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.

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 the course, 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

  • 11 Jul (Mon-Fri) - Data Sciences and Machine Learning

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 is Data Science and what does a Data scientist do.?
Various examples of Data Science in the industries and how Python is deployed for Data Science applications
Various steps in Data Science process like data wrangling, data exploration and selecting the model
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 basic construct in Python
Understanding indentation like tabs and spaces
Code comments like Pound # character, names and variables
Python built-in data types like containers (list, set, tuple and dict), numeric (float, complex, int), text sequence (string), constants (true, false, ellipsis) and others (classes, instances, modules, exceptions and more)
Basic operators in Python like logical, bitwise, assignment, comparison and more, slicing and the slice operator
Loop and control statements like break, if, for, continue, else, range() and more.
Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
What are access modifiers, instances, class members, classes and objects
Function parameter and return type functions
Lambda expressions, connecting with database to pull the data.
Introduction to mathematical computing in Python
What are arrays and matrices, array indexing, array math, ND-array object
Data types, standard deviation
Conditional probability in NumPy, correlation, covariance SciPy for Scientific Computing
Introduction to SciPy
Building on top of NumPy
What are the characteristics of SciPy
Various sub packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more  Bayes Theorem with SciPy.
Introduction to Machine Learning with Python
Various tools in Python used for Machine Learning like NumPy, ScikitLearn, Pandas, Matplotlib and more
Use cases of Machine Learning
Process flow of Machine Learning and Various categories of Machine Learning
Understanding Linear Regression and Logistic Regression
What is gradient descent in Machine Learning
Introduction to Python DataFrames, importing data from JSON, CSV, Excel, SQL database, NumPy array to DataFrame Various data operations like selecting, filtering, sorting, viewing, joining and combining, how to handle
Missing values, time series analysis.
What is exploratory data analysis and building of hypothesis, plotting, and other techniques
What is a data object and its basic functionalities?
Using Pandas library for data manipulation
NumPy dependency of Pandas library, loading and handling data with Pandas
How to merge data objects, concatenation and various types of joins on data objects
Exploring and analyzing datasets. Data Visualization with Matplotlib
Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
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.
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
Introduction to unsupervised learning, use cases of unsupervised learning
What is K-means clustering, understanding the K-means clustering algorithm
Optimal clustering
Hierarchical clustering and K-means clustering and how does hierarchical clustering work
Searching a grid, model training, multiple parameters and building of a pipeline
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 web scraping in Python, various web scraping libraries
BeautifulSoup, ScrapyPython packages
Installing of BeautifulSoup
Installing Python parser lxml
Creating soup object with input HTML
Searching of tree, full or partial parsing, output print and searching the tree
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.

    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.

    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. Data Science uses a scientific approach to extract meaning and insights from the data Whereas ML is a tool/technique used by Data scientists.