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Course Overview

Data Science Training

with Machine Learning


Data science and Analysis are key things that were used for ages to make error-free decisions but now they are playing some very important role in developing machine learning applications.


In this course, you will get an in-depth understanding of analyzing the data and building Machine learning (ML) models using ML libraries in Python. Use the analysis was done to communicate insights and make predictions using the model.

The interesting methodology taught in this course once understood can be used in solving real world problems with the required modifications. So in order to get to this level of understanding, we will go into the mathematical foundations of machine learning during the data science training.

There are basically three types of math that we will need:

  • Probability
  • Statistics
  • Linear Algebra

We will develop all three of these areas quite carefully because they’re really crucial to mastering machine learning. So at the end of the day, what are the skills that you will acquire from this course?

Practitioner grade Python skills for solving Data Science problems – You will be cleaning and processing a lot of data before you start applying machine learning models to solve problems.

Familiarity with the most widely-used machine learning methods – What are they? How do they work? Why do they work? What kinds of data are they good for? What are their strengths and weaknesses? What is going on under the hood?

Strong grounding in the foundations of machine learning that will help you to keep up with this field as you scale to learn newer things. The field is evolving very rapidly.

By the end of this data science training program, you would have built a collection of projects across different areas in machine learning and this will essentially constitute a portfolio that you can showcase.

Skills Covered

    • Python coding concepts
    • Data Cleaning, Analysis and Filtering
    • Data Visualisations
    • Predictive model using existing sets of information
    • Various form of Machine Learning
    • Web scraping and data preparation

Upcoming Trainings

Key Features

    • Instructor Lead Learning
    • 70 Hours of Blended Skill Development
    • Hands-on Training
    • Live Project Work
    • Essential Interview Training
    • Career guidance to get job opportunities as a Data Scientist or Machine Learning Developer
    • Resume Assistance
    • Job Placement Services
    • On-Job support and assistance



  • Annual Salary
  • Hiring Companies

Service Options

Training Services

  • Highly Experienced Trainers
  • Hands-on Learning
  • 24×7 student assistance and support
  • Comprehensive Study Material
  • Supervised Hands-on Learning
  • Interview Training

Job Placement Services

  • Resume Development
  • Marketing Services
  • Job onboarding assistance and support
  • 24×7 On-Job assistance and support
  • Free Access to Skill Development Webinars

Corporate Training

  • Performance insights to let you analyze, troubleshoot, and improve performance within your organization
  • Compliance training assists your employees in achieving learning goals
  • Flexible Training Schedules
  • 24×7 learner assistance and support
  • Expert assistance of Machine Learning Implimentation

Course Curriculum


It’s usually preferable to have a computer sciences background to get into Data Sciences, but however, our comprehensive training at Resolve6 is the perfect Bootcamp to get those basics aligned, and get started to upscale your career path as a Data Scientist/Machine Learning Developer.

Course Content

What exactly is data science, and what precisely does a data scientist do?
Various industry examples of data science and how Python is used for data science applications
Numerous aspects in the Data Science process, such as data exploration, data wrangling, and model selection
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & it’s types
What is Python?
Why Python?
Installing Python
Python IDEs
Introduction to a fundamental Python construct
Understanding indentation such as tabs and spaces
Pound # character, names, variables and other code comments.
Python data types include containers,  numeric, text sequences  constants, and more
Python’s basic operators include logical, bitwise, assignment, comparison, and others, as well as slicing and the slice operator.
Break, if, for, continue, else, range(), and other loop and control statements.
Understanding OOP concepts such as encapsulation, inheritance, polymorphism, and abstraction
What is the difference between access modifiers, instances, class members, classes, and objects?
Function parameters and return types
Lambda expressions are used to connect to a database and get data.
Introduction to Python’s mathematical computing
What are arrays and matrices, array indexing, array math, and the ND-array object
Standard deviation, data types
NumPy conditional probability, correlation, and covariance SciPy for Scientific Computing
SciPy Fundamentals
NumPy on top of NumPy
What are the features of SciPy?
SciPy subpackages include Signal, Integrate, Fftpack, Cluster, Optimize, Stats, and more.
Using SciPy, prove the Bayes Theorem.
Introduction to Machine Learning with Python Tools for Machine Learning in Python include NumPy, ScikitLearn, Pandas, Matplotlib, and more.
Machine Learning Use Cases
Machine Learning Process Flow and Machine Learning Categories
Understanding Logistic Regression and Linear Regression
In Machine Learning, what is gradient descent?
Introduction to Python DataFrames, including importing data from JSON, CSV, Excel, SQL databases, and NumPy arrays into DataFrames. Various data operations such as selecting, filtering, sorting, displaying, joining, and combining, handling missing values, and time series analysis are covered in this data science training.
What is exploratory data analysis and building of hypothesis, plotting, and other techniques
What is a data object and what are its primary functions?
It involves the use Pandas library to manipulate data
Pandas library NumPy requirement, Pandas data loading and handling
Concatenation and several sorts of joins on data objects, as well as how to merge data objects
Exploration and analysis of datasets Matplotlib Data Visualization in data science training
Matplotlib is used for plotting graphs and charts such as Histogram, Scatter, Pie, Bar, Line, and others
It involves the use of Matplotlib API, Subplots, and Pandas built-in data visualization.
Need of Machine Learning
Introduction to Machine Learning
Types of Machine Learning, such as supervised, unsupervised and reinforcement learning
Why Machine Learning with Python and applications of Machine Learning.
What exactly supervised learning and classification are all about?
Decision Tree, an algorithm for inducing Decision Trees
Forest at Random
Matrix of Perplexity
Naive Bayes, how it works, and how to implement the Naive Bayes classifier
Support Vector Machine, Support Vector Mechanism, and Support Vector Mechanism Working Process
What Hyper Parameter Optimization (HPO) is all about?
Using Random Search vs. Grid Search
How to go about putting a Support Vector Machine to work for classification in data science training?
Introduction to supervised learning
Types of supervised learning – regression and classification
Introduction to regression
Simple linear regression
Multiple linear regression
Decision Tree, an algorithm for Decision Tree induction
Confusion Matrix
Random Forest
Naïve Bayes, working of Naïve Bayes, how to implement Naïve Bayes classifier
Support Vector Machine, working process of Support Vector Mechanism
What is Hyper Parameter Optimization
Comparing Random Search with Grid Search
How to implement a Support Vector Machine for classification?
Assumptions in linear regression, and math behind linear regression.
Hands-on Exercise – Linear Regression and Train-Test Implementation
Introduction to classification
Linear regression vs logistic regression
Math behind logistic regression with detailed formulas log it function and odds
Confusion matrix and accuracy
True positive rate v/s false positive rate
Threshold evaluation with ROCR.
Hands-on Exercise – Logistic regression, Confusion matrix Implementation
Introduction to tree-based classification
Understanding a decision tree
Impurity function and entropy to understand the concept of information gain for the right split of node
Gini index
Pruning, pre-pruning, post-pruning, cost-complexity pruning
Introduction to ensemble techniques
Understanding bagging
Introduction to random forests
Finding the right number of trees in a random forest.
Hands-on Exercise – Decision tree Implementation and hyper parameters in the random forest.
Introduction to probabilistic classifiers
Understanding Naïve Bayes
Math behind the Bayes theorem
Understanding a support vector machine (SVM)
Kernel functions in SVM, and math behind SVM.
Hands-on Exercise – Naïve Bayes and SVM implementation.
How a save a model using Pickle
Transfer a saved model
Deploy a saved model
Flask to deploy Machine Learning Model on Server
Unsupervised learning introduction, unsupervised learning usage scenarios
What is K-means clustering, and how does the K-means clustering method work?
Clustering at its best
What is the difference between hierarchical clustering and K-means clustering, and how does hierarchical clustering work in data science training?
Grid search,many parameters, model training, and pipeline construction
Types of unsupervised learning
Clustering and dimensionality reduction
Types of clustering
Introduction to k-means clustering
Math behind k-means
Dimensionality reduction with PCA.
Hands-on Exercise – K-Means and PCA implementation
Importance of Dimensions
Why Dimensionality Reduction
PCA and its implementation
LDA and its implementation
Factor Analysis
Scaling dimensional model
Hands On: – PCA
Hands On: – Scaling
Define Association Rules
Backend of recommendation engines and develop your own using python
What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How Recommendation Engines work?
Collaborative Filtering
Content Based Filtering
Hands On: – Apriori Algorithm
Hands On: – Market Basket Analysis
Introduction to Text Mining
Introduction to Sentiment
Setting up API Bridge, between Python and Twitter Account
Extracting Tweet from Twitter Account
Scoring the tweet
Introduction to Python web scraping and various web scraping libraries
Scrapy Python and Beautiful Soup packages
Beautiful Soup installation
lxml Python parser installation
Creating a soup object from HTML input
Tree searching, output printing,  full or partial parsing, and tree searching
Introduction to Natural Language Processing (NLP)
Introduction to text mining
Importance and applications of text mining
How NLP works with text mining
Writing and reading to word files
OS modules
Natural Language Toolkit (NLTK) environment and text mining: its cleaning, pre-processing and text classification.

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    Training FAQs

    Some of the fundamental concepts expected from Data scientists are correlation, causation, and how to statistically test hypotheses. Basic knowledge of linear algebra and calculus is definitely required. It may be hard to master them initially but given the time and practice with working, these areas will be familiar and comfortable to work on while undergoing data science training.

    One can learn all the latest techniques, master multiple tools, and make the best graphs, but if you cannot explain your analysis to your client, you will fail as a data scientist. It is the most important part of data science training.

    GitHub profile is a must, it instills confidence, trust, and flexibility to check out any project that you have mentioned in a resume

    We go with python because it is easier to write, read and understand codes in python. Support of bulk of important libraries for doing ML tasks.

    Machine learning is a sub-set of Data Science or one can say that it is a part of data science training. Data Science uses a scientific approach to extract meaning and insights from the data Whereas ML is a tool/technique used by Data scientists.