Best Machine Learning Certification Course in Mumbai
Learners 1000+
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Best Machine Learning Course in Mumbai to Learn in Online / Classroom Format from Best Machine Learning Training Institute QuickXpert Infotech known for Quality Training in Reasonable Fees and Placements!
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About Our Data Science & Analytics Certification Course with Real Time Projects -
Learn from basics of Python to advanced A.I. and Analytics modules on Real Time Projects under or Experts. Can be learnt by both freshers and experienced to become a Data Science Professional
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What You Will Learn ?
Python, Python Analytics, Statistics, Data Analysis, M.L. Algorithms, Neural Networks, A.I., Deep Learning, Tensor Flow, Keras, Advanced Libraries, Image Processing, NLP, SQL, Power BI etc. with projects
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Jobs Roles Targeted -
Data Scientist, Machine Learning Engineer, Data Analyst etc.
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Any Pre-requisites -
None. Any one from IT or non IT can learn.
Syllabus
Syllabus
Data Science & Analalytics Course
- 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
- Decision Tree (CART)
- Ensemble Learning
- Random Forest
- xgBoost
- K Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Kernel Functions
- Naive Bayes Classifier (NBC) / MultinomialNB Algorithm
- GRID SEARCH CV AND RANDOM SEARCG CV
- Linear Discriminant Analysis (LDA)
- Unsupervised Learning
- Hierachical Clustering
- K Means Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Time Series Forecasting
- Time Series Data
- Trend Chart
- Stationarity
- Seasonality
- Moving Average
- Exponential Smoothing
- ARIMA
- GARCH
- Model Improvement & Validation
- Regularization
- Lasso
- Ridge
- ElasticNet
- Cross Validation
- Methods
- Sigmoid Function
- Entropy
- Information Gain
- Gini Index
- Feature Selection
- Ensemble Modeling (Bagging | Boosting | Stacking)
- K Selection / Distance Metrics
- Kernel Functions
- Elbow Method
- Metrics
- MAE / MSE/ RMSE / R2 and Adjusted R2
- AUC ROC CURVE
- Precision
- Recall
- F1 score
- Confusion Metrics
- Only Theory
- SMOTE and near miss
- EXTRA TREE CLASSIFIER (Random forest)
- TPOT
- LOG LOSS
- CROSS VALIDATION
- KERNAL PCA
- LIGHT GBM and cat boosting
- DBSCAN
- MINI BATCH K MEANS
- Passive Aggressive Classifier Algorithm
- Facebook Prophet
- time series RNN LSTM
- 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.
- 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
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
- Batch Size
- Learning Rate
- Momentum
- Transfer Learning
- vgg16
- Activations
- Optimizers
- Losses
- Validation
- Evaluation Metrics
- Keras Backend
- Callbacks - Early Stopping, TensorBoard
- vgg19
- Kernel
- Depth
- Pooling
- resnrt 50
- NLTK
- Simple NLTK
- Stemming
- Lemitaization
- Regex
- Stopwords
- Corpus
- Unigram
- Bigram
- Trigram
- BAG fo words(count vectorization)
- TD-IDF-term frequency inverse document frequency 1
- Word embedding
- word 2 vec --cbow / skipgram
- glove
- Fasttext
- Keyed vector
- LSTM / RNN (bidirentional)
- GRU(theory - Arthiecture build )
- 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.
- 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
- FETCH command - Top N, Top % Rows
- 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
- Rollups & Cubes
- Analytic (Window) Functions
- Over() with summary functions
- Partition By Queries
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
- Joins
- Cartesian Product
- Equi and Non-Equi Joins
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Self Join
- Complex Queries - Joins & Groups Integration
- SET Operators
- Union
- Union All
- Intersect
- Minus
- Subquery
- Single Valued Row Sub Query
- Multi Valued Sub Query
- Views
- Inline Views
- ROWID & ROWNUM
- TOP N Analysis or Complex Inline View
- DDL
- Tables - Create, Alter, Drop
- Indexes - Types, Create, Drop
- Constraints - Not Null, Primary Key, Unique Key, Foreign Key
- Sequence
- Synonyms
- Foreign Keys
- 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
- Savepoint
- DCL - Grant & Revoke
- Project
- Introduction to Power BI
- What is Power BI and Why Power BI
- Installing Power BI Desktop
- Exploring the Power BI Workflow
- Adjusting the settings of the Power BI Desktop
- Comparison of Power BI vs Other Reporting Tools
- Getting and Transforming Data with Power BI Desktop
- Connecting to different sources
- Connecting to multiple sources
- Different connecting Options(DirectQuery vs Import Data Vs Live Connection)
- Shaping and transforming data with Power Query
- Editing,Merging,Appending queries etc
- Advance Importing techniques without coding
- Data cleaning & Advance data cleaning
- Modeling with Power BI
- Introduction to Modeling
- Building Relational Models(Setup and Manage Relationships)
- Creating table relationships
- Understanding the filter flow
- Cardinality and Cross filtering
- DAX
- Understanding Dax Syntax
- Calculated Columns vs Measures
- Common Dax functions and formulae
- Understanding the evaluation context in DAX
- Visualising Data with Reports
- Creating Visualisations
- Color & Conditional Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips
- Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Hierarchies
- Constant Lines
- Tables, Matrices & Table Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualisations
- Custom Visuals
- Managing and Arranging
- Drillthrough
- Custom Report Themes
- Grouping and Binning
- Bookmarks & Buttons
- Introduction to Power BI Service
- Introduction to the Power BI Service
- Quick Tour of Power BI Service
- Connecting to Data from Power BI service
- Building Blocks of Power BI Service
- Sharing and Collaboration Tools in Power BI Service
- Sharing and Collaboration Options Overview
- Publish from Power BI Desktop
- Publish Reports to Web
- Printing and Exporting from Power BI Service
- Sharing Reports & Dashboards
- Workspaces (My Workspace vs App Workspace)
- Publishing Apps in Power BI Service
- Row Level Security in Power BI Desktop and Service
- Power BI Gateway and Refreshing Datasets
- Understanding Gateways in Power BI
- Difference between Personal and On-Premise Gateway
- Installation of Personal Gateway
- Installation of On Premise Enterprise Gateway
- Setting Up a Gateway in Power BI Service
- Understanding Data Refresh(Manual vs Scheduled Refresh)
- Troubleshooting Refreshing scenarios
- A.I. in Power BI
- Advance Report techniques
- Best Practices
- Pkg1 - Data Science - 3.5 to 4 months
- Pkg2 - Data Science and Analytics - 6 months
- Customized training available
- Pkg1 - Data Science - ₹38000(Classroom) / ₹35000(Online)
- Pkg2 - Data Science & Analytics - ₹53000(Classroom) / ₹48000(Online)
- customized training available
- can pay in installments
- Upcoming Batches
Tools You Will Learn
Why get Trained from us ?
9+ yrs in IT Training
Get Trained from Experts
100% Job Oriented Programs
80% Practical
Study Material / Notes
Course Completion Certificate
Live Projects / Case Studies
Interview Preparation
Best Placement Service
1000+ Companies
Personal Attention
Short sized batches (generally 5 to 8)
Weekdays/Weekend Batches available - Upcoming Batch
Data Science Projects
Few of the projects covered in one of our batches
