Best Machine Learning Course in Mumbai - Online Training Classes Available
Learners 1000+
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Best Machine Learning Course in Mumbai to Learn from Best Machine Learning Training Institute and Classes in Online / Classroom Format in Reasonable Fees with Placement Support from Experts!
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About Data Science -
It is Umbrella term that covers everything like analysing data, data cleaning, data visualization etc. As name suggest it is science of analyzing data to extract meaningful information from it to help management take decisions for company. Machine Learning and Adv. Machine Learning or Aritificial Intelligence (A.I.) provides various Data Analysis Algorithms to solve Data Analysis projects. Finally data is represented graphically or summary data using visualization tools like Tableau, Power BI etc. Hence Data analytics or Data Visualzization comes in combination or required along with Data Science. Combination of both makes you a Data Scientist!
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About Our Data Science & Analytics Certification Course -
This course is targeted to both freshers and experienced who wish to become a Data Scientist! Course covers 10+ case studies across multiple industries to help you understand how to implement data science models for real time scenarios. In Analytics you will be learning both SQL and Analytics tool because data science output is represented Analytically using Analytical Tools like Tableau or Power BI. Hence the combination of Data Science and Analytics is most preferred in Industry with Excellent Pay Package!
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Trainer -
Data Science Expert
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What You Will Learn ?
Python, Python Analytics, SQL, Tableau, Statistics, Data Analysis, M.L. Algorithms, Neural Networks, A.I., Tensor Flow, Keras, Advanced Libraries, Image Processing, Text Analytics, NLP etc.
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Jobs Roles Targeted -
Data Scientist, Machine Learning Engineer, Data Engineer, Data Science & Analytics Engineer etc.
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Scope & Job Openings -
Highly Demanded
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Any Pre-requisites -
None. Any one from IT or non IT can learn. Students from any IT / Stats / Any Engg. Background Preferred.
Syllabus
Job Opportunity
Data Science & Analalytics Course
- Oracle 18/21c Installation
- Basics
- Oracle Basics
- Database models
- ER Model Overview
- Data types
- Understanding Test Database
- Basics Queries
- Arithmetic and String functions
- DML Operations - Insert, Update and Delete
- Writing Queries
- Filters
- DISTINCT
- BETWEEN
- IN
- LIKE
- NOT
- NULL
- AND, OR, NOT etc
- Using Complex Data Filtering Techniques
- Sorting
- Ascending Order
- Descending Order
- Complex Sorting
- Functions
- String Functions - lower case, uppercase, substring, instring, trim etc.
- Number Functions
- Date Manipulation
- Null Value Functions - NVL, NVL2, NULLIF, COALESCE
- Conditional Expressions - Case and Decode
- Groups
- Basics
- Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT etc
- Grouping Filters - HAVING
- Joins
- Cartesian Product
- Equi and Non-Equi Joins
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Self Join
- SET Operators
- Union
- Union All
- Intersect
- Minus
- Sub Query & Co-related Sub Query
- Single Row Sub Query
- Multi Row Sub Query
- Creating Views & Inline Views
- Creating Complex Queries
- Joins & Groups
- Joins & Inline Views Integration
- DDL
- Tables - Create, Alter, Drop
- Indexes - Types, Create, Drop
- Constraints - Not Null, Primary Key, Unique Key, Foreign Key
- Sequence
- Synonyms
- Foreign Key & its Effect on data
- DML operations - Insert, Update & Delete
- Insert, Update, Delete & Truncate
- Common Operations
- Creating Tables using queries
- Bulk Data Inserts using Queries
- Transactions ( T SQL)
- Commit
- Rollback
- DCL - Grant Revoke
- Project
- Installation and Configuration
- Introduction to Tableau
- Architecture
- Data Model
- Data Connection with single data sources
- Data Joins
- Working with multiple data sources
- Data Blending
- Difference between joining and blending data
- Tableau Interface
- Measures and dimension
- Shelves
- Show me
- Data Types
- Default Properties
- Marks Card
- Page Shelf
- Saving and sharing your work-overview
- Difference between .twb and .twbx
- Discrete vs Continuous
- Date Aggregations and Date parts
- Cross tab
- Totals & Subtotals
- Chart Types
- Bar Charts & Stacked Bars
- Pie Chart
- Line Graph with Date
- Line Graph without Date
- Area charts
- Tree Map
- Word Cloud
- Packed Bubble
- Box & Whiskers Plot
- Scatter Plots
- Highlight tables
- Heat maps
- Data Visualization
- Geographical fields
- Map options
- Run Time Columns
- Calculated Fields
- String Calculation
- Data Calculation
- Logical Calculation
- Numeric Calculation
- Parameters
- Table Calculations
- Level of detail
- Data Formatting
- Legends
- Highlights
- Labelling
- Number Formats
- Alignment
- Advanced Functionalities
- Sorting
- Groups
- Sets
- Hierarchy
- Reference and Trend Lines
- Edit Axis
- Bins
- Filters
- Dimension Filter
- Measure Filter
- Filter - Display Option
- Context Filters
- Relevant Filters
- Sets in filters
- Conditional Filters
- Advanced Charts
- Pareto
- Waterfall
- Funnel
- Waffel Chart
- Dashboards
- Dashboard Objects
- Dashboard Actions
- URL
- Highlight
- Filter
- Advanced Interactivity
- Publish to Web
- Case Studies / Project
- Data Visualization & Analytics
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- Data Sources
- from SQL (MySQL)
- from Flat Files (CSV / Excel etc.)
- from Python
- Introduction & Installation
- Introdution to Python & Python Analytics or Adv. Python
- Python / Anaconda Installations & Configuration
- Programming Basics
- Variables
- Data Types
- User Inputs
- Unpacking
- Boolean Practice
- What If, Else and If, Making Decisions
- Loops
- Functions
- Creating Functions
- Using Built in Functions
- OOPs Programming
- Modules
- Classes & Objects
- Inheritance
- Overloading
- Composition
- Data Structures
- List
- Tuple
- Set
- Exception Handling
- MySQL Server Setup
- Installing and Configuring MySQL Server
- Creating Database
- CRUD Operations
- Create Tables
- Insert
- Update
- Delete
- Database Integration with Python
- Connection to Database from Python
- Writing functions to handle database operations
- CRUD Operations in python
- Web Scrapping
- Getting Data from Any Website using Python (new)
- Data Analytics in Python
- Installing & Understanding Analytics Packages
- NumPy Array Operations
- Pandas Data Frame Operations
- Data Acquisition (Import & Export)
- Indexing
- Selection
- Sorting
- Filtering
- Group By
- Binning
- Concatenation
- Merge
- Append
- Drop
- Data Visualization in Python
- Installing Librarires
- Matplotlib
- Seaborn
- Charts & Plots
- Histogram
- Scatter Plots
- Box Plots
- Line Chart
- Bar Chart
- Pie Chart etc.
- Dashboards
- Project
- Installing Librarires
- Beautiful SOAP library
- Project
- Machine Learning Examples
- Introduction to Machine Learning & Data Analysis
- Machine Learning Examples
- Statistics & Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Descriptive Analytics
- Measure of Central Tendency
- Dispersion
- Probability Distribution
- Correlation & Covariance
- Inferential Statistics
- Diagnostic Analytics
- Outliers | Missing Values
- Sparsity
- Inferential Statistics
- Random Sampling
- Hypothesis Testing
- Supervised Learning
- Installing & Using Scikit-Learn Package
- Linear Regression
- Multi-Linear Regression
- Stepwise Regression
- Logistic Regression
- Sigmoid Function
- Entropy
- Information Gain
- Gini Index
- Decision Tree (CART)
- Ensemble Learning
- Random Forest
- xgBoost
- K Nearest Neighbors (KNN)
- K Selection
- Distance Metrics
- Support Vector Machine (SVM)
- Kernel Functions
- Naive Bayes Classifier (NBC)
- Perceptron Learning
- Multi Layer Perceptron (MLP)
- Unsupervised Learning
- Hierachical Clustering
- Dendograms
- K Means Clustering
- Association Rule Mining
- Recommendation Engine
- Model Improvement & Validation
- Regularization
- Lasso
- Ridge
- ElasticNet
- Cross Validation
- ROC Curve
- Confusion Matrix
- Overfitting
- Underfitting Problems
- Precision vs Recall
- F1 Score
- Type I vs Type II Error
- Ensemble Modeling (Bagging | Boosting | Stacking)
- Feature Selection
- Feature Extraction
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Multi class Classification
- Linear Discriminant Analysis (LDA)
- Scree Plot
- Elbow Method
- Time Series Forecasting
- Time Series Data
- Trend Chart
- Stationarity
- Seasonality
- Moving Average
- Exponential Smoothing
- ARIMA
- GARCH
- When to Use Which Algorithms!
- Projects / Case Studies
- 1) Real Estate Price Prediction
- 2) Fraud Detection Problem in BFSI domain
- 3) Disease Detection in Healthcare domain
- 4) Market Segmentation in Advertising Sector
- 5) Market Basket Analysis in Departmental Sector
- 6) Passenger Forecasting in Aviation Sector
- 7) News Classification in Media Sector
- 8) Dashboard (Visualization Tool)
- Please Note - Case studies / projects changes from time to time and will be covered along with their respective modules.
- End to End Data Analysis Project using Python, Tableau, SQL, M.L., Flat files etc.
- Intro to AI & Neural Networks
- Deep Learning vs Machine Learning
- Tech Advancement
- All about Artificial Neural Networks (ANN)
- Understand How Deep Neural Network Works?
- Different variants of Gradient Descent
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
- Batch Size
- Learning Rate
- Momentum
- Deep Learning in Python
- Deep Learning packages in python
- Google TensorFlow Framework
- Model Building with default TFLearn API
- Keras Vs TFLearn Vs Pycharm APIs
- Model Building with Keras API Wrapper
- Activations
- Optimizers
- Losses
- Validation
- Evaluation Metrics
- Keras Backend
- Callbacks - Early Stopping, TensorBoard
- CNN - Convolutional Neural Networks
- Understanding Architecture & Visualizing a CNN
- Kernel
- Depth
- Pooling
- RNN - Recurrent Neural Networks
- Recurrent Neural Network Model
- Training RNNs with Back-propagation
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Text Analytics / NLP - Natural Language Processing
- Understanding Text Mining & Analyti
- Tokenization
- Stop Word Removal
- Stemming
- Lexical Analysis
- Projects & Case Studies
- Image Recognition using MNIST Dataset
- Object Recognition using CIFAR-10 Dataset
- Speech Recognition (Google Voice | Alexa | Siri)
- Sentiment Analysis and Word Clouds
- Chat Bot Building
- Please Note - Case studies / projects changes from time to time and will be covered along with their respective modules.
Syllabus
Tools You Will Learn
Why get Trained from us ?
Training From Experts
Personal Attention
80% Practical
Study Material
Course Completion Certificate
Live Projects /
Case Studies
Interview Preparation
Best Placement Service
FAQs
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How Old is QuickXpert Infotech?
We are an ISO Certified Institute and into IT Training and Placements since 2014 or from 9+ yrs. We provide both classroom (Thane) and Live Online Trainings and have trained Indian and International students from countries like US, UK, Australia etc.
Our Benefits:
✓ Training from Experts
✓ 100% Job Oriented Training Programs
✓ 100% Placement Support - Best placement service - Overall 1000+ companies for placements. Placement support will be provided until you get job until 1 year. Many got jobs while course was ongoing or just within 2 months of completion of course. 1 of our recent placed student at fresher level got 4.5 LPA package and many got around 3.2 LPA package. You could be our next Placed Student!
✓ 1000s Trained
✓ Reasonable Fees
✓ Free Demo!
Inquire Now
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Is FREE Demo available?
Yes. Both in Live Online and Classroom mode of training.
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How Online Trainings Conducted?
Trainers will be live and not recorded ones. Live Lectures conducted on Zoom, Google Meet, Gotomeet etc.
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Upcoming Batch?
New batches at regular intervals. Short sized batches for individual attention. Limited seats per batch. For more info submit inquiry form.
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Do You Provide Corporate Trainings?
Yes. We have provided corporate trainings in NMIMS, Infogix, GeBBS Healthcare solutions, PDG Softwares etc. Please submit inquiry form or call us to convey your requirements.
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Group Discounts
Yes - Please submit inquiry form for details.
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Can i bring my laptop for study?
Yes.
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Do You Provide Computers / Laptops for Practice?
Yes. We have 1 student 1 laptop policy for all our trainings in our labs for practice.
Our Training Process - We keep both lectures and practicals on same day. 1 to 1 Live doubt solving with personal attention. -
More Queries?
Please visit home page and also submit inquriy form on this page. Our counsellors will get back to you shortly during working hours.
