the course and gain the certification. Subsequently participants can check their alumnus status on SGIT , Steinbeis Global Institute Tübingen.
What is a Data Scientist? Or Rather Who is a Data Scientist?
Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data – incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive Modeling and finally Data Optimization. A Data Scientist does not provide a solution; they provide the most optimized solution out of the many available. Various research bodies including IBM say that the demand for Data Scientists and AI professionals will skyrocket to 700,000 by 2020. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant, you should not believe us. Go! research on your own and confirm the facts and figures.
Who Should Do The Course?
Professionals who can consider Business Analytics / Data Analytics Certification / Data Science Certificate Program Training as their next logical move to enhance their careers include:
- Professionals working on Business Intelligence and reporting tools
- Professionals working on Data Warehouse Technologies
- Statisticians, Economists, Mathematicians
- Software programmers (they have an edge in writing code to accomplish a prediction / classification / forecasting model)
- Business analysts (they have an edge in terms of industry / sector / domain experience)
- Six Sigma Consultants who already have exposure to Statistics
- Freshers (market demand is thriving organisations to hire the freshers trained on analytics)
How To Become A Data Scientist?
Accrue knowledge on dealing with data by getting trained and / or certified by any of the well-known institutes which have rich experience working closely with the ever-evolving industry. Knowing about Data Analytics tools or Data Mining software alone will not help you analyse data.
What Else Is Required?
You should possess Domain / Sector / Industry knowledge and learn relevant concepts to strike the right nail. A few such examples which are not limited to those mentioned are:
- One into web development might want to learn Web Analytics
- One into search engine optimisation might want to learn Social Media Analytics and Website Analytics
- One into sales and marketing might want to learn Marketing Analytics, Customer Analytics, Twitter Analytics, Facebook Analytics, Social Media Analytics and Data Collection Tools
- One into human resources might want to learn Workforce Analytics
- One into health care might want to learn Healthcare Data Analytics
Real-World Problems
Life Sciences And Health Care – Wearable Devices
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Many people across the globe are wondering on how to predict diseases in their initial stages and how changing lifestyle habits will cure the disease instead of medication. For this, people started wearing health bands, a few call them sports band which is used to track heartbeat rate, calories burnt, sleeping patterns, number of steps taken (walked) and many more. Jawbone is the most famous wristband and its users have helped generate about 130 million nights of sleep and experts call it the biggest sleep study on the planet. Also recorded are about 1.6 trillion steps and 180 million items of food.
You can tag the data to your personal doctor who will then monitor and inform you of what diseases you are likely to be infected with and what precautions should be taken to avoid it. Sounds WOW!
Retail – Location-Based Analytics
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Thousands of footfalls are witnessed in any known shopping mall across the globe. Can the store owners within the mall convert the footfalls into revenue? The Answer is ‘YES’! The moment a person connects to free wi-fi accessible in the mall, a unique MAC address is assigned to the person. From that point on, details such as time spent in a store, speed of movement (moving across the wifi zones / range), past buying behaviour, number of times an individual visited a store versus number of times purchase happened and different parameters are checked to send a personalised coupon which will lure the potential customer to become a source of revenue.
The number of coupons sent versus the number of purchases and the number of coupons sent versus the amount purchased is the key Business metrics captured and evaluated to enhance the prediction model. Amazing, isn’t it!
Why You Should Pursue A Career In Data Science / Business Analytics / Data Analytics?
You may question if Data Science Certification is worth it? The answer is yes. Data Science / Analytics is creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing historical data in order to make informed decisions and improve their businesses. Digitalization in all the walks of business is helping them to generate and analyse the data. This is helping to create many Data Science / Analytics job opportunities in this space. The void between the demand and supply for Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. A Data Scientist's career path is long and lucrative as the generation of online data is perpetual and growing in the future. ExcelR offers the best Data Science online certification training along with classroom and e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.
Data Scientist Training and Placement
Subsequent to the completion of our Data Science Certification program, assignments, projects and placement assistance will kick start. Help will be rendered in terms of resume building, FAQs for the interviews, one-to-one discussion on job description during interview calls, etc. A couple of mock interviews (one on one / telephonic) will be conducted by the SME's to evaluate the grey areas and areas of strength. This helps the participant to retrospect and understand their interview readiness. Participants can attend and successfully crack the interviews with complete confidence. ExcelR offers the best Data Science training and the reviews from our past participant's vouch for our statement.
Interview Preparation Sessions
Participants who have completed the Data Science course training and the projects will be put under our Placement Incubation Program. As part of this program, participants will undergo a thorough interview preparation process on Data Science. A huge repository of Data Science Interview questions with answers will be provided for the participants to prepare. A dedicated Data Science Subject Matter Expert (SME) will help in resume building, conduct mock interviews and evaluate each participant's knowledge, expertise and provide feedback. Our SMEs will do the necessary handholding on interview preparation process till the time the participant is placed. Guidance is also provided on Linkedin profile building and tricks of the trade to improve the marketability of the resume. - ExcelR Management
Course Curriculum
- Recap of Demo
- Introduction to Types of Analytics
- Project life cycle
- An introduction to our E learning platform
Description:Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.
Topics
- Data Types
- Measure Of central tendency
- Measures of Dispersion
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot
- R
- R Studio
- Descriptive Stats in R
- Python (Installation and basic commands) and Libraries
- Jupyter note book
- Set up Github
- Descriptive Stats in Python
- Pandas and Matplotlib / Seaborn
Topics
- Random Variable
- Probability
- Probility Distribution
- Normal Distribution
- SND
- Expected Value
- Sampling Funnel
- Sampling Variation
- CLT
- Confidence interval
- Assignments Session-1 (1 hr)
- Introduction to Hypothesis Testing
- Hypothesis Testing with examples
- 2 proportion test
- 2 sample t test
- Anova and Chisquare case studies
- Visualization
- Data Cleaning
- Imputation Techniques
- Scatter Plot
- Correlation analysis
- Transformations
- Normalization and Standardization
Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.
Topics
- Principles of Regression
- Introduction to Simple Linear Regression
- Multiple Linear Regression
Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.
Topics
- Multiple Logistic Regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1 score
- Receiver operating characteristics curve (ROC curve)
Description: Learn deployment using Rshiny and streamlit in R and python
Topics
- R shiny
- Streamlit
Description:As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.
Topics
- Supervised vs Unsupervised learning
- Data Mining Process
- Hierarchical Clustering / Agglomerative Clustering
- Measure of distance
- Numeric - Euclidean, Manhattan, Mahalanobis
- Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
- Mixed - Gower’s General Dissimilarity Coefficient
- Types of Linkages
- Single Linkage / Nearest Neighbour
- Complete Linkage / Farthest Neighbour
- Average Linkage
- Centroid Linkage
- Measure of distance
- Visualization of clustering algorithm using Dendrogram
K-Means
Description:In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.
Topics
- Non-Hierarchial
- Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
- Choosing the ideal K value using Scree plot / Elbow Curve
DBSCAN
Description:Introduction to Density based clustering method
Topics
- A geneal intuition for DBSCAN
- Different parameters in DBSCAN
- Metrics used to evaluate the performance of model
- Pro's and Con's of DBSCAN
Description:Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and tSNE
Topics
- PCA and tSNE
- Why dimension reduction
- Advantages of PCA
- Calculation of PCA weights
- 2D Visualization using Principal components
- Basics of Matrix algebra
Description:Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.
Topics
- What is Market Basket / Affinity Analysis
- Measure of association
- Support
- Confidence
- Lift Ratio
- Apriori Algorithm
Description:Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.
Topics
- User-based collaborative filtering
- Measure of distance / similarity between users
- Driver for recommendation
- Computation reduction techniques
- Search based methods / Item to item collaborative filtering
- Vulnerability of recommender systems
- Workflow from data to deployment
- Data nuances
- Mindsets of modelling
Description:Decision Tree and is one of the most powerful classifier algorithms today. Under this tutorial learn the math behind decision tree algorithm with a case study
Topics
- Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information Gain
- Implementation of Decision tree using C5.0 and Sklearn libraries
Description: Learn about how to handle categorical data using different methods
Topics
- Encoding Methods
- OHE
- Label Encoders
- Outlier detection-Isolation Fores
- Predictive power Score
Description: It helps in reducing overfitting , training time and it improves accuracy
Topics
- Recurcive Feature Elimination
- PCA
Description:Here you are going to learn what are they ways to improve the models interms of accuracy and reducing overfitting ( Bias vs Variance )
Topics
- Splitting data into train and test
- Methods of cross validation
- Accuracy methods
Description:Rather working on a single model we can work on a diverse set of models it can achieved by using Ensemble learning
Topics
- Bagging
- Boosting
- Random Forest
- XGBM
- LGBM
Description:KNN and SVM: KNN algorithm is by far one of the easiest algorithms to learn and interpret. SVM is another most popular algorithm best part is it can be used for both classification and regression purpose, learn these two by using simple case studies
Topics
- Deciding the K value
- Building a KNN model by splitting the data
- Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
- Kernel tricks
- Lasso Regression
- Ridge Regression
Description: Neural Networks: It is a supervised machine learning algorithm which mimics our human brain and it is foundation for Artificial Intelligence and Deep Learning. Here you learn the operation of neural networks using R and Python.
Topics
- Artificial Neural Network
- Biological Neuron vs Artificial Neuron
- ANN structure
- Activation function
- Network Topology
- Classification Hyperplanes
- Best fit “boundary”
- Gradient Descent
- Stochastic Gradient Descent Intro
- Back Propogation
- Intoduction to concepts of CNN
Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.
Topics
- Sources of data
- Bag of words
- Pre-processing, corpus Document-Term Matrix (DTM) and TDM
- Word Clouds
- Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
- Vector space Modelling
- Word embedding
- Document Similarity using Cosine similarity
Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.
Topics
- Extract Tweets from Twitter
- Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and
Topics
- Install Libraries from Shell
- Extraction and text analytics in Python
Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.
Topics
- Sentiment Extraction
- Lexicons and Emotion Mining
Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.
Topics
- Probability – Recap
- Bayes Rule
- Naive Bayes Classifier
- Text Classification using Naive Bayes
Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.
Topics
- Introduction to time series data
- Steps of forecasting
- Components of time series data
- Scatter plot and Time Plot
- Lag Plot
- ACF - Auto-Correlation Function / Correlogram
- Visualization principles
- Naive forecast methods
- Errors in forecast and its metrics
- Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
- Model-Based approaches
- AR (Auto-Regressive) model for errors
- Random walk
- ARMA (Auto-Regressive Moving Average), Order p and q
- ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
- Data-driven approach to forecasting
- Smoothing techniques
- Moving Average
- Simple Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
- De-seasoning and de-trending
- Forecasting using Python and R
- Concept with a business case
- End to End project Description with deployment using R and Python
Assignments/Projects/Placement Support
- Basic Statistics
- Data types Identification and probability
- Expected values, Measures of central tendencies
- Skewness and Kurtosis & Boxplot
- Practice Mean, Median, Varience, Standard Deviation and Graphical representations in R
- Creating Python Objects
- Practice Mean, Median, Varience, Standard Deviation and Graphical representations in Python
- Confidence intervals and distributions
- Hypothesis Testing
- Buyer ratio
- Customer Order Form
- Cutlets
- Pantaloons
- Lab TAT
- Linear regression
- Prediction of weight based on Calories consumed
- Delivery Time period Vs Sorting time
- Employee Churn rate Vs Salary
- Salary Prediction
- R shiny and Flask
- Practice R shiny and Python Flask for Linear Regression assignments
- Multiple Linear Regression
- 50 startups case study
- Computer data Case study
- Toyota Corolla
- Logistic Regression
- Term deposit case study
- Elections results Case study
- Multinomial Regression
- Student Program Case study
- Hierarchical Clustering
- Crime data
- Eastwest Airlines
- K means Clustering
- Insurance policy
- Crime data
- PCA
- Dimension Reduction for Wine data
- Network Analytics
- Node Properties practice in R
- Association Rules
- Association Rules for Book store
- Association Rules for Mobile store
- Association Rules for Retail Transactions
- Recommendation Engine
- Recommend Jokes for subscribers
- Text mining, Web Extraction
- Extraction of tweets from twitter
- Reviews from ecommerce websites
- Text mining
- Sentiment Analysis on extracted data
- NLP
- Emotion mining by extracting a speech or novel from web
- Naive Bayes
- Spam and Ham classifications
- KNN Classifier
- Types of Glass
- Classification of Animals
- Decision Tree and Random Forest
- Fraud Check
- Sales prediction of an Organization
- XGB and GLM
- Social Networks Ads
- Lasso and Ridge Regression
- Practice Lasso and Ridge with multiple Linear Assignments
- ANN
- Forest Fires case study
- SVM
- Classification of Alphabets
- Survival analysis
- Prediction of Patient survival probability
- Forecasting model based
- Airlines Forecasting
- Forecasting of sales for a soft drinks case study
- Forecasting
- Forecasting of Bike shares
- Forecasting of Solar power consumption
- Industry : Aviation
Predicting the flight delays
- How to determine which flights would be delayed and by how long?
- Industry : Manufacturing
Predict impurity in ore
- The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions
- Industry : Oil and Gas
Predicting the oil price
- Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013
- Industry : Automotive
Electric Motor Temperature
- Predict the temperature of rotor and stator of E-Motor
- Industry : Daily Analysis of a product
"Daily" Twitter Data Analysis for a Product
- Sentiment Emotion mining of twitter data of new product
- Industry : E commerce
Natural Language Processing
- Top 5 relevant answers to be retrived based on input question
- Resume Preparation
- Interview Support
Value added courses
- Introduction to Big Data
- Challenges in Big Data and Workarounds
- Introduction to Hadoop and its Components
- Hadoop components and Hands-on
- Understand the MapReduce (Distributed Computation Framework) and its Drawback
- Introduction to Spark
- Spark Components
- Spark MLlib and Hands-on (one ML model in spark)
Introduction to R Programming
- Introduction to R
- Data Types in R
How To Install R & R Studio
Data Structures in R
- Variable in R
- R-Overview
- Vector
- Matrix
- Array
- List
- Data-Frame
- Operators in R
- arithmetic
- Relational
- Logical
- Assignment
- Miscellaneous
- Conditiional Statement
- Decision Making
- IF Statement
- IF-Else Statement
- Nested IF-Else Statement
- Switch Statement
- Loops
- While Loop
- Repeat Loop
- For Loop
- Strings
- Functions
- User-defined Function
- Calling a Function
- Calling a Function without an Argument
- Calling a Function with an Argument
- Decision Making
Programming Statistical
- Box Plots
- Bar Charts
- Histogram
- Pareto Chart
- Pie Chart
- Line Chart
- Scatterplot
How to Import Dataset in R
- Read CSV Files
- Read Excel Files
- Read SAS Files
- Read STATA Files
- Read SPSS Files
- Read JSON Files
- Read Text Files
R-Packages
- DpLyr
- Hmisc or mise
- Ggplot2
- Caret
- Data Table
How to Integrate R and SQL
How to Get Data From SQL to R
Introduction
- Python Introduction - Programing Cycle of Python
- Python IDE and Jupyter notebook
Variables
- Variables
- Data type
Code Practice Platform
- create , insert , update and delete operation , Handling erros
Operators
- Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
- Decision making - Loops
Loops
- While loop, for loop and nested loop
- Number type conversion - int(), long(). Float ()
- Mathametical functions , Random function , Trigonometric function
Sting
- Strings- Escape char, String special Operator , String formatting Operator
- Build in string methods - center(), count()decode(), encode()
List
- Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
- Built in Function - cmp(), len(), min(), max(), list comprehension
Tuples
- Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
- Built in tuples functions - cmp(), len ()
Dictionary
- Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
- Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
- Date & time -Time Tuple , calendor module and time module
Function
- Function - Define function , Calling function
- pass by refernece as value , Function arguments , Anonymous functions , return statements
- Scope of variables - local & global , Decorators and recursion
- Map reduce and filter
Modules
- Import statemnts , Locating modules - current directory , Pythonpath
- Dir() function , global and location functions and reload () functions , Sys module and subprocess module
- Packages in Python
Files
- Files in Python- Reading keyboard input , input function
- Opening and closing files . Syntax and list of modes
- Files object attribute- open , close . Reading and writing files , file Position.
- Renaming and deleting files
- Pickle and Json
Directories
- mkdir methid, chdir () method , getcwd method , rm dir
Exception Handling
- Exception handling - List of exceptions - Try and exception
- Try- finally clause and user defined exceptions
OOP
- OOP concepts , class , objects , Inheritance
- Overriding methods like _init_, Overloading operators , Data hiding
Regular Expressions
- match function , search function , matching vs searching
- Regular exp modifiers and patterns
SQLite and My SQL
- Data base connectivity
- Methods- MySQL , oracle , how to install MYSQL , DB connection
- create , insert , update and delete operation , Handling erros
Framework
- Introduction to Django framwork , overview , environment
- Apps life cycle , creating views
- Application, Rest API
- Introduction to What is DataBase
- Difference between SQL and NOSQL DB
- How to Install MYSQL and Workbench
- Connecting to DB
- Creating to DB
- What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
- Select statement and using Queries for seeing your data
- Joining 2 tables
- Where clause usage
- Indexes and views
- Different operations in SQL
- How to Connect to your applications from MYSQL includes R and Python
What is Data Visualization?
- Why Visualization came into Picture?
- Importance of Visualizing Data
- Poor Visualizations Vs. Perfect Visualizations
- Principles of Visualizations
- Tufte’s Graphical Integrity Rule
- Tufte’s Principles for Analytical Design
- Visual Rhetoric
- Goal of Data Visualization
Tableau – Data Visualization Tool
- Introduction to Tableau
- What is Tableau? Different Products and their functioning
- Architecture Of Tableau
- Pivot Tables
- Split Tables
- Hiding
- Rename and Aliases
- Data Interpretation
Tableau User Interface
- Understanding about Data Types and Visual Cues
Basic Chart types
- Text Tables, Highlight Tables, Heat Map
- Pie Chart, Tree Chart
- Bar Charts, Circle Charts
Intermediate Chart
- Time Series Charts
- Time Series Hands-On
- Dual Lines
- Dual Combination
Advanced Charts
- Bullet Chart
- Scatter Plot
- Introduction to Correlation Analysis
- Introduction to Regression Analysis
- Trendlines
- Histograms
- Bin Sizes in Tableau
- Box Plot
- Pareto Chart
- Donut Chart, Word Cloud
- Forecasting ( Predictive Analysis)
Maps in Tableau
- Types of Maps in Tableau
- Polygon Maps
- Connecting with WMS Server
- Custom Geo coding
- Data Layers
- Radial & Lasso Selection
Adding Background Image
- How to get Background Image and highlight the data on it
- Creating Data Extracts
- Filters and their working at different levels
- Usage of Filters on at Extract and Data Source level
- Worksheet level filters
- Context, Dimension Measures Filter
Data Connectivity in-depth understanding
- Joins
- Unions
- Data Blending
- Cross Database Joins
- Sets
- Groups
- Parameters
Creating Calculated Fields
- Logical Functions
- Case-If Function
- ZN Function
- Else-If Function
- Ad-Hoc Calculations
- Quick Table Calculations
- Level of Detail (LoD)
- Fixed LoD
- Include LoD
- Exclude LoD
Responsive Tool Tips
- Dashboards
- Actions at Sheet level and Dashboard level
- Story
Connecting Tableau with Tableau Server
- Publishing our Workbooks in Tableau Server
- Publishing dataset on to Tableau Server
- Setting Permissions on Tableau Server
Connecting Tableau with R
- What is R?
- How to integrate Tableau with R?
- Tableau Prep
Introduction to Neural Network & Deep Learning
Topics
- Introduction
- Deep Learning Importance [Strength & Limitation]
- SP | MLP
- Neural Network Overview
- Neural Network Representation
- Activation Function
- Loss Function
- Importance of Non-linear Activation Function
- Gradient Descent for Neural Network
Parameter & Hyper parameter
Topics
- Train, Test & Validation Set
- Vanishing & Exploding Gradient
- Dropout
- Regularization
- Optimization algorithm
- Learning Rate
- Tuning
- Softmax
CNN
Topics
- CNN
- Deep Convolution Model
- Detection Algorithm
- Face Recognition
RNN
Topics
- RNN
- LSTM
- Bi Directional LSTM
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