"Master AI with TopMentor: Learn from industry leaders in our cutting-edge mentorship program. Designed for beginners, our curriculum covers AI essentials, ML, Python, Deep Learning, and more. Gain hands-on experience and unleash your potential in real-world applications. Transform data into insights with confidence. Join the best Artificial Intelligence Course in India now!"
For any queries or concerns about our courses, contact us today.
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At TopMentor, we believe training is just the beginning β your success is our real goal. Thatβs why our dedicated placement support extends far beyond the classroom. We offer lifetime career assistance, including expertly crafted ATS-friendly resumes, mock interviews tailored to your target roles, and one-on-one prep with industry veterans. Youβll also get access to guaranteed interview calls through our hiring partners, plus curated job opportunities aligned with your skills. With TopMentor by your side, youβre never navigating your job search alone β weβre with you every step of the way until you secure your dream job, and long after.
'Top Mentor' offers the best AI course with highest placement rates resulting in multiple awards.
At TopMentor We blend cutting-edge tech with real world mentorship to future-proof your career.
Here, most of the mentors come from reputed institutions like IITs, MITs of the world & have huge experience in the field!
Mentors will personally guide you, help you find jobs ensuring your success in AI Career.
Syllabus is designed by top AI Engineers in India keeping best interest of students. It focuses on delivering results and jobs to students.
We have tie-ups wIth HRs & placement agencies. So you will get 100% job assistance after the end of the course.
Self-paced learning allows you to study anytime, anywhere, at your own speedβmaking it easier to balance upskilling with work, college, or personal commitments.
Lifetime LMS access ensures you can revisit course materials, updates, and resources anytime in the futureβhelping you refresh your skills whenever needed.
Practical Project based learning can often lead to a deeper understanding of a concept through the act of personal experience.
One-to-one mentorship with mentor brings 2X faster doubt clearing. This setup allows you to build relationship with mentor, ask doubts on the go whenever you need to resolve them.
TopMentor takes care of entire 360 degree placement processes, offers 1200+ hiring partners, professional resume building, linkedin profile building, portfolio building, interview prep etc.
TopMentor co-founders had huge education loans when they started their careers. So when they founded the company, they were determined to keep the costs low and affordable to anyone willing to change his/her career.
Artificial Intelligence is expected to create 2.3 Million jobs by 2026. By 2030, Ai would contribute upto $15.7 trillion to global economy.
AI involves solving complex problems making it a valuable skill both professionally and in everyday life. The ability to analyze data and draw conclusions is highly sought after.
AI professionals are in high demand, with a looming skill shortage predicted as more industries embrace big data. This trend translates into higher salaries and numerous job opportunities for graduates with analytics skills.
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The world of AI is currently experiencing a significant boom, driven by the abundance of available data. As data becomes more valuable, the demand for AI professionals will increase, offering better job prospects and career advancement.
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This part of AI training covers overview of artificial intelligence. In addition, terminologies & applications within AI along with 3 exercises
Donβt know python? No worries β we got your back. We will teach right from basics of python so even if you donβt know python, you would still be able to make it.
Our instructor is a master statistician & will help you easily understand and master statistics even though you are not good with maths.
Training covers detailed practical process & execution. Master logical regressions, modeling. .Master Principles of Predictive analytics.
Learn to create machine learning algorithms in python. Master making robust machine learning models & using them to solve any complex problems.
Dive deep and practice real time on engaging visuals & capstone projects for your portfolio. Perfect for people without any prior oop knowledge.
Master various features of tableau. Create & design the visualizations for your audience. Learn to combine the data & practices to present your story
PowerBi is crucial for career because it equips learners with valuable data visualization skills which are highly sought after by companies
If you wanna begin your deep learning journey then this course is great for you. It is designed in easiest way such that you donβt get bogged down unnecessarily.
Best way to learn SQL is by practicing it. Install free open source database & start writing and running simple queries using data. MYSQL is a free popular database that is compatible with most operating systems.
Jira is most widely used bug tracking and project management tool. If you follow any development methodology you can easily start using Jira in your project as it provides best customization possibilities.
possibilities statistics can facilitate decision making and performance reviews for business. From statistics business can understand how customers behave and react to its offerings. Business can improve accordingly.
Github emphasizes iterative development and flexibility to adapt to changing requirements, while scrum provides a framework for managing complext projects
Github can easily be used as a collaboration platform among coders and can be used to build complex systems. As a beginner you should learn programing syntax first.
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a. Introduction/Definition/overview of Data Analytics
b. Types of Analytics
c. Scope and Role of Analytics in Business
d. Fundamentals of Data
a. Sum-if & ifs
b. Average-if & ifs
c. Count-if & ifs
d. Lookup Function - VLOOKUP, HLOOKUP, XLOOKUP
e. Excel Shortcuts
a. Sum-if & ifs
b. Average-if & ifs
c. Count-if & ifs
d. Lookup Function - VLOOKUP, HLOOKUP, XLOOKUP
e. Excel Shortcuts
a. Data Analysis
b. Correlation
c. Regression
d. Co-variance
e. Moving Average
f. Descriptive Stats
g. What-If Analysis
h. Goal Seek
I. Scenario Manager
j. Pivot Query
k. Loading Data
L. Transformations
m. Live Data Connection and Analysis.
a. Overview of Data
b. Types and Forms of Data
c. Applications and Examples of Data
d. Overview of Statistics
e. Types of Statistics - Applications and Examples
a. Overview of Descriptive Statistics
b. Functions in Descriptive Statistics - Applications and Its Mathematical Explanation
Β Β i. Mean
Β Β ii. Median
Β Β iii. Mode
c. Standard Deviation
d. Skewness
e. Kurtosis
f. Range
g. Variance
h. Co-efficient Of Variation
I. Correlation
Β Β i. Pearson
Β Β ii. Spearman
j. Co-variance
k. Squared Error
L. Mean Squared Error, RMSE, MAD
m. Hands On Python/Excel
a. Overview of Inferential Statistics
b. Population and Sample
c. Test in Inferential Statistics
d. T-test
e. Chi-square
f. Anova
g. P-value
h. Tables and Critical value
I. Confidence Interval
j. Hands on in Python/Excel
a. What is Probability Distribution?
b. Examples and Applications of Probability Distribution
c. What are Normal Distributions?
d. What are Uniform Distributions?
e. What are Skewed Distributions?
f. What is a z-score?
g. Hands on in Python/Excel
a. Why we need Programming in Data Science
b. In which language can we do Programming?
c. Overview of Programming
d. What is Compiler and Interpreter?
a. Python Overview
b. Python Installations
c. Setting paths in Python
d. Coding with IDLE
e. Installing Editor - PyCharm/VS-Code
f. Setting interpreter
a. What is Anaconda?
b. Installations
c. Keywords and Identifiers in Python
d. Comments, Indentations and Statements
a. Integers
b. Strings
c. Float
d. Boolean
e. Bytes
a. Arithmetic Operators
b. Logical Operators
c. Membership operators
d. Equality Operators
e. Comparison Operators
a. If
b. If elseif
c. Loops -
d. For Loop
f. While Loop
g. Transfer Statements
h. Break
I. Continue.
j. Pass
a. Lists
b. Tuple
c. Sets
d. Dictionary
e. Range
a. Python Inbuilt Functions
b. Lambda Functions
c. UsΡr DΡfinΡd Functions
d. Arguments in Functions
e. Modules in Python-
f. Math Module
g. Random Module
h. Exception Handling in Python
a. Overview of Pandas
b. Pandas Functions
c. Forms in Pandas
d. Data Frame Basics
e. Key Operations in Data Frame and Series
f. Data Analysis using Pandas
a. Introduction to NumPy
b. Introduction to Array
c. Numerical operations
a. Data Cleaning
b. Missing Values Handling
c. Handling categorical and Numerical Features
d. Outlier Detection and Imputation
e. Exploratory Data Analysis (EDA)
f. Decide Suitable Algorithms
g. Standard Scaler, Normalization, transformations
a. Introduction to matplotlib
b. Introduction to Seaborn
c. Introduction to Iris dataset and 2D scatterplot.
d. 3D scatterplot.
e. Pair plots.
f. Limitations for Pair plots.
g. Histogram and introduction to PDF (Probability Density Function).
h. Univariate analysis using PDF.
I. CDF (Cumulative Distribution Function)
j. Variance, Standard Deviation.
k. Median.
L. Percentiles and Quartiles.
m. IQR (IntΡrquartilΡ RangΡ) Boxplot with whiskΡrs
n. Violin plots.
Β Β i. Heatmaps and Correlations Plots
Β Β ii. Regression Plots
Β Β iii. Line Plots
Β Β iv. Pie Charts and Donut Charts in Python
a. Introduction to databases.
b. Why SQL?
c. Installing MySQL.
d. Load Data.
e. Use, Describe, Show Table.
f. Select
g. Order By, Distinct.
h. Where Clause, Comparison Operators, NULL.
I. Logic Operators
j. Aggregate Functions: COUNT, MIN, MAX, AVG, SU
k. Group By.
L. Join and Natural Join.
m. Subqueries/Nested Queries/Inner Queries.
n. DML: INSERT.
o. DML: UPDATE, DELETE.
p. DML: CREATE, TABLE.
q. DDL: DROP TABLE, TRUNCATE, DELETE.
r. Normalisation
s. Dimensional Modelling
t. Window Functions
Β Β i. SUM
Β Β ii. MAX
Β Β iii. Average
Β Β iv. Rank
Β Β v. Partition
Β Β vi. Order by
u. Views, Stored Procedures and Tables in MYSQL
v. Data Analysis and Data Wrangling In MYSQL
w. Temporary table creation
x. Database Administration
a. Introduction to Power BI - Need, Importance
b. Power BI - Advantages and Scalable Options
c. History - Power View, Power Query, Power Pivot
d. Power BI Architecture and Data Access
e. Power BI Desktop - Installation, Usage
f. Sample Reports and Visualization Controls
g. Understanding Desktop & Mobile Editions
h. Report Rendering Options and End User Access
a. Report Design with Legacy & .DAT Files
b. Report Design with Database Tables
c. Understanding Power BI Report Designer
d. Report Canvas, Report Pages: Creation, Renames
e. Report Visuals, Fields and UI Options
f. Experimenting Visual Interactions, Advantages
g. Reports with Multiple Pages and Advantages
h. Pages with Multiple Visualizations. Data Access
I. "GET DATA" Options and Report Fields, Filters
j. Report View Options: Full, Fit Page, Width Scale
k. Report Design using Databases & Queries
L. Query Settings and Data Preloads
m. Navigation Options and Report Refresh
n. Stacked bar chart, Stacked column chart
o. Clustered bar chart, Clustered column chart
p. Adding Report Titles. Report Format Options
q. Focus Mode, Explore and Export Settings
a. Power BI Design: Canvas, Visualizations and Fields
b. Import Data Options with Power BI Model, Advantages
c. DirΡct Query Options and Real-time (LIVE) Data Access
d. Data Fields and Filters with Visualizations
e. Visualization Filters, Page Filters, Report Filters
f. Conditional Filters and Clearing. Testing Sets
g. Creating Customised Tables with Power BI Editor
h. General Properties, Sizing, Dimensions, and Positions
I. Alternate Text and Tiles. Header (Column, Row) Properties
j. Grid Properties (Vertical, Horizontal) and Styles
k. Table Styles & Alternate Row Colors - Static, Dynamic
L. Sparse, Flashy Rows, Condensed Table Reports. Focus Mode
m. Totals Computations, Background. Borders Properties
n. Column Headers, Column Formatting, Value Properties
o. Conditional Formatting Options - Colour Scale
p. Page Level Filters and Report Level Filters
q. Visual-Level Filters and Format Options
r. Report Fields, Formats and Analytics
s. Page-Level Filters and Column Formatting, Filters
t. Background Properties, Borders and Lock Aspect
a. Chart report types and properties
b. Stacked bar chart, stacked column chart
c. Clustered bar chart, clustered column chart
d. 100% stacked bar chart, 100% stacked column chart
e. Line charts, area charts, stacked area charts
f. Line and stacked row charts
g. Line and stacked column charts
h. Waterfall chart, scatter chart, pie chart
I. Field Properties: Axis, Legend, Value, Tooltip
j. Field Properties: Colour Saturation, Filter Types
k. Formats: Legend, Axis, Data Labels, Plot Area
L. Data Labels: Visibility, Colour and Display Units
m. Data Labels: Precision, Position, Text Options
n. Analytics: Constant Line, Position, Labels
o. Working with Waterfall Charts and Default Values
p. Modifying Legends and Visual Filters - Options
q. Map Reports: Working with Map Reports
r. Hierarchies: Grouping Multiple Report Fields
s. Hierarchy Levels and Usages in Visualizations
t. Preordered Attribute Collection - Advantages
u. Using Field Hierarchies with Chart Reports
v. Direct Import and In-memory Loads, Advantages
a. Hierarchies and Drilldown Options
b. Hierarchy Levels and Drill Modes - Usage
c. Drill-thru Options with Tree Map and Pie Chart
d. Higher Levels and Next Level Navigation Options
e. Aggregates with Bottom/Up Navigations. Rules
f. Multi Field Aggregations and Hierarchies in Power BI
g. DRILLDOWN, SHOWNEXTLEVEL, EXPANDTONEXTLEVEL
h. SEE DATA and SEE RECORDS Options. Differences
I. Toggle Options with Tabular Data. Filters
j. Drilldown Buttons and Mouse Hover Options @ Visuals
k. Dependant Aggregations, Independent Aggregations
L. Automated Records Selection with Tabular Data
m. Report Parameters: Creation and Data Type
n. Available Values and Default values. Member Values
o. Parameters for Column Data and Table / Query Filters
p. Parameters Creation - Query Mode, UI Option
q. Linking Parameters to Query Columns - Options
r. Edit Query Options and Parameter Manage Entries
s. Connection Parameters and Dynamic Data Sources
a. Understanding Power Query Editor - Options
b. Power BI Interface and Query / Dataset Edits
c. Working with Empty Tables and Load / Edits
d. Empty Table Names and Header Row Promotions
e. Undo Headers Options. Blank Columns Detection
f. Data Imports and Query Marking in Query Editor
g. JSON Files & Binary Formats with Power Query
a. PurposΡ of Data Analysis Expressions (DAX)
b. Scope of Usage with DAX. Usability Options
c. DAX Context: Row Context and Filter Context
d. DAX Entities: Calculated Columns and Measures
e. DAX Data Types: Numeric, Boolean, Variant, Currency
f. Datetime Data Tye with DAX. Comparison with Excel
g. DAX Operators & Symbols. Usage. Operator Priority
h. Parenthesis, Comparison, Arithmetic, Text, Logic
I. DAX Functions and Types: Table Valued Functions
j. Filter, Aggregation and Time Intelligence Functions
k. Information Functions, Logical, Parent-Child Functions
L. Statistical and Text Functions. Formulas and Queries
m. Syntax Requirements with DAX. Differences with Excel
n. Naming Conventions and DAX Format Representation
o. Working with Special Characters in Table Names
p. Attribute / Column Scope with DAX - Examples
q. Measure / Column Scope with DAX β Examples
a. YTD, QTD, MTD Calculations with DAX
b. DAX Calculations and Measures
c. Using TOPN, RANKX, RANK.EQ
d. Computations using STDEV & VAR
e. SAMPLE Function, COUNTALL, ISERROR
f. ISTEXT, DATEFORMAT, TIMEFORMAT
g. Time Intelligence Functions with DAX
h. Data Analysis Expressions and Functions
i. DATESYTD, DATESQTD, DATESMTD
j. ENDOFYEAR, ENDOFQUARTER,ENDOFMONTH
k. FIRSTDATE, LASTDATE, DATESBETWEEN
l. CLOSINGBALANCEYEAR,CLOSINGBALANCEQTR
m. SAMEPERIOD and PREVIOUSMONTH,QUARTER
n. IF..ELSEIF.. Conditions with DAX
o. Slicing and Dicing Options with Columns, Measures
p. DAX for Query Extraction, Data Mashup Operations
q. Calculated Columns and Calculated Measures with DAX
a. PowerBI Report Validation and Publish
b. Understanding PowerBI Cloud Architecture
c. Data Refresh with Power BI Architecture
d. PBIX and PBIT Files with Power BI - Usage
e. Visual Data Imports and Visual Schemas
a. Relationships
b. Joins and Cardinality
c. Snowο¬ake Schema
d. Star Schema in Power BI
a. Tokenization
b. Data Cleaning β Trimming, Case Conversion, Imputations, Removing.
a. What are LLMs?
b. Evolution of Natural Language Processing
c. How does an LLM outputs a word?
d. Next Word Prediction
e. Training and using LLMs
f. Ways to use LLMs: - Text Response and Embeddings
g. LLM Embeddings
h. Sentiment Analysis with LLMs
I. Serendipity in LLMs
j. How are LLMs revolutionizing industries today?
a. Study of GPT architecture and variants.
b. Applications of GPT models in text generation and dialogue systems.
c. Case study-based implementation of GPT-based tasks. GPT-based
chatbot enhances E-Shop's customer support service or any other use case.
a. Introduction to Vector Databases
b. Architecture of Vector Databases
c. Indexing Techniques
d. Distance Metrics and Similarity Measures
e. Nearest Neighbour Search
f. Open-Source Vector Databases: - Chroma and Milvus
a. Attention Mechanism
b. Transformer Architecture and components
c. GPT Architecture
d. GPT Training Process: - Pre-Training and Fine-Tuning
e. Building your own GPT from scratch using Open-Source APIβs
a. Art of Prompt Engineering
a. Fine Tuning
b. Prompt Engineering
c. Text Summarizations.
d. Text Generation
a. Data Science and It's Concept
b. Scope of Data Science
c. Data Engineering
d. Data Stories
e. Business Intelligence and It's Concepts
f. Application of Bi in The Real World
g. Basics of Artificial Intelligence
h. Applications of Al in The Real World
a. DΡfinition/Overview
b. Importance of Data Science
c. Role and Scope of Data Science
d. History and Future of Data Science
E. Benefits and Challenges of Data Science
f. Life Cycle of Data Science
g. Salaries of Data Science Roles
a. Definition/Overview
b. Concept and Tools Of B!?
c. Life Cycle of Business Intelligence
a. Definition/overview
b. Examples
c. Applications of Data Mining
a. What is ML?
b. Different types of ML?
c. Life Cycle of ML?
d. Challenges of ML?
a. Linear Regression Analysis
Β Β i. Geometric intuition of Linear Regression
Β Β ii. Geometric intuition of Multiple Linear Regression
Β Β iii. Lasso and Ridge Regression iv. Polynomial Regression
Β Β iv. Diagnostics of Regression
Β Β v. Rsquare
Β vi. RMSE, MSE
Β vii. Sampling
Β Β Β β’ Train-Test Split
Β Β Β β’ StratifiΡd Sampling
Β Β Β β’ Cross validation
a. Clustering-
Β Β i. Overview of Clustering
Β Β ii. Applications of Clustering
Β Β iii. K-Means Clustering Analysis
Β Β Β β’ K-Means: Geometric Intuition,
Β Β Β β’ Centroids
Β Β Β β’ K-Means: Mathematical formulation: objective function
Β Β Β β’ K-Means Algorithm.
Β Β Β β’ Failure cases/ Limitations.
Β Β Β β’ Determining the right K-Elbow method.
Β Β iv. Hierarchical Clustering Analysis
Β Β Β β’ Agglomerative & Divisive, Dendrograms.
Β Β Β β’ Agglomerative clustering.
Β Β Β β’ Proximity methods: Advantages and Limitations.
a. What is NLP?
b. What is Text Mining?
c. Text Mining Process
d. Punctuation Remover
e. Deletion, Cleaner
f. Stop Words Remover
g. Lemmatize
h. Stemmer
I. POS tagging
j. Tokenization
k. Word Cloud
L. Sentiment Detection
a. What is Deep Learning?
b. Scope and Challenges
c. Life Cycle
d. Epochs
e. Real-Life use cases of Deep Learning
f. Single Layer Neural Network
g. Multi-Layer Neural Network
h. Important Python packages for Deep Learning
I. Activations Functions
ο· What is Cloud Computing? Why it matters?
ο· Traditional IT Infrastructure vs. Cloud Infrastructure
ο· Cloud Companies (Microsoft Azure, GCP, AWS) & amp; their Cloud Services
(Compute, storage, nΡtworking, apps, cognitive Ρetc.)
ο· Use Cases of Cloud computing
ο· Overview of Cloud Segments: IaaS, PaaS, SaaS
ο· Overview of Cloud Deployment Models
ο· Overview of Cloud Security
ο· AWS vs. Azure vs. GCP
β’ Introduction to Artificial Intelligence (AI)
β’ Modern era of AI
β’ Role of Machine learning & Deep Learning in AI
β’ Hardware for AI (CPU vs. GPU vs. FPGA)
β’ Software Frameworks for AI & Deep Learning
β’ Key Industry applications of AI
β’ Key Industry applications of AI
β’ Overview of activation functions, hidden layers, hidden units
β’ Illustrate & Training a Perceptron
β’ Important Parameters of Perceptron
β’ Understand limitations of A Single Layer Perceptron
β’ Illustrate Multi-Layer Perceptron
β’ Understand Backpropagation β Using Example
β’ Implementation of ANN in Python- Keras
β’ Activation Function Introduction
β’ Sigmoid Activation Function
β’ tanh Activation Function
β’ Vanishing gradients with RNNs
β’ SoftMax activation function
β’ SoftMax activation function - Detailed
β’ Gradient descent
β’ Learning rate
β’ SGD
β’ Introduction To Optimizers
β’ Adagrad optimizer
β’ Adadelta and RMSprop Optimizer
β’ Adam Optimizer
β’ Maximum Likelihood
β’ Cross-Entropy
β’ Mean Squared Error Loss
β’ Cross-Entropy Loss (or Log Loss)
β’ Reading Image Part-1
β’ Reading Image Part-2
β’ Render Image
β’ Render Multiple Image
β’ Writing Image
β’ Drawing Shapes
β’ Mouse Clicks
β’ Color Trackbar
β’ Arithmetic Operation
β’ Image Blending
β’ Color Spaces
β’ Bitwise Operators
β’ Image Rotation
β’ Image Scaling
β’ Read AVI Videos
β’ Read Mp4 Videos
β’ Writing to Video
β’ Mask Detection-Part 1
β’ OpenCV Project Part-1
β’ Mask Detection-Part 2
β’ Use of Keras for Deep Learning Models
β’ Object Detection using OpenCV.
β’ Object Detection using Yolo v3.
β’ Object detection of Multiple Objects
β’ Introduction to TensorFlow Lite
β’ Use of Google Colab
β’ Importing and exporting of Data in Google Colab
β’ Running ML Models in CPU and GPU using Google Colab
β’ Running Deep Learning Models in Colab
a. Overview of Streamlit
b. Application of Streamlit and its Uses
c. Streamlit Simple App Creation in Python
d. Machine Learning in Streamlit
a. ModΡl SΡrialization (Json, XML)
b. UpdatablΡ ClassifiΡrs
c. Batch mode.
d. Joblib, Pickle
a. Profile Creation
b. Repository Creation
c. Maintenance
d. Tags in GitHub
e. Uploading Files in GitHub
f. Commit Changes
a. What is SDLC?
b. Different methods in SDLC.
c. What is difference between Waterfall and Agile?
d. What are the advantages and disadvantages of Agile?
e. What are the advantages and disadvantages of Waterfall?
f. Jira and its Uses
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As the industry matures, jobs in AI will not only grow in number but also complexity and diversity. This will open doors for various professionalsβjunior, senior, researchers, statisticians, practitioners, experimental scientists, etc.
The biggest advantage of working as a AI professional is that you can work in any industry, which can be sales, marketing, pharma, healthcare, consulting, finance, CPG, retails or any business which makes implements AI driven operations.
After learning artificial intelligence, you can become:
1. Machine Learning Engineer
2. Data Scientist
3. AI Research Scientist
4. AI Solutions Architect
5. Business Intelligence Developer
6. Robotics Engineer
7. Natural Language Processing (NLP) Engineer
8. Computer Vision Engineer
9. AI Product Manager
10. AI Consultant
The relationship is exclusively between one mentor and one mentee. An experienced senior mentor takes a junior mentee under their wing, sharing their wisdom, and expertise that help mentee solve doubts, problems faced during the program. This helps mentee progress faster
Learn anytime, anywhere & track your progress.
We have over 10 years of experience with top AI professionals as mentors. Here is what our students have to say about our Artificial Intelligence Mentorship Program.
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There is not much difference between a trainer and a teacher. In the corporate world, a teacher is known as a trainer.
The trainerβs job is not to motivate or make an employee or a student potentially active on a targeted task, the trainer is just to explore their knowledge about a particular subject which they are specialized in. A trainer will not give you the knowledge of what can happen on a practical project/live project because a trainer isn't able to find the best way in which you can learn the particular subject or skill or concept.
Like a teacher, a trainer will provide you an environment of a classroom like you got in school and college, where the trainer will stand by in front of you behind a table. So, as we know in the classroom some of us get to understand what the trainer says and some of us not, because of miscommunication between the trainer and you the points get may be skipped.
Mentors provide you personal advice, counsel, and support with their experience of working on a project with practical examples. A mentor has far more information than a trainer because of their working status. A mentor can be more effective than a trainer. A mentor is not a full-time trainer like a trainer does. A mentor does his mentorship in their free time while they do not have other stuff to do.
Mentors stick to a commitment until the job is done. Mentors wait for the mentees to understand and complete the process, they do not rush the process until the mentee is able to complete the process on his/her own. Usually, the mentors and mentee develop their friendship when official relationships are not mandatory. Generally, a mentor is not much aware of the companyβs goal and they are more concerned about the menteeβs personal goal.
TopMentor is known for its mentors who are currently working as a professional in the industry. So, I bet you will never be disappointed during the class. There will be no option that you are not understanding or even incase you donβt understand, you can ask the mentor at any time and as soon as possible the mentor will reply to you and will solve your doubt you will have.
We are a Nasscom & Futureskillsprime approved company. We provide 13+ international certificates to students with their unique certificate ID. Every month out students find jobs in India and abroad with the help of these valuable international certifications.Β
We at TopMentor strongly recommend online learning for data science. In offline mode, students often miss classes due to emergencies and can't revisit them as exact same recordings are not availableβbut online courses provide class recordings, so you never fall behind. You can also learn from anywhere without settling for a nearby average institute. Plus, online sessions make it easier for shy students to ask doubts without hesitation. With industry mentors, flexible learning, and dedicated support, TopMentor offers one of the best online data science programs at the most affordable price.
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Thereβs absolutely no eligibility for our data science mentorship program. There is NO need of having any previous coding, maths or even tech background. Anyone who wants to change and transform life and career should and can join the data science masters program with Top Mentor.
Yes absolutely, you will get 100% job assistance after the end of the course. We have tie ups with placement agencies, HRβs and companies with requirements in artificial intelligence. It helps you get more interviews and easier placements. You will get placement calls from city of your choice.
As this page specifically talks about self paced version of course, there is no such duration. You can complete the content at your pace.
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We are featured in prestigious media publications.
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Top Mentor sets highest placement standards by placing 550+ students in the last quarter.
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TopMentor, an Edtech Startup becomes the goto place for professionals seeking jobs abroad.
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Owing to the phenomenal placement records, TopMentor stands as a promising company in the edtech world.
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