Course / Course Details

Deep dive with infotact on Data Science & ML

  • Smrutirupa Parida image

    By - Smrutirupa Parida

  • 23 students
  • 725 Hours 34 Min
  • (0)

Course Requirements

  • Basic understanding of programming (preferably Python)
  • Familiarity with mathematics fundamentals (algebra, basic statistics)
  • A laptop/desktop with internet connectivity
  • Willingness to learn and work on real-world datasets
  • Basic knowledge of tools like Excel or any data handling platform (optional but helpful)
  • No prior experience in Machine Learning required (beginner-friendly to intermediate level) 

Course Description

The Deep Dive with Infotact on Data Science & Machine Learning is a comprehensive, industry-oriented program designed to equip learners with the skills required to analyze data, build intelligent systems, and solve real-world problems using modern data science techniques.

This course covers the complete lifecycle of a data science project—from data collection and preprocessing to model building, evaluation, and deployment. Learners will gain hands-on experience with tools such as Python, NumPy, Pandas, Matplotlib, Scikit-learn, and introductory concepts of deep learning.

The program emphasizes practical learning, enabling participants to work on real-world projects, case studies, and datasets, ensuring they are job-ready and capable of applying their knowledge in professional environments.

Course Outcomes

By the end of this course, learners will be able to:

  • Understand core concepts of Data Science and Machine Learning
  • Perform data cleaning, preprocessing, and exploratory data analysis (EDA)
  • Visualize data effectively using modern tools and libraries
  • Build and evaluate machine learning models (regression, classification, clustering)
  • Apply statistical techniques to derive meaningful insights
  • Work with real-world datasets and solve business problems
  • Understand model performance metrics and optimization techniques
  • Deploy basic machine learning models for real-world applications
  • Gain confidence to work on data-driven projects and internships
  • Prepare for entry-level roles in Data Science and Machine Learning

Course Curriculum

  • 31 chapters
  • 102 lectures
  • 0 quizzes
  • 725 Hours 34 Min total length
Toggle all chapters
1 Course Outline
5.59 Min

2 Join Our Online Classroom
4.01 Min

3 Your First Day
3.48 Min

1 What Is Machine Learning
6.52 Min

2 AIMachine LearningData Science
4.51 Min

3 ZTM Resources
4.23 Min

4 Exercise Machine Learning Playground
6.16 Min

5 How Did We Get Here
6.03 Min

6 Exercise YouTube Recommendation Engine
4.24 Min

7 Types of Machine Learning
4.41 Min

8 What Is Machine Learning Round 2
4.45 Min

9 Section Review
1.48 Min

1 Section Overview
3.08 Min

2 Introducing Our Framework
2.38 Min

3 6 Step Machine Learning Framework
4.59 Min

4 Types of Machine Learning Problems
10.32 Min

5 Types of Data
4.51 Min

6 Types of Evaluation
3.31 Min

7 Features In Data
5.22 Min

8 Modelling - Splitting Data
5.58 Min

9 Modelling - Picking the Model
4.35 Min

10 Modelling - Tuning
3.17 Min

11 Modelling - Comparison
9.32 Min

12 Experimentation
3.35 Min

13 Tools We Will Use
4 Min

1 The 2 Paths
3.27 Min

1 Assets
10 Min

1 Section Overview
1.09 Min

2 Introducing Our Tools
3.28 Min

3 What is Conda
2.35 Min

4 Conda Environments
4.3 Min

5 Mac Environment Setup
17.26 Min

1 Mac Environment Setup 2
14.11 Min

2 Windows Environment Setup
5.17 Min

1 Windows Environment Setup 2
23.17 Min

1 Jupyter Notebook Walkthrough
10.2 Min

2 Jupyter Notebook Walkthrough 2
16.18 Min

3 Jupyter Notebook Walkthrough 3
8.1 Min

1 Assets
10 Min

1 Section Overview
2.27 Min

2 Pandas Introduction
4.29 Min

3 Series, Data Frames and CSVs
13.21 Min

4 Describing Data with Pandas
9.48 Min

5 Selecting and Viewing Data with Pandas
11.08 Min

1 Selecting and Viewing Data with Pandas Part 2
13.07 Min

2 Manipulating Data
13.56 Min

1 Manipulating Data 2
9.57 Min

2 Manipulating Data 3
10.12 Min

3 How To Download The Course Assignments
7.43 Min

1 Section Overview
2.4 Min

2 NumPy Introduction
5.17 Min

3 NumPy DataTypes and Attributes
14.05 Min

4 Creating NumPy Arrays
9.22 Min

5 NumPy Random Seed
7.17 Min

6 Viewing Arrays and Matrices
9.35 Min

1 Manipulating Arrays
11.32 Min

2 Manipulating Arrays 2
9.44 Min

3 Standard Deviation and Variance
7.1 Min

4 Reshape and Transpose
7.26 Min

1 Dot Product vs Element Wise
11.45 Min

2 Exercise Nut Butter Store Sales
13.04 Min

3 Comparison Operators
3.33 Min

4 Sorting Arrays
6.19 Min

1 Turn Images Into NumPy Arrays
7.37 Min

2 Exercise Imposter Syndrome
2.56 Min

1 Histograms And Subplots
8.4 Min

2 Subplots Option 2
4.15 Min

3 Quick Tip Data Visualizations
1.48 Min

4 Plotting From Pandas DataFrames
5.58 Min

5 Plotting From Pandas DataFrames 2
10.33 Min

1 Plotting from Pandas DataFrames 3
8.32 Min

2 Plotting from Pandas DataFrames 4
6.36 Min

3 Plotting from Pandas DataFrames 5
8.29 Min

4 Plotting from Pandas DataFrames 6
8.28 Min

1 Plotting from Pandas DataFrames 7
11.2 Min

2 Customizing Your Plots
10.09 Min

1 Customizing Your Plots 2
9.41 Min

2 Saving And Sharing Your Plots
4.14 Min

1 scikit-learn-data
10 Min

1 Section Overview
2.29 Min

2 Scikit-learn Introduction
6.41 Min

3 Refresher What Is Machine Learning
5.4 Min

4 Scikit-learn Cheatsheet
6.13 Min

1 Typical scikit-learn Workflow
23.14 Min

1 Optional Debugging Warnings In Jupyter
18.57 Min

2 Getting Your Data Ready Splitting Your Data
8.37 Min

3 Quick Tip Clean, Transform, Reduce
5.03 Min

1 Getting Your Data Ready Convert Data To Numbers
16.54 Min

2 Getting Your Data Ready Handling Missing Values With Pandas
12.22 Min

1 Getting Your Data Ready Handling Missing Values With Scikit-learn
17.29 Min

1 NEW Choosing The Right Model For Your Data
20.14 Min

1 NEW Choosing The Right Model For Your Data 2 (Regression)
11.21 Min

2 Quick Tip How ML Algorithms Work
1.25 Min

3 Choosing The Right Model For Your Data 3 (Classification)
12.45 Min

1 Fitting A Model To The Data
6.45 Min

2 Making Predictions With Our Model
8.24 Min

3 predict() vs predict_proba()
8.33 Min

4 NEW Making Predictions With Our Model (Regression)
8.48 Min

1 NEW Evaluating A Machine Learning Model (Score) Part 1
9.41 Min

2 NEW Evaluating A Machine Learning Model (Score) Part 2
6.47 Min

3 Evaluating A Machine Learning Model 2 (Cross Validation)
13.16 Min

4 Evaluating A Classification Model 1 (Accuracy)
4.46 Min

Instructor

Smrutirupa Parida

5 Rating
1 Reviews
117 Students
3 Courses

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  • Duration 725 Hours 34 Min

  • Lectures 102 lessons

  • Enrolled 23 students

  • Category: DATA

  • 0 Quizzes

  • Language: English

  • Skill Level Beginner

  • Certificate of Completion

  • Full lifetime access

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